sample of research findings and analysis

How To Write The Results/Findings Chapter

For qualitative studies (dissertations & theses).

By: Jenna Crossley (PhD). Expert Reviewed By: Dr. Eunice Rautenbach | August 2021

So, you’ve collected and analysed your qualitative data, and it’s time to write up your results chapter. But where do you start? In this post, we’ll guide you through the qualitative results chapter (also called the findings chapter), step by step. 

Overview: Qualitative Results Chapter

• What (exactly) the qualitative results chapter is
• What to include in your results chapter
• How to write up your results chapter
• A few tips and tricks to help you along the way
• Free results chapter template

What exactly is the results chapter?

The results chapter in a dissertation or thesis (or any formal academic research piece) is where you objectively and neutrally present the findings of your qualitative analysis (or analyses if you used multiple qualitative analysis methods ). This chapter can sometimes be combined with the discussion chapter (where you interpret the data and discuss its meaning), depending on your university’s preference.  We’ll treat the two chapters as separate, as that’s the most common approach.

In contrast to a quantitative results chapter that presents numbers and statistics, a qualitative results chapter presents data primarily in the form of words . But this doesn’t mean that a qualitative study can’t have quantitative elements – you could, for example, present the number of times a theme or topic pops up in your data, depending on the analysis method(s) you adopt.

Adding a quantitative element to your study can add some rigour, which strengthens your results by providing more evidence for your claims. This is particularly common when using qualitative content analysis. Keep in mind though that qualitative research aims to achieve depth, richness and identify nuances , so don’t get tunnel vision by focusing on the numbers. They’re just cream on top in a qualitative analysis.

So, to recap, the results chapter is where you objectively present the findings of your analysis, without interpreting them (you’ll save that for the discussion chapter). With that out the way, let’s take a look at what you should include in your results chapter.

What should you include in the results chapter?

As we’ve mentioned, your qualitative results chapter should purely present and describe your results , not interpret them in relation to the existing literature or your research questions . Any speculations or discussion about the implications of your findings should be reserved for your discussion chapter.

In your results chapter, you’ll want to talk about your analysis findings and whether or not they support your hypotheses (if you have any). Naturally, the exact contents of your results chapter will depend on which qualitative analysis method (or methods) you use. For example, if you were to use thematic analysis, you’d detail the themes identified in your analysis, using extracts from the transcripts or text to support your claims.

While you do need to present your analysis findings in some detail, you should avoid dumping large amounts of raw data in this chapter. Instead, focus on presenting the key findings and using a handful of select quotes or text extracts to support each finding . The reams of data and analysis can be relegated to your appendices.

While it’s tempting to include every last detail you found in your qualitative analysis, it is important to make sure that you report only that which is relevant to your research aims, objectives and research questions .  Always keep these three components, as well as your hypotheses (if you have any) front of mind when writing the chapter and use them as a filter to decide what’s relevant and what’s not.

Need a helping hand?

How do I write the results chapter?

Now that we’ve covered the basics, it’s time to look at how to structure your chapter. Broadly speaking, the results chapter needs to contain three core components – the introduction, the body and the concluding summary. Let’s take a look at each of these.

Section 1: Introduction

The first step is to craft a brief introduction to the chapter. This intro is vital as it provides some context for your findings. In your introduction, you should begin by reiterating your problem statement and research questions and highlight the purpose of your research . Make sure that you spell this out for the reader so that the rest of your chapter is well contextualised.

The next step is to briefly outline the structure of your results chapter. In other words, explain what’s included in the chapter and what the reader can expect. In the results chapter, you want to tell a story that is coherent, flows logically, and is easy to follow , so make sure that you plan your structure out well and convey that structure (at a high level), so that your reader is well oriented.

The introduction section shouldn’t be lengthy. Two or three short paragraphs should be more than adequate. It is merely an introduction and overview, not a summary of the chapter.

Pro Tip – To help you structure your chapter, it can be useful to set up an initial draft with (sub)section headings so that you’re able to easily (re)arrange parts of your chapter. This will also help your reader to follow your results and give your chapter some coherence.  Be sure to use level-based heading styles (e.g. Heading 1, 2, 3 styles) to help the reader differentiate between levels visually. You can find these options in Word (example below).

Section 2: Body

Before we get started on what to include in the body of your chapter, it’s vital to remember that a results section should be completely objective and descriptive, not interpretive . So, be careful not to use words such as, “suggests” or “implies”, as these usually accompany some form of interpretation – that’s reserved for your discussion chapter.

The structure of your body section is very important , so make sure that you plan it out well. When planning out your qualitative results chapter, create sections and subsections so that you can maintain the flow of the story you’re trying to tell. Be sure to systematically and consistently describe each portion of results. Try to adopt a standardised structure for each portion so that you achieve a high level of consistency throughout the chapter.

For qualitative studies, results chapters tend to be structured according to themes , which makes it easier for readers to follow. However, keep in mind that not all results chapters have to be structured in this manner. For example, if you’re conducting a longitudinal study, you may want to structure your chapter chronologically. Similarly, you might structure this chapter based on your theoretical framework . The exact structure of your chapter will depend on the nature of your study , especially your research questions.

As you work through the body of your chapter, make sure that you use quotes to substantiate every one of your claims . You can present these quotes in italics to differentiate them from your own words. A general rule of thumb is to use at least two pieces of evidence per claim, and these should be linked directly to your data. Also, remember that you need to include all relevant results , not just the ones that support your assumptions or initial leanings.

In addition to including quotes, you can also link your claims to the data by using appendices , which you should reference throughout your text. When you reference, make sure that you include both the name/number of the appendix , as well as the line(s) from which you drew your data.

As referencing styles can vary greatly, be sure to look up the appendix referencing conventions of your university’s prescribed style (e.g. APA , Harvard, etc) and keep this consistent throughout your chapter.

Section 3: Concluding summary

The concluding summary is very important because it summarises your key findings and lays the foundation for the discussion chapter . Keep in mind that some readers may skip directly to this section (from the introduction section), so make sure that it can be read and understood well in isolation.

In this section, you need to remind the reader of the key findings. That is, the results that directly relate to your research questions and that you will build upon in your discussion chapter. Remember, your reader has digested a lot of information in this chapter, so you need to use this section to remind them of the most important takeaways.

Importantly, the concluding summary should not present any new information and should only describe what you’ve already presented in your chapter. Keep it concise – you’re not summarising the whole chapter, just the essentials.

Tips for writing an A-grade results chapter

Now that you’ve got a clear picture of what the qualitative results chapter is all about, here are some quick tips and reminders to help you craft a high-quality chapter:

• Your results chapter should be written in the past tense . You’ve done the work already, so you want to tell the reader what you found , not what you are currently finding .
• Make sure that you review your work multiple times and check that every claim is adequately backed up by evidence . Aim for at least two examples per claim, and make use of an appendix to reference these.
• When writing up your results, make sure that you stick to only what is relevant . Don’t waste time on data that are not relevant to your research objectives and research questions.
• Use headings and subheadings to create an intuitive, easy to follow piece of writing. Make use of Microsoft Word’s “heading styles” and be sure to use them consistently.
• When referring to numerical data, tables and figures can provide a useful visual aid. When using these, make sure that they can be read and understood independent of your body text (i.e. that they can stand-alone). To this end, use clear, concise labels for each of your tables or figures and make use of colours to code indicate differences or hierarchy.
• Similarly, when you’re writing up your chapter, it can be useful to highlight topics and themes in different colours . This can help you to differentiate between your data if you get a bit overwhelmed and will also help you to ensure that your results flow logically and coherently.

If you have any questions, leave a comment below and we’ll do our best to help. If you’d like 1-on-1 help with your results chapter (or any chapter of your dissertation or thesis), check out our private dissertation coaching service here or book a free initial consultation to discuss how we can help you.

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20 Comments

This was extremely helpful. Thanks a lot guys

Hi, thanks for the great research support platform created by the gradcoach team!

I wanted to ask- While “suggests” or “implies” are interpretive terms, what terms could we use for the results chapter? Could you share some examples of descriptive terms?

I think that instead of saying, ‘The data suggested, or The data implied,’ you can say, ‘The Data showed or revealed, or illustrated or outlined’…If interview data, you may say Jane Doe illuminated or elaborated, or Jane Doe described… or Jane Doe expressed or stated.

I found this article very useful. Thank you very much for the outstanding work you are doing.

What if i have 3 different interviewees answering the same interview questions? Should i then present the results in form of the table with the division on the 3 perspectives or rather give a results in form of the text and highlight who said what?

I think this tabular representation of results is a great idea. I am doing it too along with the text. Thanks

That was helpful was struggling to separate the discussion from the findings

this was very useful, Thank you.

Very helpful, I am confident to write my results chapter now.

It is so helpful! It is a good job. Thank you very much!

Very useful, well explained. Many thanks.

Hello, I appreciate the way you provided a supportive comments about qualitative results presenting tips

I loved this! It explains everything needed, and it has helped me better organize my thoughts. What words should I not use while writing my results section, other than subjective ones.

Thanks a lot, it is really helpful

Thank you so much dear, i really appropriate your nice explanations about this.

Thank you so much for this! I was wondering if anyone could help with how to prproperly integrate quotations (Excerpts) from interviews in the finding chapter in a qualitative research. Please GradCoach, address this issue and provide examples.

what if I’m not doing any interviews myself and all the information is coming from case studies that have already done the research.

Very helpful thank you.

This was very helpful as I was wondering how to structure this part of my dissertation, to include the quotes… Thanks for this explanation

This is very helpful, thanks! I am required to write up my results chapters with the discussion in each of them – any tips and tricks for this strategy?

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• How to Write Discussions and Conclusions

The discussion section contains the results and outcomes of a study. An effective discussion informs readers what can be learned from your experiment and provides context for the results.

What makes an effective discussion?

When you’re ready to write your discussion, you’ve already introduced the purpose of your study and provided an in-depth description of the methodology. The discussion informs readers about the larger implications of your study based on the results. Highlighting these implications while not overstating the findings can be challenging, especially when you’re submitting to a journal that selects articles based on novelty or potential impact. Regardless of what journal you are submitting to, the discussion section always serves the same purpose: concluding what your study results actually mean.

A successful discussion section puts your findings in context. It should include:

• the results of your research,
• a discussion of related research, and
• a comparison between your results and initial hypothesis.

Tip: Not all journals share the same naming conventions.

You can apply the advice in this article to the conclusion, results or discussion sections of your manuscript.

Our Early Career Researcher community tells us that the conclusion is often considered the most difficult aspect of a manuscript to write. To help, this guide provides questions to ask yourself, a basic structure to model your discussion off of and examples from published manuscripts. 

Questions to ask yourself:

• Was my hypothesis correct?
• If my hypothesis is partially correct or entirely different, what can be learned from the results? 
• How do the conclusions reshape or add onto the existing knowledge in the field? What does previous research say about the topic? 
• Why are the results important or relevant to your audience? Do they add further evidence to a scientific consensus or disprove prior studies? 
• How can future research build on these observations? What are the key experiments that must be done? 
• What is the “take-home” message you want your reader to leave with?

How to structure a discussion

Trying to fit a complete discussion into a single paragraph can add unnecessary stress to the writing process. If possible, you’ll want to give yourself two or three paragraphs to give the reader a comprehensive understanding of your study as a whole. Here’s one way to structure an effective discussion:

Writing Tips

While the above sections can help you brainstorm and structure your discussion, there are many common mistakes that writers revert to when having difficulties with their paper. Writing a discussion can be a delicate balance between summarizing your results, providing proper context for your research and avoiding introducing new information. Remember that your paper should be both confident and honest about the results! 

• Read the journal’s guidelines on the discussion and conclusion sections. If possible, learn about the guidelines before writing the discussion to ensure you’re writing to meet their expectations. 
• Begin with a clear statement of the principal findings. This will reinforce the main take-away for the reader and set up the rest of the discussion. 
• Explain why the outcomes of your study are important to the reader. Discuss the implications of your findings realistically based on previous literature, highlighting both the strengths and limitations of the research. 
• State whether the results prove or disprove your hypothesis. If your hypothesis was disproved, what might be the reasons? 
• Introduce new or expanded ways to think about the research question. Indicate what next steps can be taken to further pursue any unresolved questions. 
• If dealing with a contemporary or ongoing problem, such as climate change, discuss possible consequences if the problem is avoided. 
• Be concise. Adding unnecessary detail can distract from the main findings. 

Don’t

• Rewrite your abstract. Statements with “we investigated” or “we studied” generally do not belong in the discussion. 
• Include new arguments or evidence not previously discussed. Necessary information and evidence should be introduced in the main body of the paper. 
• Apologize. Even if your research contains significant limitations, don’t undermine your authority by including statements that doubt your methodology or execution. 
• Shy away from speaking on limitations or negative results. Including limitations and negative results will give readers a complete understanding of the presented research. Potential limitations include sources of potential bias, threats to internal or external validity, barriers to implementing an intervention and other issues inherent to the study design. 
• Overstate the importance of your findings. Making grand statements about how a study will fully resolve large questions can lead readers to doubt the success of the research. 

Snippets of Effective Discussions:

Consumer-based actions to reduce plastic pollution in rivers: A multi-criteria decision analysis approach

Identifying reliable indicators of fitness in polar bears

• How to Write a Great Title
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How to Write the Results/Findings Section in Research

What is the research paper Results section and what does it do?

The Results section of a scientific research paper represents the core findings of a study derived from the methods applied to gather and analyze information. It presents these findings in a logical sequence without bias or interpretation from the author, setting up the reader for later interpretation and evaluation in the Discussion section. A major purpose of the Results section is to break down the data into sentences that show its significance to the research question(s).

The Results section appears third in the section sequence in most scientific papers. It follows the presentation of the Methods and Materials and is presented before the Discussion section —although the Results and Discussion are presented together in many journals. This section answers the basic question “What did you find in your research?”

What is included in the Results section?

The Results section should include the findings of your study and ONLY the findings of your study. The findings include:

• Data presented in tables, charts, graphs, and other figures (may be placed into the text or on separate pages at the end of the manuscript)
• A contextual analysis of this data explaining its meaning in sentence form
• All data that corresponds to the central research question(s)
• All secondary findings (secondary outcomes, subgroup analyses, etc.)

If the scope of the study is broad, or if you studied a variety of variables, or if the methodology used yields a wide range of different results, the author should present only those results that are most relevant to the research question stated in the Introduction section .

As a general rule, any information that does not present the direct findings or outcome of the study should be left out of this section. Unless the journal requests that authors combine the Results and Discussion sections, explanations and interpretations should be omitted from the Results.

How are the results organized?

The best way to organize your Results section is “logically.” One logical and clear method of organizing research results is to provide them alongside the research questions—within each research question, present the type of data that addresses that research question.

Let’s look at an example. Your research question is based on a survey among patients who were treated at a hospital and received postoperative care. Let’s say your first research question is:

“What do hospital patients over age 55 think about postoperative care?”

This can actually be represented as a heading within your Results section, though it might be presented as a statement rather than a question:

Attitudes towards postoperative care in patients over the age of 55

Now present the results that address this specific research question first. In this case, perhaps a table illustrating data from a survey. Likert items can be included in this example. Tables can also present standard deviations, probabilities, correlation matrices, etc.

Following this, present a content analysis, in words, of one end of the spectrum of the survey or data table. In our example case, start with the POSITIVE survey responses regarding postoperative care, using descriptive phrases. For example:

“Sixty-five percent of patients over 55 responded positively to the question “ Are you satisfied with your hospital’s postoperative care ?” (Fig. 2)

Include other results such as subcategory analyses. The amount of textual description used will depend on how much interpretation of tables and figures is necessary and how many examples the reader needs in order to understand the significance of your research findings.

Next, present a content analysis of another part of the spectrum of the same research question, perhaps the NEGATIVE or NEUTRAL responses to the survey. For instance:

  “As Figure 1 shows, 15 out of 60 patients in Group A responded negatively to Question 2.”

After you have assessed the data in one figure and explained it sufficiently, move on to your next research question. For example:

  “How does patient satisfaction correspond to in-hospital improvements made to postoperative care?”

This kind of data may be presented through a figure or set of figures (for instance, a paired T-test table).

Explain the data you present, here in a table, with a concise content analysis:

“The p-value for the comparison between the before and after groups of patients was .03% (Fig. 2), indicating that the greater the dissatisfaction among patients, the more frequent the improvements that were made to postoperative care.”

Let’s examine another example of a Results section from a study on plant tolerance to heavy metal stress . In the Introduction section, the aims of the study are presented as “determining the physiological and morphological responses of Allium cepa L. towards increased cadmium toxicity” and “evaluating its potential to accumulate the metal and its associated environmental consequences.” The Results section presents data showing how these aims are achieved in tables alongside a content analysis, beginning with an overview of the findings:

“Cadmium caused inhibition of root and leave elongation, with increasing effects at higher exposure doses (Fig. 1a-c).”

The figure containing this data is cited in parentheses. Note that this author has combined three graphs into one single figure. Separating the data into separate graphs focusing on specific aspects makes it easier for the reader to assess the findings, and consolidating this information into one figure saves space and makes it easy to locate the most relevant results.

Following this overall summary, the relevant data in the tables is broken down into greater detail in text form in the Results section.

• “Results on the bio-accumulation of cadmium were found to be the highest (17.5 mg kgG1) in the bulb, when the concentration of cadmium in the solution was 1×10G2 M and lowest (0.11 mg kgG1) in the leaves when the concentration was 1×10G3 M.”

Captioning and Referencing Tables and Figures

Tables and figures are central components of your Results section and you need to carefully think about the most effective way to use graphs and tables to present your findings . Therefore, it is crucial to know how to write strong figure captions and to refer to them within the text of the Results section.

The most important advice one can give here as well as throughout the paper is to check the requirements and standards of the journal to which you are submitting your work. Every journal has its own design and layout standards, which you can find in the author instructions on the target journal’s website. Perusing a journal’s published articles will also give you an idea of the proper number, size, and complexity of your figures.

Regardless of which format you use, the figures should be placed in the order they are referenced in the Results section and be as clear and easy to understand as possible. If there are multiple variables being considered (within one or more research questions), it can be a good idea to split these up into separate figures. Subsequently, these can be referenced and analyzed under separate headings and paragraphs in the text.

To create a caption, consider the research question being asked and change it into a phrase. For instance, if one question is “Which color did participants choose?”, the caption might be “Color choice by participant group.” Or in our last research paper example, where the question was “What is the concentration of cadmium in different parts of the onion after 14 days?” the caption reads:

 “Fig. 1(a-c): Mean concentration of Cd determined in (a) bulbs, (b) leaves, and (c) roots of onions after a 14-day period.”

Steps for Composing the Results Section

Because each study is unique, there is no one-size-fits-all approach when it comes to designing a strategy for structuring and writing the section of a research paper where findings are presented. The content and layout of this section will be determined by the specific area of research, the design of the study and its particular methodologies, and the guidelines of the target journal and its editors. However, the following steps can be used to compose the results of most scientific research studies and are essential for researchers who are new to preparing a manuscript for publication or who need a reminder of how to construct the Results section.

Step 1 : Consult the guidelines or instructions that the target journal or publisher provides authors and read research papers it has published, especially those with similar topics, methods, or results to your study.

• The guidelines will generally outline specific requirements for the results or findings section, and the published articles will provide sound examples of successful approaches.
• Note length limitations on restrictions on content. For instance, while many journals require the Results and Discussion sections to be separate, others do not—qualitative research papers often include results and interpretations in the same section (“Results and Discussion”).
• Reading the aims and scope in the journal’s “ guide for authors ” section and understanding the interests of its readers will be invaluable in preparing to write the Results section.

Step 2 : Consider your research results in relation to the journal’s requirements and catalogue your results.

• Focus on experimental results and other findings that are especially relevant to your research questions and objectives and include them even if they are unexpected or do not support your ideas and hypotheses.
• Catalogue your findings—use subheadings to streamline and clarify your report. This will help you avoid excessive and peripheral details as you write and also help your reader understand and remember your findings. Create appendices that might interest specialists but prove too long or distracting for other readers.
• Decide how you will structure of your results. You might match the order of the research questions and hypotheses to your results, or you could arrange them according to the order presented in the Methods section. A chronological order or even a hierarchy of importance or meaningful grouping of main themes or categories might prove effective. Consider your audience, evidence, and most importantly, the objectives of your research when choosing a structure for presenting your findings.

Step 3 : Design figures and tables to present and illustrate your data.

• Tables and figures should be numbered according to the order in which they are mentioned in the main text of the paper.
• Information in figures should be relatively self-explanatory (with the aid of captions), and their design should include all definitions and other information necessary for readers to understand the findings without reading all of the text.
• Use tables and figures as a focal point to tell a clear and informative story about your research and avoid repeating information. But remember that while figures clarify and enhance the text, they cannot replace it.

Step 4 : Draft your Results section using the findings and figures you have organized.

• The goal is to communicate this complex information as clearly and precisely as possible; precise and compact phrases and sentences are most effective.
• In the opening paragraph of this section, restate your research questions or aims to focus the reader’s attention to what the results are trying to show. It is also a good idea to summarize key findings at the end of this section to create a logical transition to the interpretation and discussion that follows.
• Try to write in the past tense and the active voice to relay the findings since the research has already been done and the agent is usually clear. This will ensure that your explanations are also clear and logical.
• Make sure that any specialized terminology or abbreviation you have used here has been defined and clarified in the  Introduction section .

Step 5 : Review your draft; edit and revise until it reports results exactly as you would like to have them reported to your readers.

• Double-check the accuracy and consistency of all the data, as well as all of the visual elements included.
• Read your draft aloud to catch language errors (grammar, spelling, and mechanics), awkward phrases, and missing transitions.
• Ensure that your results are presented in the best order to focus on objectives and prepare readers for interpretations, valuations, and recommendations in the Discussion section . Look back over the paper’s Introduction and background while anticipating the Discussion and Conclusion sections to ensure that the presentation of your results is consistent and effective.
• Consider seeking additional guidance on your paper. Find additional readers to look over your Results section and see if it can be improved in any way. Peers, professors, or qualified experts can provide valuable insights.

One excellent option is to use a professional English proofreading and editing service  such as Wordvice, including our paper editing service . With hundreds of qualified editors from dozens of scientific fields, Wordvice has helped thousands of authors revise their manuscripts and get accepted into their target journals. Read more about the  proofreading and editing process  before proceeding with getting academic editing services and manuscript editing services for your manuscript.

As the representation of your study’s data output, the Results section presents the core information in your research paper. By writing with clarity and conciseness and by highlighting and explaining the crucial findings of their study, authors increase the impact and effectiveness of their research manuscripts.

For more articles and videos on writing your research manuscript, visit Wordvice’s Resources page.

Wordvice Resources

• How to Write a Research Paper Introduction 
• Which Verb Tenses to Use in a Research Paper
• How to Write an Abstract for a Research Paper
• How to Write a Research Paper Title
• Useful Phrases for Academic Writing
• Common Transition Terms in Academic Papers
• Active and Passive Voice in Research Papers
• 100+ Verbs That Will Make Your Research Writing Amazing
• Tips for Paraphrasing in Research Papers

Library Guides

Dissertations 5: findings, analysis and discussion: home.

• Results/Findings

Alternative Structures

The time has come to show and discuss the findings of your research. How to structure this part of your dissertation? 

Dissertations can have different structures, as you can see in the dissertation  structure  guide.

Dissertations organised by sections

Many dissertations are organised by sections. In this case, we suggest three options. Note that, if within your course you have been instructed to use a specific structure, you should do that. Also note that sometimes there is considerable freedom on the structure, so you can come up with other structures too. 

A) More common for scientific dissertations and quantitative methods:

- Results chapter 

- Discussion chapter

Example: 

• Introduction
• Literature review
• Methodology
• (Recommendations)

if you write a scientific dissertation, or anyway using quantitative methods, you will have some  objective  results that you will present in the Results chapter. You will then interpret the results in the Discussion chapter.  

B) More common for qualitative methods

- Analysis chapter. This can have more descriptive/thematic subheadings.

- Discussion chapter. This can have more descriptive/thematic subheadings.

• Case study of Company X (fashion brand) environmental strategies 
• Successful elements
• Lessons learnt
• Criticisms of Company X environmental strategies 
• Possible alternatives

C) More common for qualitative methods

- Analysis and discussion chapter. This can have more descriptive/thematic titles.

• Case study of Company X (fashion brand) environmental strategies 

If your dissertation uses qualitative methods, it is harder to identify and report objective data. Instead, it may be more productive and meaningful to present the findings in the same sections where you also analyse, and possibly discuss, them. You will probably have different sections dealing with different themes. The different themes can be subheadings of the Analysis and Discussion (together or separate) chapter(s). 

Thematic dissertations

If the structure of your dissertation is thematic ,  you will have several chapters analysing and discussing the issues raised by your research. The chapters will have descriptive/thematic titles. 

• Background on the conflict in Yemen (2004-present day)
• Classification of the conflict in international law  
• International law violations
• Options for enforcement of international law
• Next: Results/Findings >>
• Last Updated: Aug 4, 2023 2:17 PM
• URL: https://libguides.westminster.ac.uk/c.php?g=696975

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Home Market Research

Data Analysis in Research: Types & Methods

Content Index

Why analyze data in research?

Types of data in research, finding patterns in the qualitative data, methods used for data analysis in qualitative research, preparing data for analysis, methods used for data analysis in quantitative research, considerations in research data analysis, what is data analysis in research.

Definition of research in data analysis: According to LeCompte and Schensul, research data analysis is a process used by researchers to reduce data to a story and interpret it to derive insights. The data analysis process helps reduce a large chunk of data into smaller fragments, which makes sense. 

Three essential things occur during the data analysis process — the first is data organization . Summarization and categorization together contribute to becoming the second known method used for data reduction. It helps find patterns and themes in the data for easy identification and linking. The third and last way is data analysis – researchers do it in both top-down and bottom-up fashion.

LEARN ABOUT: Research Process Steps

On the other hand, Marshall and Rossman describe data analysis as a messy, ambiguous, and time-consuming but creative and fascinating process through which a mass of collected data is brought to order, structure and meaning.

We can say that “the data analysis and data interpretation is a process representing the application of deductive and inductive logic to the research and data analysis.”

Researchers rely heavily on data as they have a story to tell or research problems to solve. It starts with a question, and data is nothing but an answer to that question. But, what if there is no question to ask? Well! It is possible to explore data even without a problem – we call it ‘Data Mining’, which often reveals some interesting patterns within the data that are worth exploring.

Irrelevant to the type of data researchers explore, their mission and audiences’ vision guide them to find the patterns to shape the story they want to tell. One of the essential things expected from researchers while analyzing data is to stay open and remain unbiased toward unexpected patterns, expressions, and results. Remember, sometimes, data analysis tells the most unforeseen yet exciting stories that were not expected when initiating data analysis. Therefore, rely on the data you have at hand and enjoy the journey of exploratory research. 

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Every kind of data has a rare quality of describing things after assigning a specific value to it. For analysis, you need to organize these values, processed and presented in a given context, to make it useful. Data can be in different forms; here are the primary data types.

• Qualitative data: When the data presented has words and descriptions, then we call it qualitative data . Although you can observe this data, it is subjective and harder to analyze data in research, especially for comparison. Example: Quality data represents everything describing taste, experience, texture, or an opinion that is considered quality data. This type of data is usually collected through focus groups, personal qualitative interviews , qualitative observation or using open-ended questions in surveys.
• Quantitative data: Any data expressed in numbers of numerical figures are called quantitative data . This type of data can be distinguished into categories, grouped, measured, calculated, or ranked. Example: questions such as age, rank, cost, length, weight, scores, etc. everything comes under this type of data. You can present such data in graphical format, charts, or apply statistical analysis methods to this data. The (Outcomes Measurement Systems) OMS questionnaires in surveys are a significant source of collecting numeric data.
• Categorical data: It is data presented in groups. However, an item included in the categorical data cannot belong to more than one group. Example: A person responding to a survey by telling his living style, marital status, smoking habit, or drinking habit comes under the categorical data. A chi-square test is a standard method used to analyze this data.

Learn More : Examples of Qualitative Data in Education

Data analysis in qualitative research

Data analysis and qualitative data research work a little differently from the numerical data as the quality data is made up of words, descriptions, images, objects, and sometimes symbols. Getting insight from such complicated information is a complicated process. Hence it is typically used for exploratory research and data analysis .

Although there are several ways to find patterns in the textual information, a word-based method is the most relied and widely used global technique for research and data analysis. Notably, the data analysis process in qualitative research is manual. Here the researchers usually read the available data and find repetitive or commonly used words. 

For example, while studying data collected from African countries to understand the most pressing issues people face, researchers might find  “food”  and  “hunger” are the most commonly used words and will highlight them for further analysis.

LEARN ABOUT: Level of Analysis

The keyword context is another widely used word-based technique. In this method, the researcher tries to understand the concept by analyzing the context in which the participants use a particular keyword.  

For example , researchers conducting research and data analysis for studying the concept of ‘diabetes’ amongst respondents might analyze the context of when and how the respondent has used or referred to the word ‘diabetes.’

The scrutiny-based technique is also one of the highly recommended  text analysis  methods used to identify a quality data pattern. Compare and contrast is the widely used method under this technique to differentiate how a specific text is similar or different from each other. 

For example: To find out the “importance of resident doctor in a company,” the collected data is divided into people who think it is necessary to hire a resident doctor and those who think it is unnecessary. Compare and contrast is the best method that can be used to analyze the polls having single-answer questions types .

Metaphors can be used to reduce the data pile and find patterns in it so that it becomes easier to connect data with theory.

Variable Partitioning is another technique used to split variables so that researchers can find more coherent descriptions and explanations from the enormous data.

LEARN ABOUT: Qualitative Research Questions and Questionnaires

There are several techniques to analyze the data in qualitative research, but here are some commonly used methods,

• Content Analysis:  It is widely accepted and the most frequently employed technique for data analysis in research methodology. It can be used to analyze the documented information from text, images, and sometimes from the physical items. It depends on the research questions to predict when and where to use this method.
• Narrative Analysis: This method is used to analyze content gathered from various sources such as personal interviews, field observation, and  surveys . The majority of times, stories, or opinions shared by people are focused on finding answers to the research questions.
• Discourse Analysis:  Similar to narrative analysis, discourse analysis is used to analyze the interactions with people. Nevertheless, this particular method considers the social context under which or within which the communication between the researcher and respondent takes place. In addition to that, discourse analysis also focuses on the lifestyle and day-to-day environment while deriving any conclusion.
• Grounded Theory:  When you want to explain why a particular phenomenon happened, then using grounded theory for analyzing quality data is the best resort. Grounded theory is applied to study data about the host of similar cases occurring in different settings. When researchers are using this method, they might alter explanations or produce new ones until they arrive at some conclusion.

LEARN ABOUT: 12 Best Tools for Researchers

Data analysis in quantitative research

The first stage in research and data analysis is to make it for the analysis so that the nominal data can be converted into something meaningful. Data preparation consists of the below phases.

Phase I: Data Validation

Data validation is done to understand if the collected data sample is per the pre-set standards, or it is a biased data sample again divided into four different stages

• Fraud: To ensure an actual human being records each response to the survey or the questionnaire
• Screening: To make sure each participant or respondent is selected or chosen in compliance with the research criteria
• Procedure: To ensure ethical standards were maintained while collecting the data sample
• Completeness: To ensure that the respondent has answered all the questions in an online survey. Else, the interviewer had asked all the questions devised in the questionnaire.

Phase II: Data Editing

More often, an extensive research data sample comes loaded with errors. Respondents sometimes fill in some fields incorrectly or sometimes skip them accidentally. Data editing is a process wherein the researchers have to confirm that the provided data is free of such errors. They need to conduct necessary checks and outlier checks to edit the raw edit and make it ready for analysis.

Phase III: Data Coding

Out of all three, this is the most critical phase of data preparation associated with grouping and assigning values to the survey responses . If a survey is completed with a 1000 sample size, the researcher will create an age bracket to distinguish the respondents based on their age. Thus, it becomes easier to analyze small data buckets rather than deal with the massive data pile.

LEARN ABOUT: Steps in Qualitative Research

After the data is prepared for analysis, researchers are open to using different research and data analysis methods to derive meaningful insights. For sure, statistical analysis plans are the most favored to analyze numerical data. In statistical analysis, distinguishing between categorical data and numerical data is essential, as categorical data involves distinct categories or labels, while numerical data consists of measurable quantities. The method is again classified into two groups. First, ‘Descriptive Statistics’ used to describe data. Second, ‘Inferential statistics’ that helps in comparing the data .

Descriptive statistics

This method is used to describe the basic features of versatile types of data in research. It presents the data in such a meaningful way that pattern in the data starts making sense. Nevertheless, the descriptive analysis does not go beyond making conclusions. The conclusions are again based on the hypothesis researchers have formulated so far. Here are a few major types of descriptive analysis methods.

Measures of Frequency

• Count, Percent, Frequency
• It is used to denote home often a particular event occurs.
• Researchers use it when they want to showcase how often a response is given.

Measures of Central Tendency

• Mean, Median, Mode
• The method is widely used to demonstrate distribution by various points.
• Researchers use this method when they want to showcase the most commonly or averagely indicated response.

Measures of Dispersion or Variation

• Range, Variance, Standard deviation
• Here the field equals high/low points.
• Variance standard deviation = difference between the observed score and mean
• It is used to identify the spread of scores by stating intervals.
• Researchers use this method to showcase data spread out. It helps them identify the depth until which the data is spread out that it directly affects the mean.

Measures of Position

• Percentile ranks, Quartile ranks
• It relies on standardized scores helping researchers to identify the relationship between different scores.
• It is often used when researchers want to compare scores with the average count.

For quantitative research use of descriptive analysis often give absolute numbers, but the in-depth analysis is never sufficient to demonstrate the rationale behind those numbers. Nevertheless, it is necessary to think of the best method for research and data analysis suiting your survey questionnaire and what story researchers want to tell. For example, the mean is the best way to demonstrate the students’ average scores in schools. It is better to rely on the descriptive statistics when the researchers intend to keep the research or outcome limited to the provided  sample  without generalizing it. For example, when you want to compare average voting done in two different cities, differential statistics are enough.

Descriptive analysis is also called a ‘univariate analysis’ since it is commonly used to analyze a single variable.

Inferential statistics

Inferential statistics are used to make predictions about a larger population after research and data analysis of the representing population’s collected sample. For example, you can ask some odd 100 audiences at a movie theater if they like the movie they are watching. Researchers then use inferential statistics on the collected  sample  to reason that about 80-90% of people like the movie. 

Here are two significant areas of inferential statistics.

• Estimating parameters: It takes statistics from the sample research data and demonstrates something about the population parameter.
• Hypothesis test: I t’s about sampling research data to answer the survey research questions. For example, researchers might be interested to understand if the new shade of lipstick recently launched is good or not, or if the multivitamin capsules help children to perform better at games.

These are sophisticated analysis methods used to showcase the relationship between different variables instead of describing a single variable. It is often used when researchers want something beyond absolute numbers to understand the relationship between variables.

Here are some of the commonly used methods for data analysis in research.

• Correlation: When researchers are not conducting experimental research or quasi-experimental research wherein the researchers are interested to understand the relationship between two or more variables, they opt for correlational research methods.
• Cross-tabulation: Also called contingency tables,  cross-tabulation  is used to analyze the relationship between multiple variables.  Suppose provided data has age and gender categories presented in rows and columns. A two-dimensional cross-tabulation helps for seamless data analysis and research by showing the number of males and females in each age category.
• Regression analysis: For understanding the strong relationship between two variables, researchers do not look beyond the primary and commonly used regression analysis method, which is also a type of predictive analysis used. In this method, you have an essential factor called the dependent variable. You also have multiple independent variables in regression analysis. You undertake efforts to find out the impact of independent variables on the dependent variable. The values of both independent and dependent variables are assumed as being ascertained in an error-free random manner.
• Frequency tables: The statistical procedure is used for testing the degree to which two or more vary or differ in an experiment. A considerable degree of variation means research findings were significant. In many contexts, ANOVA testing and variance analysis are similar.
• Analysis of variance: The statistical procedure is used for testing the degree to which two or more vary or differ in an experiment. A considerable degree of variation means research findings were significant. In many contexts, ANOVA testing and variance analysis are similar.
• Researchers must have the necessary research skills to analyze and manipulation the data , Getting trained to demonstrate a high standard of research practice. Ideally, researchers must possess more than a basic understanding of the rationale of selecting one statistical method over the other to obtain better data insights.
• Usually, research and data analytics projects differ by scientific discipline; therefore, getting statistical advice at the beginning of analysis helps design a survey questionnaire, select data collection methods , and choose samples.

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• The primary aim of data research and analysis is to derive ultimate insights that are unbiased. Any mistake in or keeping a biased mind to collect data, selecting an analysis method, or choosing  audience  sample il to draw a biased inference.
• Irrelevant to the sophistication used in research data and analysis is enough to rectify the poorly defined objective outcome measurements. It does not matter if the design is at fault or intentions are not clear, but lack of clarity might mislead readers, so avoid the practice.
• The motive behind data analysis in research is to present accurate and reliable data. As far as possible, avoid statistical errors, and find a way to deal with everyday challenges like outliers, missing data, data altering, data mining , or developing graphical representation.

LEARN MORE: Descriptive Research vs Correlational Research The sheer amount of data generated daily is frightening. Especially when data analysis has taken center stage. in 2018. In last year, the total data supply amounted to 2.8 trillion gigabytes. Hence, it is clear that the enterprises willing to survive in the hypercompetitive world must possess an excellent capability to analyze complex research data, derive actionable insights, and adapt to the new market needs.

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How to Write the Dissertation Findings or Results – Steps & Tips

Published by Grace Graffin at August 11th, 2021 , Revised On October 9, 2023

Each  part of the dissertation is unique, and some general and specific rules must be followed. The dissertation’s findings section presents the key results of your research without interpreting their meaning .

Theoretically, this is an exciting section of a dissertation because it involves writing what you have observed and found. However, it can be a little tricky if there is too much information to confuse the readers.

The goal is to include only the essential and relevant findings in this section. The results must be presented in an orderly sequence to provide clarity to the readers.

This section of the dissertation should be easy for the readers to follow, so you should avoid going into a lengthy debate over the interpretation of the results.

It is vitally important to focus only on clear and precise observations. The findings chapter of the  dissertation  is theoretically the easiest to write.

It includes  statistical analysis and a brief write-up about whether or not the results emerging from the analysis are significant. This segment should be written in the past sentence as you describe what you have done in the past.

This article will provide detailed information about  how to   write the findings of a dissertation .

When to Write Dissertation Findings Chapter

As soon as you have gathered and analysed your data, you can start to write up the findings chapter of your dissertation paper. Remember that it is your chance to report the most notable findings of your research work and relate them to the research hypothesis  or  research questions set out in  the introduction chapter of the dissertation .

You will be required to separately report your study’s findings before moving on to the discussion chapter  if your dissertation is based on the  collection of primary data  or experimental work.

However, you may not be required to have an independent findings chapter if your dissertation is purely descriptive and focuses on the analysis of case studies or interpretation of texts.

• Always report the findings of your research in the past tense.
• The dissertation findings chapter varies from one project to another, depending on the data collected and analyzed.
• Avoid reporting results that are not relevant to your research questions or research hypothesis.

Does your Dissertation Have the Following?

• Great Research/Sources
• Perfect Language
• Accurate Sources

If not, we can help. Our panel of experts makes sure to keep the 3 pillars of the Dissertation strong.

1. Reporting Quantitative Findings

The best way to present your quantitative findings is to structure them around the research  hypothesis or  questions you intend to address as part of your dissertation project.

Report the relevant findings for each research question or hypothesis, focusing on how you analyzed them.

Analysis of your findings will help you determine how they relate to the different research questions and whether they support the hypothesis you formulated.

While you must highlight meaningful relationships, variances, and tendencies, it is important not to guess their interpretations and implications because this is something to save for the discussion  and  conclusion  chapters.

Any findings not directly relevant to your research questions or explanations concerning the data collection process  should be added to the dissertation paper’s appendix section.

Use of Figures and Tables in Dissertation Findings

Suppose your dissertation is based on quantitative research. In that case, it is important to include charts, graphs, tables, and other visual elements to help your readers understand the emerging trends and relationships in your findings.

Repeating information will give the impression that you are short on ideas. Refer to all charts, illustrations, and tables in your writing but avoid recurrence.

The text should be used only to elaborate and summarize certain parts of your results. On the other hand, illustrations and tables are used to present multifaceted data.

It is recommended to give descriptive labels and captions to all illustrations used so the readers can figure out what each refers to.

How to Report Quantitative Findings

Here is an example of how to report quantitative results in your dissertation findings chapter;

Two hundred seventeen participants completed both the pretest and post-test and a Pairwise T-test was used for the analysis. The quantitative data analysis reveals a statistically significant difference between the mean scores of the pretest and posttest scales from the Teachers Discovering Computers course. The pretest mean was 29.00 with a standard deviation of 7.65, while the posttest mean was 26.50 with a standard deviation of 9.74 (Table 1). These results yield a significance level of .000, indicating a strong treatment effect (see Table 3). With the correlation between the scores being .448, the little relationship is seen between the pretest and posttest scores (Table 2). This leads the researcher to conclude that the impact of the course on the educators’ perception and integration of technology into the curriculum is dramatic.

Paired Samples

Paired samples correlation, paired samples test.

Also Read: How to Write the Abstract for the Dissertation.

2. Reporting Qualitative Findings

A notable issue with reporting qualitative findings is that not all results directly relate to your research questions or hypothesis.

The best way to present the results of qualitative research is to frame your findings around the most critical areas or themes you obtained after you examined the data.

In-depth data analysis will help you observe what the data shows for each theme. Any developments, relationships, patterns, and independent responses directly relevant to your research question or hypothesis should be mentioned to the readers.

Additional information not directly relevant to your research can be included in the appendix .

How to Report Qualitative Findings

Here is an example of how to report qualitative results in your dissertation findings chapter;

How do I report quantitative findings?

The best way to present your quantitative findings is to structure them around the  research hypothesis  or  research questions  you intended to address as part of your dissertation project. Report the relevant findings for each of the research questions or hypotheses, focusing on how you analyzed them.

How do I report qualitative findings?

The best way to present the  qualitative research  results is to frame your findings around the most important areas or themes that you obtained after examining the data.

An in-depth analysis of the data will help you observe what the data is showing for each theme. Any developments, relationships, patterns, and independent responses that are directly relevant to your  research question  or  hypothesis  should be clearly mentioned for the readers.

Can I use interpretive phrases like ‘it confirms’ in the finding chapter?

No, It is highly advisable to avoid using interpretive and subjective phrases in the finding chapter. These terms are more suitable for the  discussion chapter , where you will be expected to provide your interpretation of the results in detail.

Can I report the results from other research papers in my findings chapter?

NO, you must not be presenting results from other research studies in your findings.

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• Published: 07 May 2024

Causal association between low vitamin D and polycystic ovary syndrome: a bidirectional mendelian randomization study

• Bingrui Gao 1 ,
• Chenxi Zhang 1 ,
• Deping Wang 1 , 2 ,
• Bojuan Li 1 ,
• Zhongyan Shan 1 ,
• Weiping Teng 1 &
• Jing Li   ORCID: orcid.org/0000-0002-3681-4095 1  

Journal of Ovarian Research volume  17 , Article number:  95 ( 2024 ) Cite this article

219 Accesses

Metrics details

Recent studies have revealed the correlation between serum vitamin D (VD) level and polycystic ovary syndrome (PCOS), but the causality and specific mechanisms remain uncertain.

We aimed to investigate the cause-effect relationship between serum VD and PCOS, and the role of testosterone in the related pathological mechanisms.

We assessed the causality between serum VD and PCOS by using genome-wide association studies (GWAS) data in a bidirectional two-sample Mendelian randomization (TS-MR) analysis. Subsequently, a MR mediation analysis was conducted to examine the mediating action of testosterone in the causality between serum VD and PCOS. Ultimately, we integrated GWAS data with cis-expression quantitative loci (cis-eQTLs) data for gene annotation, and used the potentially related genes for functional enrichment analysis to assess the involvement of testosterone and the potential mechanisms.

TS-MR analysis showed that individuals with lower level of serum VD were more likely to develop PCOS (OR = 0.750, 95% CI: 0.587–0.959, P  = 0.022). MR mediation analysis uncovered indirect causal effect of serum VD level on the risk of PCOS via testosterone (OR = 0.983, 95% CI: 0.968–0.998, P  = 0.025). Functional enrichment analysis showed that several pathways may be involved in the VD-testosterone-PCOS axis, such as steroid hormone biosynthesis and autophagy process.

Our findings suggest that genetically predicted lower serum VD level may cause a higher risk of developing PCOS, which may be mediated by increased testosterone production.

Introduction

Vitamin D (VD) is an essential fat-soluble steroid hormone that is necessary for calcium-phosphate metabolism, bone homeostasis, cell differentiation, and immune system function. The prevalence of VD deficiency (VDD) in the population has gradually increased over the past few decades. VDD is associated with various diseases, including cardiovascular disease, inflammation, dyslipidemia, weight gain, and infectious diseases [ 1 , 2 ]. Furthermore, mounting studies have indicated the potential link between the serum VD status and women's reproductive health. Firstly, the biological function of VD is mediated via intracellular VD receptors (VDRs), which are distributed among various tissues, encompassing hypothalamic, pituitary tissue, endometrium, and ovary [ 3 , 4 ]. Secondly, VD participates in regulating genes associated with ovarian and placental functions [ 5 , 6 ]. All evidences suggest that the serum VD plays a potentially significant role in female reproductive health.

Polycystic ovary syndrome (PCOS) is the most common endocrine disorder that effects women of reproductive age, with a global incidence ranging 20–25% [ 7 , 8 ]. PCOS will affect woman's endometrial function and oocyte competence [ 9 , 10 ], which leads to reproductive dysfunction in PCOS patients, including infertility, miscarriage, and pregnancy complications [ 11 , 12 , 13 ]. However, the exact pathogenesis of PCOS remains unclear. Prior observational studies have elucidated the correlation between the serum VD and the risk of PCOS. A recent study revealed that serum VD concentration were lower in women diagnosed with PCOS compared to body mass index (BMI)-matched control, suggesting that regardless of BMI, PCOS is correlated with reduced VD level [ 14 ]. However, these studies can only prove that there is a correlation between them, they cannot clarify the causality between them. In addition, hyperandrogenemia stands as one of the diagnostic criteria for PCOS and impacts 60–80% of patients [ 15 ]. Female are actually more sensitive to testosterone even though it is known as a male hormone [ 16 ]. Growing evidences showed that testosterone may play an important role between the serum VD level and the risk of PCOS. Hahn et al. illustrated an association between the serum VD level and the severity of hirsutism in individuals with PCOS [ 17 ]. The research conducted by Latic et al. indicates a negative correlation between serum VD level and testosterone production in patients with PCOS [ 18 ]. However, a study by Mesinovic et al. suggested no discernible correlation between the serum VD level and androgen production in individuals with PCOS [ 19 ]. Moreover, a large observational study by Gallea et al. also showcased the association between serum VD levels, insulin, and body weight among PCOS patients but not specifically with hyperandrogenemia [ 20 ]. The reason for these different results may be due to the fact that observational studies are susceptible to confounding factors as well as various biases [ 21 ]. Therefore, it is not clear whether testosterone production mediate the relationship between serum VD level and the risk of PCOS, due to the limitations of the study methodology.

In recent years, mendelian randomization (MR) analysis is widely used as an epidemiological method in medical research. Firstly, MR analysis can minimize the impact of confounding factors and various biases on the results by simulating randomized controlled trials (RCTs) at the genetic level, and secondly, MR analysis can also determine causality and reduce the impact of reverse causality on the results of the study [ 22 ].

Thus, in this study, we use the bidirectional two-sample MR (TS-MR) analysis to investigate the cause-effect relationship between the serum VD level and the risk of PCOS. Secondly, we perform the mediation MR analysis to test the mediating role of testosterone production between serum VD level and the risk of PCOS. Finally, we used the bioinformatics analysis to assess the possible biological functions and molecular mechanisms between them.

Materials and methods

Study design of mendelian randomization study.

Our study explored the cause-effect of serum VD level as an exposure on the risk of developing PCOS as an outcome trait and the effect of testosterone as a mediator between VD and PCOS through bidirectional TS-MR analysis, multivariable MR (MVMR) and mediator MR analysis (Fig.  1 ). In order to ensure the study's validity, the study needed to meet the three following crucial assumptions [ 23 ] (Fig.  1 C):1) the correlation assumption: instrumental variables (IVs) must be robustly correlated with the exposure factors; 2) the exclusion restriction assumption: IVs are not associated with potential confounders of the exposure or the outcome; and 3) the independence assumption: IVs do not influence the outcome variables through other pathways besides the exposure factors. This study followed guidelines of STROBE-MR [ 24 ] checklist (Table S 1 ).

Flowchart of the study. A Flowchart of the MR study; ( B ) Flowchart of the Bioinformatics study; ( C ) Diagram of the MR assumptions of the association between VD and PCOS; ( D ) Illustrative diagram for the mediation MR analysis framework Abbreviations: MR, mendelian randomization; TS-MR, two-sample MR; VD, vitamin D; PCOS, polycystic ovary syndrome; IVW, inverse variance weighted; BMI, body mass index; FBG, fasting glucose; FI, fasting insulin; MVMR, multivariable MR; BT, bioavailable testosterone; SNPs, single-nucleotide polymorphisms

Data source and IVs selection of mendelian randomization study

We obtained data associated with VD from a large genome-wide association study (GWAS) that identified 143 loci among 417,580 participants which was conducted by Revez et al. in 2020 [ 25 ]. We accessed the summary data related to PCOS from a meta-analysis in the FinnGen and Estonian Biobank (EstBB), which included 3609 cases and 229,788 controls [ 7 ]. Summary data related to bioavailable testosterone (BT) were obtained from the UK Biobank (UKB). Data on serum fasting glucose (FBG) levels were obtained from a UKB GWAS we conducted in 340,002 British participants [ 26 ]. Summary data on circulating concentrations of fasting insulin (FI) were obtained from the MAGIC GWAS included 151,013 participants [ 27 ]. Pooled data related to BMI were acquired from a GWAS meta-analysis within the (GIANT) consortium, encompassing 681,275 participants [ 28 ]. Details of the GWAS database are summarized in Table S 2 .

In the bidirectional TS-MR analysis, Single-nucleotide polymorphisms (SNPs) with genome-wide significance ( P  < 5 × 10 –8 ) were first selected. These SNPs were matched against the SNP-outcome GWAS database to exclude SNPs that could not be matched. To minimize the effects of linkage disequilibrium, we conducted a clumping process with an r 2 threshold of 0.001 and a clumping window of 10,000 kb and excluded these SNPs if present. Subsequently, we performed MR-PRESSO analysis immediately to demonstrate whether there was significant horizontal pleiotropy to exclude outlier SNPs [ 29 ]. To ensure that the IVs were not affected by confounding variables, we searched the PhenoScanner V2 [ 30 ] and deleted obesity-related SNPs associated with BMI and waist circumference (WC). Finally, 88 SNPs (VD on PCOS) and 2 SNPs (PCOS on VD) were used as IVs in the primary bidirectional TS-MR study, respectively. All SNPs exhibited an F statistic greater than 10. The variance explained for each SNP (R 2 ) was calculated using the widely-accepted formula [ 31 , 32 ]. We used the same method as above to screen the SNPs required in the MR mediation analysis. All the IVs SNPs are summarized in Table S 3 - 7 .

Statistic analysis of mendelian randomization study

Initially, the primary analysis aimed to explore the causal relationship between VD and PCOS. We used bidirectional TS-MR analysis to assess the causal relationship between VD and PCOS. In this, we used Cochran's Q test to assess the heterogeneity [ 33 ]; if there was no heterogeneity, we would use the fixed-effects inverse variance weighted (IVW) method, otherwise, we would use the random-effects IVW method [ 34 ]. Furthermore, considering that obesity, abnormal insulin levels, and abnormal glucose values are common in patients with PCOS, we adjusted genetically predicted BMI, FBG, and FI by MVMR to explore the direct causal effect between VD and PCOS. To make the results more robust.

Secondly, a stepwise MR analysis approach was used to examine whether there exist mediation effects of BT between VD and PCOS. To assess the direct causal effect between VD, BT, and PCOS, we performed an MVMR analysis using the MVMR R package [ 35 ]. Conditional F statistics were calculated for assessing the strength of the genetic instruments in MVMR analysis [ 36 ]. The product of the coefficients method [ 37 ] and the multivariate delta method [ 38 ] were used to calculate the indirect effects of VD on PCOS via mediator.

Sensitivity analysis of mendelian randomization study

The following tests were used as sensitivity analyses to assess the robustness of MR effect estimates to invalid genetic variants. Firstly, we conducted MR-Egger regression [ 39 , 40 ], weighted median [ 41 ], and weighted mode [ 42 ] methods. MR-Egger regression can detect and explain horizontal pleiotropy mainly through intercept tests [ 39 , 40 ]. Weighted median can yield impartial estimations even when over half of the information arise from flawed IVs [ 43 ]. We used weighted mode to divide SNPs into multiple subsets based on similar causal effects, and the estimates of causal effects were computed for the subset with the highest number of SNPs [ 42 ]. Secondly, the leave-one-out (LOO) analysis can test whether the results are affected by a single SNP [ 44 ]. Thirdly, as described above we performed MR-PRESSO analysis [ 29 ] to identify the presence of potential horizontal pleiotropic outliers in IVs that could lead to biased results, as well as searching for and removing obesity-related SNPs associated with BMI and WC from the PhenoScanner database [ 45 ].

All analyses were conducted using R version 4.2.0 (R Foundation for Statistical Computing, Vienna, Austria). P values were considered significant at 0.05.

Bioinformatical analysis

We used the largest whole blood expression quantitative trait loci (eQTL) dataset from the eQTLGen consortium, which includes data on cis-eQTLs for 19,250 whole blood expressed genes from 31,684 individuals [ 46 ]. We combined SNPs data of VD-PCOS ( n -SNP = 90) and VD-BT ( n -SNP = 88) with cis-eQTLs data for gene annotation, respectively. Genes with P  < 5*10 –8 and FDR < 0.05 were screened as potentially relevant genes for VD-PCOS and VD-BT.

Subsequently, we used these potentially relevant genes for bioinformatics analyses, including Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. GO analyses [ 47 ], including biological process (BP), molecular function (MF), and cellular composition (CC), are commonly used for large-scale functional enrichment studies. KEGG is a database that stores information about genomes, biological pathways, diseases, and drugs. We used the clusterProfiler package, org.Hs.eg.db package, and enrichplot package in the software R to perform GO and KEGG enrichment analyses of the potentially relevant genes. P  < 0.05 for GO entries and KEGG pathways were considered significant.

Causal effect between serum vitamin D and polycystic ovary syndrome

In our bidirectional TS-MR analysis, the number of IVs of VD on PCOS and PCOS on VD were 90 and 2, respectively. The F-statistic values for each SNP were greater than 10 (Table S 3 ), indicating that the results were almost unaffected by weak instrumental bias. The result of fixed-effects IVW method (Cochran's Q statistic = 81.42, P  = 0.704) indicated that genetically predicted higher level of VD led to a lower risk of developing PCOS after excluding obesity-associated SNPs ( n  = 90 SNPs, OR = 0.750, 95% CI: 0.587–0.959, P  = 0.022) (Table  1 ). MR-Egger, weighted median, and weighted mode methods all obtained similar magnitude and direction to IVW method (Table  1 ). The scatter plot demonstrates the inhibitory effect of individual SNP on PCOS (Fig. S 1 ). Since the MR-Egger P -intercept was greater than 0.05 (Table S 8 ) and the funnel plot (Fig. S 2 ) was roughly symmetrical, there was no indication of horizontal pleiotropy detected in the study. The results of the LOO analyses indicated that there were no potentially affecting SNPs in the main MR analyses (Fig. S 3 ). The result of the result of the MR-PRESSO test did not show any outlier SNPs. Nevertheless, the results of reverse TS-MR showed that genetically predicted risk of developing PCOS did not affect the VD level (fixed-IVW: n  = 2 SNPs, OR = 1.004, 95% CI: 0.987–1.022, P  = 0.640) (Table  1 ).

We subsequently explored the direct effect of the serum VD level on PCOS by MVMR methods, and the results of both Model 1 (adjusted BMI) and Model 2 (adjusted BMI, FBG, and FI) showed that the negative correlation between serum VD level and the risk of PCOS remained similar (Table  2 ). This confirms the robustness of the TS-MR results.

Mendelian randomization mediation analysis

After excluding the outlier SNPs and obesity-related SNPs, MVMR analysis (adjusted BT) revealed direct causal effects of serum VD level (OR: 0.735, 95% CI: 0.552–0.978; P  = 0.035) on the risk of developing PCOS (Table  3 , Fig.  1 D). In the following steps of the MR mediation analysis, we found strong evidence for a causal effect of serum VD level (β: − 0.053, P  = 0.026) on BT (Table  3 ). In addition to this, we also found a causal relationship between BT and PCOS (OR: 1.378, 95% CI: 1.123–1.691; P  = 0.002) (Table  3 ).

Taken together, we found the potential mediation pathways between VD and PCOS: an indirect causal effect of VD on PCOS risk via BT (θ 3  × θ 4 ) (OR: 0.983, 95% CI: 0.968–0.998; P  = 0.025) (Table  3 ). The pathway mediated 5.96% of the total causal effect of VD on PCOS risk. Detailed estimates of direct and indirect causal effects can be found in Table  3 .

Bioinformatics study

The results of the MR study suggested that reduced VD level may lead to the development of PCOS, and BT is a mediator between VD and PCOS, meaning that VD can ultimately influence the development of PCOS by affecting the production of testosterone. On the basis of the above studies, we collected IVs of VD-PCOS ( n -SNPs = 90) and VD-BT ( n -SNPs = 88) respectively, and combined these IVs with cis-eQTLs data for gene annotation respectively. Ultimately, 147 (VD-PCOS) and 164 (VD-BT) potentially relevant genes were annotated (Table S 9 - 10 ), respectively. We then used these genes to perform GO and KEGG analyses.

Firstly, the potentially relevant genes of VD-PCOS were analyzed for enrichment. The results of GO analysis suggested that these genes were mainly related to androgen metabolic process, superoxide metabolic process, cell body membrane, and steroid dehydrogenase activity (Fig.  2 A). The KEGG analysis was mainly enriched in the process of autophagy, steroid biosynthesis, cytochrome P450 metabolic process, and vitamin digestion and absorption process (Fig.  2 C). Subsequently, potentially relevant genes associated with VD-BT were analyzed for enrichment. The results of GO analysis suggested that these genes were mainly associated with steroid metabolism, superoxide metabolism, autophagosome membrane, nuclear androgen receptor binding, and vitamin transmembrane transporter activity (Fig.  2 B), and the KEGG analysis was mainly enriched for autophagy, steroid biosynthesis, vitamin digestion and absorption, and cholesterol metabolism process (Fig.  2 C). All information of the enrichment analysis is shown in the additional file (Table S 11 -S 12 ).

Gene Ontology and Kyoto Encyclopedia of the Genome pathway enrichment analysis of potentially relevant genes. A The GO enrichment analysis for potentially relevant genes related to VD and PCOS; ( B ) The GO enrichment analysis for potentially relevant genes related to VD and BT; ( C ). The KEGG pathway analysis for potentially relevant genes related to VD and PCOS; ( D ). The KEGG pathway analysis for potentially relevant genes related to VD and BT. Abbreviations: VD, vitamin D; PCOS, polycystic ovary syndrome; BT, bioavailable testosterone; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of the Genome

In our bidirectional TS-MR analysis, we found that higher serum VD level was causally associated with a lower risk of developing PCOS (OR = 0.750, 95% CI: 0.587–0.959, P  = 0.022), whereas there was little evidence for a causal effect of the risk of PCOS on the effect of serum VD level. Furthermore, our MR mediation analysis confirmed that testosterone can act as one of the mediating factors between the causality of VD and PCOS (OR = 0.983, 95% CI: 0.968–0.998, P  = 0.025). The mediating effect of testosterone was 5.96%. Ultimately, we utilized potentially relevant genes for GO and KEGG enrichment analysis to assess the involvement of testosterone and the potential biological and molecular mechanisms between them.

VD, a lipid-soluble vitamin, plays a pivotal role in numerous biological processes. Primarily synthesized endogenously through exposure to sunlight, it is also acquired, albeit to a lesser extent, from dietary sources [ 48 ]. VDD is considered a globally prevalent nutritional deficiency, with various studies reporting prevalence rates of 58–91% among infertile women [ 49 ]. A cross-sectional study encompassing 625 women diagnosed with PCOS and 217 control subjects revealed that Chinese women diagnosed with PCOS exhibited notably lower level of VD compared to their healthy [ 50 ]. The result from a large observational study conducted by Krul-Poel et al. similarly demonstrated significantly diminished level of VD among women within the PCOS group [ 51 ]. Recent research has demonstrated that women with PCOS exhibit lower serum concentrations of VD compared to BMI-matched controls. This implies that the level of VD is linked to PCOS irrespective of BMI [ 14 ]. Aligned with the outcomes of these observational studies, our research indicated that higher serum VD level serves as a protective factor for the risk of PCOS. To eliminate the influence of obesity as a potential confounder on the results, we excluded obesity-related SNPs in our TS-MR analysis. Subsequently, in our MVMR analyses, we adjusted for genetically predicted BMI, FBG, and FI to explore the direct causal relationship between VD and PCOS. These stringent measures significantly enhance the credibility and robustness of our findings.

The precise mechanism through which serum VD operates on PCOS remains elusive. Hyperandrogenemia stands as a pivotal diagnostic criterion for PCOS. Numerous past studies have concentrated on exploring the correlation between serum VD and hyperandrogenemia in PCOS, yet the conclusions drawn from these studies have not reached a consensus. A study conducted by Latic N et al. revealed a negative correlation between serum VD level and testosterone in PCOS patients. Additionally, Menichini et al. demonstrated a positive impact of VD supplementation (4000 IU) on total testosterone [ 52 ]. However, a study by Mesinovic et al. suggested no discernible correlation between serum VD and androgens in individuals with PCOS [ 19 ]. Moreover, a large observational study by Gallea et al. also showcased associations between serum VD level, insulin, and body weight among PCOS patients but not specifically with hyperandrogenemia [ 20 ]. The inconsistencies observed in these findings might stem from variations in race, sample sizes, seasonal disparities, and the lifestyles of the included subjects. Our study, employing Mendelian randomization, effectively mitigated the impact of sample size, seasonal fluctuations, and diverse lifestyles on the outcomes. Furthermore, our research focused solely on individuals of European ethnicity, and we excluded BMI-related SNPs when incorporating instrumental variables, thereby significantly reducing BMI's potential confounding effect on the results. These measures ensured the robustness and reliability of our findings. Our results suggest that testosterone acts as a mediator between serum VD and PCOS, implying that serum VD may potentially contribute to the development of PCOS by influencing testosterone production.

The mechanism by which serum VD ultimately contributes to the development of PCOS by affecting testosterone remains unclear, but possible explanation has been proposed. Serum VD heightens the activity of aromatase within the ovary, thereby fostering the conversion of androgens to estrogens, ultimately culminating in diminished androgens production [ 53 ]. Kinuta et al. demonstrated a marked reduction in aromatase activity within the ovaries of VDR knockout mice in contrast to the control group [ 54 ]. In addition, we performed bioinformatics analysis to explore more possible biological mechanisms. Firstly, the results of GO and KEGG analyses of potentially related genes of VD-PCOS showed that steroid biosynthetic process, androgen metabolic process, and nuclear androgen receptor binding process were the possible biological mechanisms between the causality of the serum VD level and PCOS. These results are consistent with the results of our bidirectional TS-MR analysis, demonstrating again that the serum VD can ultimately influence the development of PCOS by modulating testosterone production. Subsequently, we subjected potentially relevant genes associated with VD-BT to bioinformatics analysis. The results suggested that autophagy process and superoxide metabolism process might be the biological mechanism between serum VD and testosterone.

There are very few studies linking autophagy to PCOS, and the results of these studies suggest that the development of PCOS is closely related to the process of autophagy [ 55 ]. Texada et al. showed that autophagy can regulate steroid production by modulating cholesterol transport in endocrine cells [ 56 ]. In addition to this, the role of VD-mediated autophagy in disease has been extensively studied, and basic study by Hu et al. showed that VD can mediate the regulation of autophagy function through gastric epithelial cell VD receptors, which ultimately affects the pathogenic effects of H. pylori [ 57 ]. However, whether VD can mediate autophagy ultimately leading to PCOS remains unknown. The results of the bioinformatics study in this study suggest that autophagy is most likely one of the important mechanisms underlying the relationship between VD and PCOS.

Our study has proved that lower serum VD level causes higher prevalence of PCOS. The latter could have oocyte competence and endometrial function impaired [ 9 , 10 ], but also cause a few adverse outcomes related to reproduction, such as infertility, miscarriage, and premature delivery [ 12 , 13 ]. It has been found that VDD could decrease the rates of ovulation and success pregnancy in the PCOS patients, leading to less live birth [ 58 ]. In addition, It has been reported that serum VD level was independent predicting factor for live birth in the PCOS patients received ovulati0on induction [ 59 ]. Yasmine et al. have reported that endometrial thickness of PCOS patients maybe improved after VD administration [ 60 ]. A recent meta-analysis has shown that VD supplementation to PCOS women could decrease the occurrence rates of early miscarriage and premature delivery [ 53 ]. The nuclear receptor of VD (VDR) and 1,25(OH)2D3 membrane binding protein are expressed in both ovarian granulosa and theca cells [ 61 , 62 ]. It has been found that VD can regulate the expression of enzymes in the VDR and ovary, ultimately regulating ovarian function [ 63 ]. One study showed that VDR mRNA was significantly less expressed in granulosa cells of the women with PCOS [ 64 ]. It may cause PCOS patients to be more sensitive to VDD. Based on the above studies and ours, serum VD level need be monitored in the female population, especially in the women of reproductive age, and timely VD administration in PCOS patients would help to improve their reproductive function and pregnancy outcomes.

Our research has several advantages. Primarily, this study confirms the direct causal relationship of the serum VD level on the risk of PCOS through the utilization of the TS-MR analysis method. This method avoids the limitation commonly found in most observational studies, thereby fortifying the reliability and validity of our finding. Secondly, we ascertain the mediating function of testosterone in the relationship between serum VD and PCOS via MR mediation analysis, thus laying the groundwork for subsequent mechanistic studies. Finally, this is the first study to combine MR studies and bioinformatics analyses together to explore causal relationship and potential functional mechanisms between serum VD level, testosterone, and the risk of PCOS, which is quite different from other studies. Nonetheless, this study also has limitations. Firstly, our study failed to capture dietary and sun exposure information that may affect serum VD level. Secondly, the use of exclusively European data in a MR analysis may not be generalizable to other ethnic populations, albeit reducing the impact of ethnicity bias on the study outcomes. Finally, the absence of relevant data prevented us from independently exploring the relationship of serum VD 2 /D 3 with the risk of PCOS, warranting further investigation.

Conclusions

In conclusion, our studies confirm the causality between lower serum VD level and higher risk of PCOS. Furthermore, testosterone may act as a mediator between serum VD and PCOS. These findings emphasize the clinical importance of testing serum VD level and timely VD supplementation as possible primary prevention and treatment of PCOS.

Availability of data and materials

No datasets were generated or analysed during the current study.

Abbreviations

• Polycystic ovary syndrome

Genome-wide association studies

Two-sample Mendelian randomization

Cis-expression quantitative loci

VD deficiency

VD receptors

Body mass index

• Mendelian randomization

Multivariable MR

Instrumental variables

Bioavailable testosterone

Fasting glucose

Fasting insulin

Single-nucleotide polymorphisms

Waist circumference

Inverse variance weighted

Leave one out

Gene ontology

Kyoto Encyclopedia of Genes and Genomes

Biological process

Molecular function

Cellular composition

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Acknowledgements

We would like to express our sincere gratitude to the compilers of the GWAS summary dataset for their management of the data collection and data resources.

This work was supported by the General Program of National Natural Science Foundation of China (grant number No.81771741), Distinguished Professor at Educational Department of Liaoning Province (grant number No. [2014]187) to JL.

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Department of Endocrinology and Metabolism, The Institute of Endocrinology, NHC Key Laboratory of Diagnosis and Treatment of Thyroid Diseases, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, 110000, P.R. China

Bingrui Gao, Chenxi Zhang, Deping Wang, Bojuan Li, Zhongyan Shan, Weiping Teng & Jing Li

Department of Endocrinology and Metabolism, Hongqi Hospital Affiliated to Mudanjiang Medical College, Mudanjiang, Heilongjiang, 157011, P.R. China

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Contributions

Designed the study: Jing Li, Bingrui Gao; Collected data: Bingrui Gao, Chenxi Zhang; Performed statistical analyses: Bingrui Gao, Deping Wang, Bojuan Li; Drafted the manuscript: Bingrui Gao; Supervised the study and reviewed the manuscript: Jing Li, Zhongyan Shan, Weiping Teng.

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Supplementary Information

Additional file 1: table s1..

STROBE-MR Checklist; Table S2. Key characteristics of participating studies; Table S3. GWAS significant SNPs used as genetic instruments for VD level on PCOS; Table S4. GWAS significant SNPs used as genetic instruments for PCOS on VD level; Table S5. GWAS significant SNPs used as genetic instruments for VD level on BT; Table S6. GWAS significant SNPs used as genetic instruments for BT on PCOS; Table S7. GWAS significant SNPs used as genetic instruments for BT and VD level on PCOS; Table S8. Heterogeneity and directional pleiotropy test using MR-Egger intercepts; Table S9. Potentially relevant genes corresponding to IVs associated with VD and PCOS; Table S10. Potentially relevant genes corresponding to IVs associated with VD and PCOS; Table S11. GO and KEGG enrichment analysis for potentially relevant genes related to VD and PCOS; Table S12. GO and KEGG enrichment analysis for potentially relevant genes related to VD and BT; Figure S1. Scatter plot of the MR estimates for the association of VD level with PCOS; Figure S2. Funnel plot reveals overall heterogeneity of the impact of VD on PCOS; Figure S3. Leave-one-out analysis of the impact of the VD on PCOS.

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Gao, B., Zhang, C., Wang, D. et al. Causal association between low vitamin D and polycystic ovary syndrome: a bidirectional mendelian randomization study. J Ovarian Res 17 , 95 (2024). https://doi.org/10.1186/s13048-024-01420-5

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• Published: 13 May 2024

Integrated single-cell and bulk RNA-Seq analysis enhances prognostic accuracy of PD-1/PD-L1 immunotherapy response in lung adenocarcinoma through necroptotic anoikis gene signatures

• Ping Sui 1 , 2   na1 ,
• Xueping Liu 3   na1 ,
• Cheng Zhong 4 &
• Zhanming Sha 5  

Scientific Reports volume  14 , Article number:  10873 ( 2024 ) Cite this article

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• Cell biology

In addition to presenting significant diagnostic and treatment challenges, lung adenocarcinoma (LUAD) is the most common form of lung cancer. Using scRNA-Seq and bulk RNA-Seq data, we identify three genes referred to as HMR, FAM83A, and KRT6A these genes are related to necroptotic anoikis-related gene expression. Initial validation, conducted on the GSE50081 dataset, demonstrated the model's ability to categorize LUAD patients into high-risk and low-risk groups with significant survival differences. This model was further applied to predict responses to PD-1/PD-L1 blockade therapies, utilizing the IMvigor210 and GSE78220 cohorts, and showed strong correlation with patient outcomes, highlighting its potential in personalized immunotherapy. Further, LUAD cell lines were analyzed using quantitative PCR (qPCR) and Western blot analysis to confirm their expression levels, further corroborating the model's relevance in LUAD pathophysiology. The mutation landscape of these genes was also explored, revealing their broad implication in various cancer types through a pan-cancer analysis. The study also delved into molecular subclustering, revealing distinct expression profiles and associations with different survival outcomes, emphasizing the model’s utility in precision oncology. Moreover, the diversity of immune cell infiltration, analyzed in relation to the necroptotic anoikis signature, suggested significant implications for immune evasion mechanisms in LUAD. While the findings present a promising stride towards personalized LUAD treatment, especially in immunotherapy, limitations such as the retrospective nature of the datasets and the need for larger sample sizes are acknowledged. Prospective clinical trials and further experimental research are essential to validate these findings and enhance the clinical applicability of our prognostic model.

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Introduction.

Current- and ex-smokers, as well as non-smokers, are at high risk of lung adenocarcinoma (LUAD), the predominant histologic subtype of lung cancer. Accounting for about 40% of lung cancer cases 1 , LUAD has a bad prognosis with an only approximately 16% 5-year survival rates 2 . Patients with early-stage LUAD may be able to undergo standard surgery, but those with advanced LUAD will still face considerable challenges from conventional radiology and chemotherapy 3 , 4 . This critical situation has led to the development of immune checkpoint inhibitors, particularly those targeting PD-1 and PD-L1, have transformed the therapeutic landscape, offering hope for better outcomes 5 . However, the variability in response to these therapies underscores the necessity of identifying novel approaches and refining patient selection to enhance the efficacy of treatment in LUAD.

In recent research on the tumor microenvironment, cancer-associated fibroblasts (CAFs) have been identified as playing a pivotal role in various cancers 6 . These cells originate from normal fibroblasts, which are transformed under the influence of hormones and cytokines within the tumor microenvironment. GPX8 has been implicated in fostering an immunosuppressive tumor microenvironment, which contributes to the adverse prognosis observed in LUAD 7 .

Diverse forms of programmed cell death have long been focal points of tumor research. There are numerous factors making tumor progression possible, but necroptosis, mediated by RIP1 kinase and RIP3, is one of the most important. Necroptosis has a significant impact on tumor outcome 8 . Additionally, necroptosis regulatory factors and their transcriptional changes could markedly impact cancer prognosis in various solid tumors 9 , 10 , 11 . Found by Seifert el al, relationship exists between necroptosis and tumor microenvironment (TME) signaling through the RIP1/RIP3 pathway 12 . Additionally, several studies have demonstrated that necroptosis can also promote tumor growth by recruiting inflammatory immune cells 13 .

Anoikis occurs when cells detach from the extracellular matrix (ECM), disrupting integrin ligation, a necessary function of tumor cells after detachment from the ECM 14 . Through both intrinsic and extrinsic pathways, various molecular markers including TNF-α, Bcl-2 and novel signal pathways induced by anoikis have been identified as a crucial factor in tumor progression 15 , 16 , 17 . Anoikis is uniquely capable of influencing cancer progression and metastatic spread due to its immune-related checkpoints, offering new immunotherapeutic approaches.

All mentioned evidence suggests a significant interplay between necroptotic anoikis in the context of lung adenocarcinoma (LUAD). To address these above challenges, our study initially focuses on unraveling the intricate interactions at the single-cell level to unveil the underlying mechanisms driving LUAD progression. Expanding our exploration to the transcriptomic landscape. Building on these results, we develop predictive models to assess immune infiltration and identify potential therapeutic targets. Ultimately, our aim is to enhance the treatment against lung cancer, particularly by improving the efficacy of PD-1 and PD-L1 checkpoint inhibitors, thus paving the way for more personalized and effective therapeutic strategies.

Materials and methods

Single cell and bulk rna-seq data sources and processing.

Lung carcinoma tissue samples, sourced from the Cancer Genome Atlas (TCGA) database ( https://cancergenome.nih.gov/ ), were analyzed for gene expression patterns. This analysis incorporated vital clinical factors like patient's survival status, total lifespan post-diagnosis, demographic details (age and gender), and the grading of the lung carcinoma. Samples missing comprehensive survival data were omitted from the study. The TCGA database provided a wealth of information, including RNA sequencing transcriptome profiles (quantified as FPKM values) and in-depth clinicopathological characteristics from a cohort of 500 lung carcinoma cases and 59 healthy lung tissue samples. A thorough examination of genetic alterations in these lung carcinoma patients was undertaken, with a focus on identifying patterns in somatic mutations and copy number variations (CNVs). To further substantiate these findings, additional comparative analysis was carried out, involving 127 lung carcinoma specimens, retrieved from the GEO database with the accession of GSE50081. This comparative approach aimed to validate the initial observations and explore any potential genomic markers or trends unique to lung carcinoma.

Adenocarcinoma tissues from the GSE149655 and GSE162498 datasets were sequenced on the 10X Genomics platform using single-cell RNA sequencing (scRNA-seq). A comprehensive bioinformatics protocol was applied to the scRNA-seq data. Initially, we used the Seurat analytical package for preprocessing. As part of this project, a correlation assessment was conducted to determine relationships between sequencing depth, mitochondrial gene representation, and intracellular gene counts using the PercentageFeatureSet function for quantifying mitochondrial gene expression compared to total gene expression.

For a rigorous analytical framework, gene expression was filtered, retaining only those genes expressed in a minimum of 5 cells. Cell selection criteria were stringently applied, encompassing a range of gene expression (over 300 and under 5000 genes), a mitochondrial gene percentage below 10%, and a threshold of 1000 unique molecular identifiers (UMIs) per cell. Post-filtering, scRNA-seq data normalization was conducted via the LogNormalize method, laying the groundwork for accurate subsequent analyses.

Determination and annotation of necroptotic anoikis-associated DEGs

Genes associated with anoikis were identified through comprehensive database searches in GeneCards [ https://www.genecards.org/ ] and Harmonizome [ https://maayanlab.cloud/Harmonizome/ ], using ‘Anoikis’ as the principal search parameter. This approach resulted in the identification of 640 genes related to anoikis and 67 genes connected to necroptosis. These genes were then rigorously screened, applying a relevance threshold of 0.4. An unsupervised clustering analysis, focusing on the expression patterns of these necroptotic anoikis-related genes, was performed using the consensusClusterPlus package in the R programming framework.

Necroptosis-related genes were extracted from a curated selection of existing scientific publications. A Pearson correlation examination was initiated to delineate the associations between genes governing overall anoikis and necroptosis, adhering to strict parameters: a Correlation Coefficient (Cor) exceeding 0.6 and a significance level (P-value) below 0.05. This meticulous process unveiled 25 genes distinctly linked to necroptotic anoikis. Further, a comparative analysis of these genes was undertaken. In this phase, the overall profile of differentially expressed genes (DEGs) was ascertained, guided by criteria of an absolute fold change surpassing 0.585 and a p-value under 0.05.

Unsupervised clustering of necroptotic anoikis-associated differentially expressed genes

Cluster analysis devoid of supervision was executed utilizing the ConsensusClusterPlus package in R, leveraging the k-means algorithm, a mainstay in machine learning. This approach involves categorizing cases into distinct groups based on specific biomarkers or signatures indicative of certain biological states or processes. The expression patterns of these hallmark gene sets mirror these specific biological dynamics. After multiple sampling, the optimal k value was identified when the number of clusters k = 2, 3, 4, … 9. Analyzing variations in the Cumulative Distribution Function (CDF) curve areas by employing tools like the Item-Consensus plot and the Proportion of Ambiguous Clustering score, the determination of the ideal cluster quantity was achieved when the index was up to the approximate maximum. This led to the delineation of two separate clusters in the context of necroptotic anoikis, designated as cluster A and B. Following this, a comparative analysis of the Overall Survival (OS) rates between these clusters was conducted, employing Kaplan–Meier survival plots.

Correlations between necroptotic anoikis-associated gene signature and clinical parameters

From the univariate Cox regression scrutiny of the intersected genes related to necroptotic and immune responses in lung cancer, P-values exceeding 0.01 were deemed statistically significant. Following this, a Cox regression construct, underpinned by the LASSO (Least Absolute Shrinkage and Selection Operator) approach, was developed. This model integrated genes with identified prognostic value from training cohorts within The Cancer Genome Atlas (TCGA). The prognostic coefficients for these genes were determined by implementing a tenfold cross-validation on the lambda values extracted from the ‘glmnet’ package.

This process led to the identification of a risk signature correlating with both necroptotic and immune response, effectively prognosticating patient outcomes in lung carcinoma. The risk score was computed using the formula:

On the basis of the median risk score, lung carcinoma patients were grouped into high-risk and low-risk categories. Kaplan–Meier analysis and log-rank tests were used to compare overall survival times between the groups. To evaluate the predictive accuracy of the gene signature, survival, survivalminer, and timeROC packages were used. A similar statistical method and formula were used in order to confirm the prognostic validity of this gene signature in lung carcinoma in the GEO cohort.

Identifying a prognostic signature of necrotic anoikis subcluster and its response to chemotherapy drugs

Using single-sample gene set enrichment analysis (ssGSEA), we examined the differences in immune cell infiltration between populations at high and low risk. To determine the efficacy of riskscore as a predictor of immunotherapy response, a comparative analysis of immune checkpoint expression levels was performed. Additionally, the oncoPredict R package was used to identify disparities in the effectiveness of targeted therapies among different patient cohorts.

Function enrichment analysis

ClusterProfiler R package's Gene Set Enrichment Analysis (GSEA) feature was used to analyze function enrichment. As part of this analysis, MSigDB was used as the source of the gene set ‘c2.cp.kegg.v7.4.symbols.gmt’ from the C2 gene set. It was found that the top five hallmark gene sets in each subgroup, prioritizing those with a p-value below 0.05, were related to functional pathway differences between these subtypes. Moreover, functional pathway differences between these subtypes were determined by gene set variation analysis. To determine whether differentially expressed genes (DEGs) have gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) features, we used the ‘clusterProfiler’ R package, focusing on q-values less than 0.05 to determine statistical significance. To visually delineate the enriched KEGG pathways among the subtypes, heatmaps were meticulously crafted using the ggkegg function within the ggplot2 R package. This method involved mapping statistically significant KEGG pathways to their corresponding gene expression profiles 18 , 19 , 20 . The resulting heatmaps were then color-coded based on the degree of enrichment, providing a detailed and intuitive visualization that underscored the distinct biochemical pathways active in each subtype, essential for interpreting complex biological differences.

Predictive nomogram development and validation

Evaluating the prognostic efficacy of a risk score for lung cancer involved both univariate and multivariate Cox regression analyses, coordinated with clinical factors like age and sex. These analyses identified independent factors for prognosis, adhering to a p-value significance criterion under 0.05. An interactive graphical representation, a nomogram, was developed using the ‘regplot’ package in R, designed to forecast overall survival probabilities at 1, 3, and 5 years for individuals with lung cancer.

The accuracy of these prognostic estimations was validated through the construction of calibration curves. To appraise the predictive significance and discriminatory power for 3, 5, and 10-year survival intervals, Kaplan–Meier survival plots were employed along with time-dependent receiver operating characteristic (ROC) analyses. Further, a decision curve analysis (DCA) was executed to affirm the predictive utility and clinical applicability of the nomogram in the context of lung cancer prognosis.

Immune infiltration analysis

The ESTIMATE algorithm, which utilizes gene expression signatures to approximate the proportion of immune and stromal cells within tumor samples, was employed to calculate the ImmuneScore, StromalScore, and ESTIMATEScore for predicting tumor purity. These scores were correlated with DRG expression using Spearman's method, with results depicted in scatter plots that include p-values and correlation coefficients.

For the assessment of tumor microenvironment (TME) cell infiltration, we employed the single-sample gene set enrichment analysis (ssGSEA) approach using the “GSVA” R package. This method facilitates the evaluation of immune cell presence based on gene expression profiles indicative of specific immune cells. Using Spearman correlation analysis, we investigated the associations between key genes and various immune cell types, streamlining the focus to include key immune populations such as activated B cells, CD4+ and CD8+ T cells, dendritic cells, and natural killer cells among others, providing a comprehensive yet concise assessment of immune landscape within the tumor microenvironment.

Process of epigenetic mutation data

Somatic alteration data for the TCGA-LUAD cohort were acquired from the TCGA database, facilitating an in-depth analysis of genetic variations. Tumor Mutational Burden (TMB) was meticulously defined as the total count of somatic, coding, base substitution, and insertion-deletion mutations per megabase of the genome sequenced. This calculation included non-synonymous and frameshift indels, adhering to a stringent 5% detection threshold to ensure precision. For the quantitative assessment of somatic non-synonymous point mutations within individual samples, the “maftools” R package was employed. This package not only provides comprehensive tools for the analysis, visualization, and summarization of mutation annotation format (MAF) files but also supports comparative studies and co-occurrence analysis. All analyses were performed using R version 4.1.3 (14/12/2023), providing a reliable and consistent computational environment.

Cell culture

The lung cancer cell lines A549, along with normal lung fibroblast cells (MRC-5), were sourced from the American Type Culture Collection (ATCC). To prevent bacterial growth, a culture medium composed of Dulbecco’s Modified Eagle Medium (DMEM, Gibco, USA) enriched with 10% fetal bovine serum (FBS) and supplemented with 100 g/mL of a 1% penicillin–streptomycin solution was utilized. Cell cultures were maintained in an incubator set at 37 °C with a 5% humidity level, ensuring optimal conditions for cellular proliferation and health, which are critical for the validity of lung cancer research experiments.

RNA isolation followed by quantitative reverse transcription-polymerase chain reaction (qRT-PCR) analysis

Quantitative real-time PCR (qRT-PCR) was utilized to evaluate gene expression levels in the human lung adenocarcinoma cell lines A549, as well as in MRC-5 normal lung fibroblasts. Total RNA was extracted using Trizol reagent (ThermoFisher, 15596026), and its purity and concentration were assessed using a NanoDrop 2000 spectrophotometer. Reverse transcription and qRT-PCR were conducted in accordance with the protocols provided with the TSK301 Reverse Transcription System Kit (Masterbio TSK301M).

The SYBR Green RT-qPCR Master Mix, essential for qRT-PCR, was supplied by Tsingke Biotechnology Co., Ltd. (Beijing, China). Primers targeting specific genes were designed and synthesized by GeneWIZ Bioengineering Co. in Suzhou. The PCR conditions included a denaturation step at 95 °C for 10 s, annealing at 60 °C for 30 s, and an elongation phase at 60 °C for 30 s. Glyceraldehyde 3-phosphate dehydrogenase (GAPDH) served as an internal control, and gene expression levels were normalized against it for quantitative assessments.

Cell cultures of A549 and MRC-5 lines were grown in DMEM (Gibco, USA) enriched with 10% fetal bovine serum (ABW, AB-FBS-1050S, Uruguay) and 100 g/mL penicillin–streptomycin (Invitrogen, Carlsbad, CA, USA). Cultivation occurred in a humidified atmosphere containing 5% CO2 at 37 °C, adhering to standard protocols. Cells were used for experimental purposes when they reached approximately 80% confluence during the logarithmic phase of growth. The study details specific primer sequences for each investigated gene, which include:

For KRT6A: Forward primer: ACCAGACCTTGCCGTTCATTAT, Reverse primer: TGACGTGGGAGTTGTGGATG.

For HMMR: Forward primer: GCTTGAGGTGTAGATGTGTCC, Reverse primer: CCCACGGGGCAAGATTTGAA.

For FAM83A: Forward primer: GCAAAACAGGGAAGAGTGTTCAT, Reverse primer: TAAGCCAACTCCAAGCCTGA.

Western blotting

In this lung cancer investigation, Western blot analysis was conducted with proteins extracted from human lung adenocarcinoma cell lines (A549) and normal lung fibroblast cells (MRC-5). Protein extraction entailed lysing cells in RIPA buffer supplemented (Beyotime, Shanghai, China) with a PMSF protease inhibitor (CoWin Biosciences, Jiangsu, China) at a 1:100 ratio. Total protein was quantified using a bicinchoninic acid (BCA) protein assay kit (Thermo Scientific, Guangzhou, China). After quantification, each protein sample, measuring 10 μg, was loaded onto a 10% sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE). Following electrophoresis, proteins were transferred to a 0.45 μm polyvinylidene difluoride membrane. These membranes were initially incubated with primary antibodies overnight at 4 °C, targeting KRT6A (BS, 1:1000), HMMR (BS, 1:1000), FAM83A (BS, 1:1000), and GAPDH (CST, 1:1000) as a standard reference. Protein band visualization was achieved using a chemiluminescence imaging system (Beijing, China) and an ECL chromogenic substrate kit (Thermo Fisher Scientific, Guangzhou, China).

Statistical analysis

In this research, version 4.3.1 of the R software environment was utilized for rigorous data processing, analysis, and visualization. The Kaplan–Meier method, facilitated by the ‘survival’ package in R, was employed to assess survival data, along with log-rank test computations for survival analysis. LASSO regression, integrating cross-validation techniques, was performed using the ‘glmnet’ package. Receiver operating characteristic curves (ROCs) were generated in conjunction with the survival package, utilizing the ‘survminer’ package. For data following a Gaussian distribution, the Student’s t -test was applied, while the Wilcoxon rank-sum test was used for datasets deviating from normal distribution. Comparative statistical assessments between two distinct datasets involved methods such as the t -test and the Mann–Whitney U test. For multi-group comparisons, one-way analysis of variance (ANOVA) was the chosen method. Data visualization was achieved with ‘ggpubr’ and ‘ggplot2’, which are well-regarded R packages for producing high-quality graphical representations. A p-value of 0.05 was established as the criterion for statistical significance across all comparative evaluations, barring specific exceptions.

ScRNA-Seq analysis of necroptotic anoikis genes in LUAD

The gene expression profiles related to necroptotic anoikis in lung adenocarcinoma (LUAD) were meticulously evaluated. Clustering analysis, based on data from 10 patients in the GEO database (GSE149655 and GSE162498), was performed (as shown in Fig.  1 A,B). This analysis involved assessing the association between two programmed cell death pathways and identified clusters by computing a “necroptosis score” and “anoikis score” (Fig.  1 C,D). The role of cancer-associated fibroblasts (CAFs) in the immune microenvironment was acknowledged by selecting marker genes ACTA2, FAP, PDGFRB, and NOTCH3 21 , 22 , 23 , aiding in dimensionality reduction for the clustering analysis, which identified five CAF clusters (Fig.  1 E).

Comprehensive single-cell RNA sequencing analysis of lung adenocarcinoma tissue samples. ( A ) Uniform Manifold Approximation and Projection (UMAP) visualization depicting the heterogeneity of lung adenocarcinoma samples, each color representing a unique sample from the GEO dataset. ( B ) UMAP plot showing discrete cell populations based on expression profiles of cancer-associated fibroblast (CAF) marker genes ACTA2, FAP, PDGFRB, and NOTCH3. To the right, expression density plots for each marker gene across the UMAP coordinates. ( C ) Necroptosis score distribution across lung adenocarcinoma samples overlayed on UMAP coordinates. ( D ) Anoikis score distribution across lung adenocarcinoma samples overlayed on UMAP coordinates. ( E ) Dimensionality reduction clustering analysis depicting the presence of distinct clusters based on the expression of ACTA2, FAP, PDGFRB, and NOTCH3 marker genes. ( F ) Bar plot representing the proportion of different CAF clusters within individual lung adenocarcinoma samples from the GEO dataset. ( G ) Bubble plot illustrating the enriched pathways and biological processes across identified clusters, with bubble size representing the strength of association. KEGG pathway information was utilized for pathway enrichment analysis. ( H ) UMAP plot differentiating malignant from non-malignant cells based on gene expression signatures.

The study also analyzed the proportion of each CAF subtype in various samples (Fig.  1 F). Pathway analysis revealed functional differences among clusters, with ECM-receptor interaction and focal adhesion being prominent in certain clusters, potentially contributing to anoikis resistance (Fig.  1 G). UMAP clustering differentiated malignant from non-malignant cells (Fig.  1 H).

The relationship between the counts of unique molecular identifiers (UMIs) and mRNAs was investigated, showing a strong association, but no clear correlation with mitochondrial gene content (Supplementary Fig.  1 A). Pre- and post-quality assurance data were visualized using violin plots (Supplementary Fig.  1 B,C). Principal component analysis (PCA) indicated the exclusion of the 10th principal component from further analysis, given its statistical significance (Supplementary Fig.  1 D,E).

To further explore necroptotic anoikis-related signatures, the study detailed the differential gene expression across the identified clusters (Supplementary Fig.  2 A). Variations in gene expression among these clusters were quantified, providing a deeper understanding of the diverse gene expression patterns related to necroptotic anoikis in LUAD. In Supplementary Fig.  2 B, we showed the top five marker genes among these identified clusters, which illustrated the difference in signature gene expression. Furthermore, we next explored various genes across different cell clusters in Supplementary Fig.  2 C. These results clearly indicated there were significant differences among the clusters we categorized.

Mutation landscape of necroptotic anoikis genes in LUAD

The mutation analysis of necroptotic anoikis genes in lung adenocarcinoma (LUAD) revealed significant genetic alterations within a subset of the cohort, as derived from the GEO dataset. An OncoPrint visual representation highlighted these mutations in 224 out of 616 samples (36.36%), underscoring the prevalence of genetic aberrations (Fig.  2 A). Notable genes with frequent alterations included ITGA8 (9%), ZEB1 (7%), ZEB2 (6%), among others, exhibiting a range of mutation types from missense to frameshift deletions. These genes were proved to relate with programed cell deaths and cancer immune in previous studies 24 , 25 , 26 .

Overview of necrotic anoikis-related gene alterations in lung adenocarcinoma. ( A ) OncoPrint visualization detailing the frequency and types of genetic alterations for necrotic anoikis-related genes in a lung adenocarcinoma cohort, with mutations such as missense, frameshift insertion, and deletion marked across samples (top panel). The right panel quantifies the proportion of lung adenocarcinoma samples exhibiting specific genetic alterations. ( B ) The distribution of genetic alterations for each necrotic anoikis-related gene is represented in a stacked bar chart, with distinct colors indicating the type of alteration (middle panel). ( C ) A frequency plot delineates the incidence of copy number variations (CNVs) across the cohort, identifying gains and losses in these genes (lower left panel). ( D ) Differential gene expression of necrotic anoikis-related genes is illustrated via boxplots, with each data point representing expression variation in individual lung adenocarcinoma samples (bottom panel). The visualizations were crafted using the R packages ggplot2 for general plotting and ComplexHeatmap, which provides capabilities for more advanced visual representations. The analysis and visualization were performed in RStudio (version 2023), which can be downloaded from https://posit.co/download/rstudio-desktop/ .

The study further examined the distribution of these mutations across various LUAD patient samples, uncovering a heterogeneous pattern of genetic changes. Boxplots illustrating the expression levels of these genes revealed significant variability across patients, suggesting potential links to diverse clinical outcomes (Fig.  2 B). A frequency plot was used to detail mutation frequencies for each gene (Fig.  2 C), coupled with boxplots that outlined the expression levels of these genes (Fig.  2 D). This approach shed light on the complex mutation landscape of necroptotic anoikis regulatory genes in LUAD, laying the groundwork for subsequent investigations into their functional roles and therapeutic implications.

Construction of necroptotic anoikis genes-related molecular subclusters

Using the ConsensusClusterPlus package, Consistent Clustering Analysis (CCA) was conducted using a set of necroptotic anoikis-related genes identified in lung adenocarcinoma (LUAD). On the basis of gene expression levels, LUAD patients were grouped into distinct groups. Differential gene expression analysis further delineated two molecular subclusters within the cohort, centered around key necroptotic anoikis genes (Supplementary Fig.  3 A). These subclusters exhibited unique expression profiles, as evidenced in the heatmap, with significant gene expression differences outlined (Supplementary Fig.  3 B).

Principal Component Analysis (PCA) was employed to discern specific necroptotic anoikis-related patterns, leading to the stratification of patients into two clusters (Supplementary Fig.  3 C,D). The determination of the optimal number of subclusters was guided by a delta area plot (Supplementary Fig.  3 E). Survival analysis indicated that patients in cluster A had a more favorable outcome compared to those in cluster B, underscoring the potential prognostic significance of these gene signatures (Supplementary Fig.  3 F).

Identification of DEGs in clusters and function enrichment analysis

In the analysis presented in Supplementary Fig.  4 A, GSVA enrichment highlighted distinct biological functions in two identified subgroups. Cluster A predominantly featured pathways related to vascular smooth muscle activity and calcium signal transduction, whereas Cluster B was characterized by enrichment in cellular proliferation processes, including cell cycle regulation, DNA replication, and RNA degradation. This analysis also identified 510 genes that exhibited differential expression between Clusters A and B. Supplementary Fig.  4 B further illustrates that these differentially expressed genes are integral to critical tumor-related processes such as division of nuclear material, segregation of chromosomes, and specifically, the segregation within nuclear chromosomes.

Additionally, as shown in Supplementary Fig.  4 C, KEGG pathway analysis underscored a significant enrichment of pathways related to oncogenesis and immune response in Cluster B, highlighting mechanisms involved in cell cycle progression, DNA replication, and the pathogenic impact of Human T-cell leukemia virus 1.

Development and immune analysis of the necroptotic anoikis signature

Analysis of patient survival related to the three genes demonstrated a notable association: elevated expression of these genes corresponded with lower overall survival rates, as depicted in Fig.  3 A–C. Figure  3 D presents heatmaps illustrating variances in gene expression profiles and associated biological pathways, particularly those involving DNA replication and repair processes. Further, Fig.  3 E delineates the disparity in immune cell infiltration among different types of immune cells between groups with high and low gene expression. This suggests that divergent immune evasion strategies might contribute to the observed variations in infiltration levels of resting NK cells and M0 macrophages. Moreover, a scatter plot analysis (Fig.  3 F) demonstrated a correlation between gene expression and biomarker prevalence, highlighting the complex interplay between these genes and tumor biology. Figure  3 G further detailed the correlation strengths between the genes and key biological processes, accentuating the multifaceted nature of their influence on cellular functions. These mentioned results revealed the association between immune environment and both the necroptotic anoikis signature.

Novel signature model genes and their prognostic and immunological correlations in lung adenocarcinoma. ( A – C ) Kaplan–Meier survival curves for lung adenocarcinoma patients stratified by expression levels of the novel signature model genes FAM83A, HMMR, and KRT6A, showing a significant correlation between high gene expression and reduced survival probability (p < 0.001). ( D ) Heatmap detailing the expression patterns of the novel signature model genes alongside their interaction with critical biological pathways such as cell cycle regulation and p53 signaling, highlighting potential mechanisms influencing tumor behavior. The visualizations were crafted using the R packages ggplot2 for general plotting and ComplexHeatmap, which provides capabilities for more advanced visual representations. The analysis and visualization were performed in RStudio (version 2023), which can be downloaded from https://posit.co/download/rstudio-desktop/ . ( E ) Correlation heatmap displaying the interactions between the novel signature genes and various immune cell infiltrates within the tumor microenvironment, including T cells, B cells, and myeloid cell populations, suggesting their potential influence on immune evasion and response. The visualizations were crafted using the R packages ggplot2 for general plotting and CIBERSORT, which provides capabilities for more advanced visual representations. The analysis and visualization were performed in RStudio (version 2023), which can be downloaded from https://posit.co/download/rstudio-desktop/ . . ( F ) Scatter plot analysis indicating the correlation between HMMR expression and the gene signatures of immune cells, with the trend line providing a visual representation of the predictive relationship. ( G ) Summary bar graph illustrating the degree of correlation between the signature genes and key oncogenic pathways, with the color gradient representing the strength of the correlation from negative (blue) to positive (red).

Our next step was to use multivariate Cox regression analysis and LASSO regression analysis to classify LUAD patients into low- and high-risk groups based on their overall survival (OS). This approach involved selecting three genes for the construction of the prognostic signature. The stratification results for the two groups are presented in Supplementary Fig.  5 A,B. Our model showed excellent stratification capability in training cohort, internal cohort and external cohort (respectively Supplementary Fig.  5 C–K). The K-M curves in Supplementary Fig.  5 L,M further demonstrated the model divided patients into two distinct groups statistically significantly (p < 0.05). Additionally, as illustrated in Supplementary Fig.  5 N, our model effectively differentiated patient subgroups according to survival probability, underscoring its potential utility in predicting clinical outcomes and guiding precision medicine strategies.

In the context of lung adenocarcinoma (LUAD), clinical characteristics are pivotal in prognostic determination. To evaluate the prognostic significance of a developed risk score, multivariable Cox regression analyses were utilized. Forest plots (Fig.  4 A,B) illustrated the hazard ratios associated with various clinical factors, such as age, gender, tumor stage, and the calculated risk score. The risk score emerged as a significant prognostic indicator (p < 0.001), with higher scores correlating with increased hazard ratios. With these results, we constructed a predictive nomogram as shown in Fig.  4 D.

Multifaceted prognostic evaluation using novel signature model in lung adenocarcinoma. ( A ) Forest plot displaying the univariate Cox proportional hazards analysis, indicating hazard ratios for age, gender, tumor stage, and risk score, with the risk score showing a significant association with patient outcomes (p < 0.001). ( B ) Forest plot from multivariate Cox analysis, confirming the independent prognostic value of tumor stage and risk score in lung adenocarcinoma after adjusting for other clinical factors. ( C ) Receiver operating characteristic (ROC) curves illustrating the discriminative performance of the novel signature model at 1, 3, and 5 years, with areas under the curve (AUCs) demonstrating the model’s predictive accuracy. ( D ) A nomogram integrating clinical variables and the novel risk score, offering a quantitative tool for predicting the probability of survival at specified time points. ( E ) Calibration plots comparing the nomogram-predicted overall survival (OS) with actual observed OS at 1, 3, and 5 years, assessing the predictive accuracy of the nomogram. ( F ) Box plots of nomogram components, scoring individual clinical parameters and risk score, facilitating personalized risk assessment. ( G ) The Kaplan–Meier curve stratifies patients into high- and low-risk groups based on the median risk score derived from the novel signature model, with significant differences in survival outcomes.

ROC curve analyses validated the constructed nomogram, yielding areas under the curve (AUCs) of 0.720 after 1 year, 0.733 after 3 years, and 0.669 after 5 years (Fig.  4 C). Clearly, our nomogram performed well in terms of prediction.

Overall survival for 1, 3, and 5 years was concordant with nomogram-predicted OS (Fig.  4 E). Using Kaplan–Meier survival curves, patients are classified as high-risk or low-risk according to their median risk score. The prognostic relevance of the nomogram was confirmed by the fact that LUAD patients in the high-risk group had significantly lower overall survival than those in the low-risk group (Fig.  4 F). These findings indicate that the nomogram can be applied to precision treatment strategies for patients with LUAD, emphasizing its predictive capability.

GSVA, GSEA and ssGSEA of novel signature

In the gene set variation analysis (GSVA) and gene set enrichment analysis (GSEA) of a novel gene signature in lung adenocarcinoma (LUAD), distinct expression patterns related to immune cell fractions were observed (Fig.  5 A). The heatmap in Fig.  5 A delineates differential expression profiles across samples, with red indicating upregulation and blue denoting downregulation within the context of identified pathways.

Integrated analysis of the necrotic anoikis-related risk signature in lung adenocarcinoma (LUAD). ( A ) Heatmap displaying gene expression profiles across LUAD samples, clustered by low and high necrotic anoikis-related risk scores. The expression levels are color-coded, with red representing upregulation and blue representing downregulation. The side bar indicates the risk category of each sample. The visualizations were crafted using the R packages ggplot2 for general plotting and ComplexHeatmap, which provides capabilities for more advanced visual representations. The analysis and visualization were performed in RStudio (version 2023), which can be downloaded from https://posit.co/download/rstudio-desktop/ . ( B ) Gene set enrichment analysis (GSEA) for low and high-risk groups. The top plot shows the enriched pathways in the low-risk group, while the bottom plot displays those in the high-risk group, with the normalized enrichment score (NES) plotted along the x-axis. ( C ) Box plots comparing the infiltration scores of various immune cells between low and high-risk groups, demonstrating significant differences in the immune landscape associated with the risk categories. ( D ) Comparison of selected immune-related functional activities between low and high-risk groups, highlighting differences in checkpoint regulation, cytotoxic activity, and other immune functions. ( E ) Box plots detailing the expression of immune checkpoint genes, comparing low and high-risk groups, indicating potential implications for immunotherapy responsiveness. ( F ) Box plots represent the sensitivity to chemotherapy agents, comparing the gene expression variability and its association with treatment response in low versus high-risk LUAD groups, indicating the potential for personalized therapy based on the risk signature.

Enrichment plots for both low and high-risk groups are presented in Fig.  5 B, demonstrating distinct pathway activations in these cohorts. Notably, in our low-risk group, identified pathways including the GNRH signaling pathway, whereas the high-risk group showed enrichment in pathways such as cell cycle and DNA replication. Additionally, the analysis of enrichment scores across immune-related gene sets revealed significant disparities between low and high-risk groups (Fig.  5 C,D), with the latter showing enhanced activation of key oncogenic pathways.

The proportion of immune cells is positively correlated with a lower risk score, according to statistical analysis. Based on single-sample GSEA (ssGSEA), we determined the amount and type of immune cells infiltrating the tumor microenvironment, and we found that low-risk and high-risk groups expressed different levels of checkpoint molecules. Upregulated expression was observed in the low-risk group, hinting at potential benefits from immune therapy (Fig.  5 E). Lastly, for both Gefitinib and Cisplatin, which are important chemotherapy drugs 27 , 28 , it demonstrated a higher sensitivity for low-risk patients, corroborating its potential as a prognostic indicator (Fig.  5 F).

Prediction of immunotherapy response to drugs and PD/PDL1 treatment

A predictive model, incorporating four critical genes, was constructed and subsequently verified across three separate lung adenocarcinoma (LUAD) patient cohorts, encompassing both the IMvigor210 and GSE78220 datasets. This prognostic framework initially segregated LUAD patients into two risk strata: high and low, as demonstrated in Fig.  6 A within the IMvigor210 cohort. A marked distinction in survival likelihood (P < 0.0001) was observed between these risk groups.

Prognostic value of necrotic anoikis-related risk score in patient outcomes and response to therapy in lung adenocarcinoma (LUAD). ( A ) Kaplan–Meier survival curves depicting the difference in survival probability between high and low-risk groups based on the necrotic anoikis-related risk score, with significant separation indicating prognostic relevance (p = 0.00041). ( B ) Box plot illustrating risk scores in relation to patient response to therapy, categorizing complete response/partial response (CR/PR) and progressive disease/stable disease (PD/SD), indicating a higher risk score is associated with poorer response. ( C ) Stacked bar graph showing the proportion of patients with CR/PR versus PD/SD in high and low-risk groups, demonstrating a higher percentage of PD/SD in the high-risk category. ( D ) Kaplan–Meier analysis for a separate patient cohort, confirming the prognostic significance of the risk score (p = 0.0068). ( E ) Another Kaplan–Meier survival curve for an additional patient subset, further validating the risk score’s prognostic impact (p = 0.034). ( F ) Kaplan–Meier curve depicting long-term survival probability, reinforcing the risk score’s predictive capacity for patient outcomes (p < 0.0001). ( G ) Proportions of PD versus CR/PR in high and low-risk groups from another patient cohort, highlighting a greater tendency for PD in the high-risk group.

Within the IMvigor210 cohort, diverse therapeutic responses to PD-1/PD-L1 checkpoint inhibition were recorded, ranging from complete responses (CR) and partial responses (PR) to stable disease (SD) and progressive disease (PD). Patients exhibiting PD/SD presented higher risk scores compared to those with CR/PR, as shown in Fig.  6 B. The incidence of SD/PD was notably higher in the high-risk group, and this group was also linked with significantly poorer outcomes, as indicated in Fig.  6 C.

Consistent validation of the model was seen across additional cohorts, including the IMvigor210 dataset. Figure  6 D specifically represents the responses of stage I + II patients within the IMvigor210 cohort to PD-1/PD-L1 inhibition therapy. Figure  6 D demonstrates consistent validation of the predictive model in this subgroup, with all p-values being less than 0.05. Similarly, Fig.  6 E represents the responses of stage III + IV patients within the IMvigor210 cohort, reinforcing the model's credibility for this stage.

Moreover, Fig.  6 F furnishes insights into the therapeutic responses of patients within the GSE78220 cohort, specifically delineating the responses of stage III + IV patients to PD-1/PD-L1 inhibition therapy within the GSE78220 dataset. Figure  6 F underscores the dependability of the model and its extension of applicability to a distinct dataset. Furthermore, Fig.  6 G elucidates the proportions of PD versus CR/PR in high and low-risk groups from another patient cohort, accentuating a pronounced predilection for PD in the high-risk group.

Validation of novel genes expression levels in mRNA and protein levels in cell lines

To ascertain the expression levels of these three candidate genes in lung adenocarcinoma, we conducted a meticulous quantitative PCR (qPCR) analysis on a range of cell lines. This qRT-PCR analysis (Fig.  7 A,B) revealed a distinct expression profile in lung adenocarcinoma cells, showcasing variations in gene expression when compared to normal pulmonary cells. These differences in expression patterns may be indicative of the genes' roles in the oncogenic processes of lung adenocarcinoma.

External validation. ( A ) The mRNA level (relative to GAPDH) of KRT6A, HMMR, and FAM83A in lung adenocarcinoma cells compared to normal pulmonary cells. (P < 0.05). ( B ) The protein expression of KRT6A, HMMR, and FAM83A in lung adenocarcinoma cells compared to normal pulmonary cells.

Complementing the qPCR analysis, Western blot analysis in Fig.  7 C,D was employed to evaluate the protein expression levels of the genes KRT6A, HMMR, and FAM83A. This protein analysis provided a deeper insight into the cellular mechanisms at play, revealing that the protein expression of these genes in lung adenocarcinoma cells was notably different from that in normal lung cells. Such variations in protein expression levels could reflect the functional implications of these genes in the pathophysiology and progression of lung adenocarcinoma. This dual approach of qRT-PCR and Western blot analysis offers a comprehensive understanding of both the transcriptional and translational modifications associated with these genes in the context of lung cancer.

Pan cancer of model genes and risk signature

To broaden the scope of our research and validate the widespread applicability of our model, an expanded investigation was conducted to evaluate its validation and predictive performance in a pan-cancer cohort. Figure  8 A,B showcase the mutations of KRT6A, HMMR, and FAM83A across 28 different cancer types, detailing the specific mutation types. These findings suggest that the impact of the identified genes extends beyond lung adenocarcinoma (LUAD), influencing a wide array of cancer types. The mutation frequency heatmap in Fig.  8 C further corroborates the mutations of these three genes across the pan-cancer spectrum.

Pan-cancer analysis of necrotic anoikis-related model genes KRT6A, HMMR, and FAM83A. ( A ) OncoPrint visualization demonstrating the alterations of KRT6A, HMMR, and FAM83A across 457 pan-cancer samples, with a summary stack bar indicating the percentage of samples affected by mutations in each gene. ( B ) Horizontal bar graph depicting the distribution and variance of mutation types for each gene across different cancer types, providing a mutation landscape across the pan-cancer spectrum. ( C ) Heatmap showing the mutation frequency of each model gene in different cancer types, with the intensity of red correlating to higher mutation frequencies, offering a clear visual of gene-specific mutation prevalence. The visualizations were crafted using the R packages ggplot2 for general plotting and ComplexHeatmap, which provides capabilities for more advanced visual representations. The analysis and visualization were performed in RStudio (version 2023), which can be downloaded from https://posit.co/download/rstudio-desktop/ . ( D ) Violin plots representing the signature scores (derived from Cox regression analysis) for each cancer type, with the colors indicating different clinical outcomes such as disease-specific survival (DSS), overall survival (OS), and progression-free interval (PFI). Below, a bubble chart illustrates the hazard ratios (HR), with color coding denoting risk (risky or protective) and size corresponding to the significance level (− log10 p-value).

The analysis indicates that the distribution of the necroptotic anoikis-related RiskScore varies across cancer types, further highlighting the unique characteristics of our lung cancer-derived gene signature. Additionally, the association of this signature with key outcomes such as disease-specific survival, overall survival, and progression-free intervals further underscores its potential relevance and applicability across various oncological contexts. Notably, while this signature was initially derived from lung cancer-specific genes, its effectiveness across different cancers underlines its broad relevance, reinforcing the value of our initial findings. This supports the initial hypothesis of the model's credibility and applicability, as evidenced in Fig.  8 D.

In this study, we elucidated the relationship between lung adenocarcinoma (LUAD) and two forms of programmed cell death: necroptotic anoikis. Given the rapidly progressive development of LUAD, it has been difficult to improve the patients’ prognosis through singular targeted therapies. Through revealing necroptotic anoikis-related gene expressions, we identified three key genes significantly correlated with LUAD outcomes, by which, we developed a predictive model, providing important tools for risk assessment and validation for patients. The model not only aids in understanding disease progression but also provides guidance for personalized therapeutic strategies.

Among malignant tumors, lung adenocarcinoma has the highest mortality and morbidity rates in China and even in world 29 . Given the limited efficacy associated with standard treatments, there is a pressing need for the advancement of immunotherapeutic approaches in the treatment of LUAD. As the most common type of lung cancer 30 , there is a critical need to investigate molecular markers related to diagnosis and prognosis in LUAD patients. Related to necroptosis, which played an important role in tumorigenesis and metastasis 31 , the evasion of anoikis been identified as a significant factor facilitating tumor invasion and progression 32 . Therefore, the signature of necroptotic anoikis-related genes were identified to affect outcomes and guide for precision treatment in LUAD, especially for immunotherapy.

The aim of this study was to evaluate the relevance of necroptotic anoikis-related genes in LUAD by analyzing both single-cell and bulk RNA sequencing data. To further analyze the pool of individual cells, we identified and isolated cancer-associated fibroblasts (CAFs) after RNA-seq quality control and normalization procedures.

Cancer-associated fibroblasts (CAFs), a predominant cell type within the tumor microenvironment (TME), have gained considerable attention due to their multifaceted roles in tumor progression and therapeutic responses. These fibroblasts not only influence the TME through inflammation modulation, extracellular matrix (ECM) remodeling, and immune cell interactions but also through processes such as angiogenesis and immune evasion. CAFs can exhibit both pro-tumorigenic activities, including T-cell exclusion and enhanced cancer cell survival, and anti-tumorigenic effects, such as the production of a dense collagen matrix that may inhibit tumor growth and metastasis. Interestingly, certain CAF subtypes are associated with improved therapy outcomes, highlighting their potential as therapeutic targets. However, clinical trials broadly targeting the tumor stroma have yet to achieve significant success, suggesting the complexity of CAF functions. This complexity is further illustrated by single-cell RNA sequencing (scRNA-seq) studies that have unveiled significant heterogeneity among CAFs in various cancer contexts, including breast cancer, pancreatic ductal adenocarcinoma, and lung cancer. This heterogeneity and the dual functional roles of CAFs emphasize the need for refined therapeutic strategies that precisely target these fibroblasts within the TME.

To explore the mechanisms of the necroptotic anoikis-based molecular subtype, we employed a consensus clustering approach, which enabled the classification of the LUAD patients into two distinct subtypes, stratified according to their expression profiles of necroptotic anoikis-related genes. Patients classified in Cluster A exhibited a more favorable prognosis compared to those in Cluster B. By analyzing gene set variation (GSVA), the functional differences between the two clusters were uncovered, with cluster A showing a notable enrichment in immune pathways, including arachidonic acid metabolism which might be an apoptotic signal that regulates programmed cell death processes 33 . The observed differences between the two clusters further revealed a significant association between necroptotic anoikis and immune environments in LUAD.

The necroptotic anoikis-related-related gene signature was further defined through univariate Cox and LASSO Cox regressions using HMMR, FAM83A, and KRT6A. The AUC values of this signature were 0.720, 0.733, and 0.669 respectively across all cohorts. Based on these gene signatures, and clinical parameters, we developed a nomogram that provides a comprehensive forecast of patient outcomes. The calibration curve in our study confirms that the nomogram is clinically robust in its prognosis. Differentially expressed genes between the two subtypes were identified as 510 in total. These DEGs significantly enriched in pathways related to immune response and tumor growth based on GO and KEGG analyses.

The tumor microenvironment (TME) in lung adenocarcinoma constitutes a complex interplay of immune cells, stromal cells, and tumor cells 34 , each crucial in modulating tumor progression and influencing clinical outcomes. Solid tumors with higher ratios of CD163+ macrophages, non-classical monocytes, and intermediate monocytes have poorer survival rates, while solid tumors with an increased proportion of mast cells have a prolonged survival rate 35 . Additionally, a greater number of B cells is significantly associated with better overall survival 36 . Through our comprehensive analysis between the necroptotic anoikis-related signature and immune environment for LUAD, the patients in low-risk group demonstrated higher expression thus could receive a better outcome for standard chemotherapy and specially for immunotherapy like PD-1/PD-L1 blockade.

Subsequently, our model demonstrated robustness in stratifying LUAD patients into low- and high-risk groups for the overall survival (OS) across three independent cohorts. Notably, our analysis indicates that patients with poorer outcomes exhibit higher RiskScores, which also correlate with reduced sensitivity to PD-1/PD-L1 blockade. High-risk groups identified by our model consistently presented with worse prognoses. The results showed the potential utility of our model in guiding immunotherapy for lung adenocarcinoma.

Apart from analysis on single-cell sequencing results, we demonstrated that the role of these three genes utilizing RT-qPCR and Western blotting. The cell experiments further confirmed the value of our neoteric anoikis-related signature.

Finally, a comprehensive pan-cancer analysis in TCGA cohort was performed to conduct the expression and mutational landscape of HMMR, FAM83A, and KRT6A, aiming to understand their roles across diverse cancer types. Our study found that not only in LUAD patients, but the mutations of these genes were also observed in various tumors, which has been reported in previous studies 37 , 38 , 39 . These fundings mentioned still require to be proved in the future.

HMMR, also known as CD168 and located on chromosome 5, plays a multifaceted role in cancer progression. It is involved in cell cycle regulation, promotes macrophage polarization, and facilitates epithelial-to-mesenchymal transition 40 . Moreover, HMMR's interaction with low molecular weight hyaluronic acid (HA) fragments notably enhances immune cell recruitment and exacerbates patient prognosis by activating tumor microenvironment dynamics and influencing pathways such as CD44 expression 41 . FAM83A, found on chromosome 8q24 and overexpressed in various cancers, impacts lung adenocarcinoma through the Wnt/β-catenin signaling pathway and is linked to PD-L1 expression, affecting immune responses in cancer therapy 42 . Additionally, FAM83A’s expression is further promoted by the antisense RNA FAM83A-AS1, enhancing lung cancer cell growth 43 . KRT6A, a type II keratin involved in the epidermalization of squamous epithelium, plays a critical role in cell migration and cancer metastasis. Its elevated levels in lung adenocarcinoma are associated with poor prognosis, primarily through mechanisms that promote the epithelial–mesenchymal transition 44 .

While our findings contribute valuable insights, it’s important to acknowledge the limitations of this study, which include: Firstly, all of our data were gathered from the GEO database, which may impact the comprehensiveness of the proposed model as well as the clarity of potential mechanisms. Secondly, the relatively small sample size of lung adenocarcinoma cases in the GEO database may have affected the statistical significance of some findings. Lastly, further experimental research and clinical research are required to verify our conclusions.

In the context of personalized lung cancer treatment, the riskScore we've introduced becomes paramount. By categorizing patients based on this score, we can precisely stratify patients into different risk groups, each potentially requiring a distinct therapeutic approach. For instance, individuals with higher riskScores may require more aggressive treatment modalities or novel agents that specifically target genes identified in our study. On the other hand, patients with lower riskScores might derive greater benefit from immunotherapies, particularly due to the increased expression of immune checkpoint molecules. This kind of precision in patient stratification ensures that treatment modalities are not ‘one-size-fits-all’, but rather tailored to each patient's unique genomic profile. As the medical community advances toward more individualized care, such riskScore analyses will be pivotal in optimizing therapeutic strategies for lung cancer patients.

We have developed a novel prognostic signature with remarkable predictive accuracy for lung adenocarcinoma (LUAD) prognosis. As a result of this study, necroptotic anoikis is highlighted as an important component of LUAD pathogenesis and provided insight into clinical decision-making and therapeutic strategies for lung adenocarcinoma management.

Data availability

Data generated or used in this study are available from the corresponding authors upon reasonable request.

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Acknowledgements

We wish to appreciate all the authors who have participated in this Research Topic and to the reviewers for their hard work and insightful comments.

This study was supported by the Natural Science Foundation of Shandong Province (ZR2021MH402).

Author information

These authors contributed equally: Ping Sui and Xueping Liu.

Authors and Affiliations

Department of Oncology, The Affiliated Yantai Yuhuangding Hospital of Qingdao University, Yantai, 264000, Shandong, China

Institute of Transfusion Medicine and Immunology, Mannheim Institute for Innate Immunoscience (MI3), Medical Faculty Mannheim, University of Heidelberg, Heidelberg, Germany

Department of Pulmonary and Critical Care Medicine, The Affiliated Yantai Yuhuangding Hospital of Qingdao University, Yantai, 264000, Shandong, China

Xueping Liu

Jiangmen Hospital of Traditional Chinese Medicine Affiliated to Jinan University, Jiangmen, 52900, China

Cheng Zhong

Department of Anesthesiology, Shandong Provincial Third Hospital, Jinan, 250031, China

Zhanming Sha

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Conception and design: Ping Sui and Cheng Zhong. Acquisition of data: Xueping Liu, Ping Sui. Analysis and curation of data: Ping Sui, Xueping Liu. Drafting the article: Ping Sui, Xueping Liu. Critically revising the article: all authors. Study supervision: Zhanming Sha. All authors read and approved the final manuscript.

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Sui, P., Liu, X., Zhong, C. et al. Integrated single-cell and bulk RNA-Seq analysis enhances prognostic accuracy of PD-1/PD-L1 immunotherapy response in lung adenocarcinoma through necroptotic anoikis gene signatures. Sci Rep 14 , 10873 (2024). https://doi.org/10.1038/s41598-024-61629-8

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An examination of the relationship between height, height dissatisfaction, drive for thinness and muscularity, and eating disorder symptoms in north American women

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• Daniel Talbot   ORCID: orcid.org/0000-0002-5505-4474 1 , 2 &
• Justin Mahlberg 3 , 4  

Recent research has evidenced the importance of height dissatisfaction in male body image, however the impact of height on body image in women remains relatively unexplored. Our study aimed to investigate the association between height, heightdissatisfaction, body dissatisfaction, and eating disorder symptoms in a sample of 139 women from the USA. Participants were recruited using Amazon's MTurk and reported their actual and ideal height, as well as completing measures of height dissatisfaction, and drive for thinness, drive for muscularity, and eating disorder symptoms. A paired sample t-test was utilised to examine differences in participants’ actual and ideal height. Additionally, linear hierarchical multiple regression was used to assess whether height, eating disorder symptoms, drive for thinness, and drive for muscularity uniquely predicted height dissatisfaction, and significant interactions were explored using a simple slope analysis complemented with a Johnson-Neyman analysis. Results showed that 48.92% of women reported identical actual and ideal height, 33.09% of women wanted to be taller, and 13.67% wanted to be shorter than their actual height. Additionally, shorter women tended to report greater height dissatisfaction, and higher levels of drive for thinness and drive for muscularity were associated with increased height dissatisfaction. However, eating disorder symptoms did not uniquely account for significant variance in height dissatisfaction once accounting for drive for thinness and muscularity. Our exploratory analysis also revealed that for taller than average women, height dissatisfaction was more strongly predicted by drive for muscularity, thus implicating the significance of height and muscle dissatisfaction for taller women. Overall, our study demonstrated that height and height dissatisfaction are important components to the theoretical construct of women’s body image, and therefore should be integrated into theoretical models of female body dissatisfaction and considered in assessment, formulation, and treatment of body image-related disorders. Further research with larger and more diverse samples, including clinical populations, is warranted to validate and extend our findings.

Avoid common mistakes on your manuscript.

Body dissatisfaction is a pervasive concern across cultures and ages and can manifest in a variety of ways, including concerns about body shape, weight, and size (Grogan, 2021 ). For women, body image research has historically focused on the desire for thinness (Fitzsimmons-Craft et al., 2012 ; Prnjak et al., 2020 ), with more recent research highlighting the importance of muscular size, shape, and tone (Bozsik et al., 2018 ; Cunningham et al., 2019 ). Commonly, both women and men internalise ideal and often unattainable bodies comprised of low body fat and notable muscle shape and tone - bodies in line with societal emphasis on thinness and muscularity as most acceptable and attractive (Talbot & Mahlberg, 2022 ). Idealised bodies are typically challenging for women to obtain and maintain, and body dissatisfaction follows from the perception that one’s body does not adhere to these internalised ideals. Notably, body dissatisfaction has been linked to several negative outcomes such as the development of eating disorders (Rosewall et al., 2018 ), as well as negative affect and stress (Barnes et al., 2020 ; Bornioli et al., 2021 ) and poorer quality of life (Santhira Shagar et al., 2021 ).

Height is largely unchangeable and cannot be altered through diet or exercise, and as such, may have a unique influence on body image and dissatisfaction. Studies in undergraduate (Talbot & Mahlberg, 2023 ) and sexual minority men (Griffiths et al., 2017 ), as well as men recruited from an internet forum for short-statured individuals (O’Gorman et al., 2019 ) have demonstrated positive associations between height dissatisfaction and muscle dissatisfaction, drive for muscularity, and eating disorder symptoms, and negative associations with quality of life. Actual height has been shown to negatively correlate with height dissatisfaction (Griffiths et al., 2017 ; O’Gorman et al., 2019 ; Talbot & Mahlberg, 2023 ), as well as conformity to masculine norms – a factor that has been heavily implicated in male body dissatisfaction (Gattario et al., 2015 ). Shorter stature has also been linked to other factors related to body dissatisfaction, such as loneliness (Mo & Bai, 2022 ) and narcissism (Kozłowska et al., 2023 ).

Whilst there is emerging research on height dissatisfaction in men, less is known about the impact of height on body image in women. Height, or women’s perception of their height, may impact their body image in a variety of ways. For example, women who are shorter in stature may experience greater dissatisfaction with their bodies, as height is often associated with social desirability and attractiveness (Swami et al., 2008 ). Indeed, Perkins et al. ( 2021 ) found that shorter Australian women were more dissatisfied with their height, felt that they were treated poorly due to their height, and reported poorer quality of life, compared to taller women.

On the other hand, taller women may also experience body dissatisfaction due to a perceived lack of femininity. There is a cultural stereotype that associates femininity with petite or delicate features such as a small frame or delicate bone structure, which may conflict with the typically larger stature of taller women. Taller women may also feel that they fall outside of others’ height preferences, as prior research has established that a dyad consisting of a taller man and shorter woman is the most common preference for heterosexual romantic relationships (Salska et al., 2008 ; Yancey & Emerson, 2016 ).

In both cases, dissatisfaction with height may lead to attention towards and behaviours targeting other more changeable dimensions of body image: body fat and muscularity. If one believes that their height, whether too short or too tall, creates a deficit in attractiveness, then they may be driven to engage with compensatory behaviours that reduce their level of body fat and increase their muscle tone or athleticism and thus move closer toward the commonly reported ideal female body (Bozsik et al., 2018 ; Prnjak et al., 2020 ). In extreme cases, this could lead to behaviours like food restriction, purging, and compulsive exercise. Indeed, Favaro et al. ( 2007 ) demonstrated a link between height and eating disorder psychopathology, finding that shorter stature was associated with an increased risk of having an eating disorder.

It is also reasonable to consider that the relationship between height dissatisfaction and body dissatisfaction may be bidirectional. Women who experience significant body dissatisfaction are rarely focused on a single aspect or dimension of their body, as indicated through studies showing close associations between body fat dissatisfaction, muscle dissatisfaction, and other eating disorder symptoms (Cunningham et al., 2022 ; Prnjak et al., 2022 ), and high comorbidity between body dysmorphic disorder - a disorder in which an individual focuses on a perceived deficit in their physical appearance, and eating disorders (Dingemans et al., 2012 ; Ruffolo et al., 2006 ). It follows that dissatisfaction with one aspect of one’s body (e.g., body fat or muscularity) may negatively impact their perception of another (e.g., height), or inform a general belief that every aspect of one’s body is unattractive or unacceptable. It is also important to consider the potential perceived impact of height on body proportion. For instance, women wanting to be thinner might see greater height as an avenue to redefining their distribution of body fat or body shape generally. Conversely, greater height might be perceived as an important aspect of achieving strength and power in women with a high drive for muscularity.

Present study

Given the relationship between height dissatisfaction and body dissatisfaction in men and the negative impact of body dissatisfaction on women, we aimed to explore the association between height, height dissatisfaction, and body dissatisfaction-related constructs in women from the USA. Based on previous studies in men we hypothesised that there would be a significant difference between women’s actual height and their desired (ideal) height. Additionally, we hypothesised that women’s height, as well as indices of body dissatisfaction including drive for thinness and muscularity, and eating disorder symptoms, would be significantly associated with height dissatisfaction. Finally, we explored the possibility that a person’s height interacted with the relationship between these predictors, to get a preliminary understanding of whether women of different height are similarly vulnerable to the relationship between height dissatisfaction and eating disorder symptoms and body dissatisfaction drives.

Materials and methods

Participants and procedure.

Our study utilised data from a larger project aimed at understanding the role of body image and personality traits. The sample for the present study comprised of 139 women from the USA recruited from Amazon’s MTurk. See Table  1 for demographic characteristics of the sample. Informed consent was obtained prior to undertaking the study. Ethics approval for the study was granted by an institutional Human Ethics Research Board at an Australian University (ID: 2021–010 S).

Participants reported their actual and ideal height and completed the Male Body Attitude Scale-Height subscale (Tylka et al., 2005 ). We selected this measure as no measure of height dissatisfaction for women exists, and the two items that comprise this scale are non-sex-specific. Of note, this scale has been used to measure height dissatisfaction in women in a previous study (Perkins et al., 2021 ). Participants endorse their height dissatisfaction on a 6-point scale (1 =  never ; 6 = always), and items were averaged to create an index of height dissatisfaction with higher scores indicating greater dissatisfaction. To measure drive for thinness we used the Drive for Thinness Scale (Garner & Van Strien, 2004 ). Participants reported their drive for thinness on a 6-point scale (1 =  never ; 6 =  always ), and items were averaged to an create index of drive for thinness with higher scores indicating a greater drive for thinness. Analogously, drive for muscularity was measured with the Drive for Muscularity Scale (McCreary et al., 2004 ). Items were averaged to create an index of drive for muscularity, with higher scores indicating a greater drive for muscularity.

To measure eating disorder symptoms, we used the Eating Disorder Examination Questionnaire–Short (Gideon et al., 2016 ). Participants indicated their frequency of symptoms per week (0 =  0 days ; 3 =  6–7 days ), and items were averaged to create an index of eating disorder psychopathology with higher scores indicating more eating disorder symptoms.

Statistical analysis

There were six participants with missing data for ideal height. We retained these participants in all analyses. To address our first hypothesis, we utilized a paired sample t- test to examine whether there was a significant difference between participant’s actual and ideal height. To address our second hypothesis, a linear hierarchical multiple regression was used to assess whether height, eating disorder symptoms, drive for thinness, and drive for muscularity uniquely predicted height dissatisfaction. All variables were grand mean centered with gcenter from the EMAtools package to reduce multicollinearity and improve interpretation of the model output. Model assumptions were checked by inspecting the full model using the check_model function from the performance package. Results indicated that assumptions of normality, linearity, and homogeneity of variance of the regression residuals, and multicollinearity of the predictors were all met (see Figure S1 in supplementary materials). In the first step of the hierarchical multiple regression, participants’ height and eating disorder symptom scores were entered as predictors. Drive for thinness scores were entered into the model in the second step, and drive for muscularity scores were entered into the model in the third step. In step four, interaction terms between height and eating disorder symptoms, drive for thinness, and drive for masculinity, respectively, were entered into the model to assess whether height moderated the effects of the predictors on height dissatisfaction.

We entered eating disorders symptoms at step 1 to examine its relationship with height dissatisfaction after controlling for the covariate of actual height. We then entered drive for thinness and drive for muscularity at steps two and three, respectively. The goal for entering these at separate steps was to assess for how each motive contributed to the variance in height dissatisfaction after accounting for eating disorder symptoms. Finally, we entered the interactions with actual height at step 4 - after accounting for the variance in height dissatisfaction already explained by each predictor - to test whether the predictors explanatory power varied at different levels of height. We explored significant interactions using a simple slope analysis complemented with a Johnson-Neyman analysis (Bauer & Curran, 2005 ) using sim_slopes from the interactions package. The simple slopes analysis involved examining whether the relationship between height dissatisfaction and each drive at higher and lower actual heights (+/- 1 standard deviation of height) was significant. The Johnson-Neyman technique complements this assessment by estimating the threshold(s) of actual height where each relationship becomes significant.

Hypothesis 1: Actual and ideal height in the sample

As seen in Table  1 , women on average reported a significantly greater ideal compared to actual height, t (132) = -2.87, p  =.005, d = -0.23.

Hypothesis 2: Height dissatisfaction as a function of eating disorder symptoms and body dissatisfaction motives

Figure  1 shows the simple correlations between the variables of interest. Notably, height dissatisfaction was moderately correlated with height ( r  = −.23), and also showed moderate correlations for drive for thinness ( r  =.30) and muscularity ( r  =.39), and eating disorder symptoms ( r  =.34). Table  2 shows the results from a hierarchical regression model assessing the predictive utility of eating disorder symptoms and drive for thinness and muscularity in explaining variance in height dissatisfaction. Step 1 of the model significantly predicted height dissatisfaction, F(2, 136) = 14.65, p  <.001, and showed that eating disorder symptoms significantly predicted height dissatisfaction after accounting for variance in height dissatisfaction explained by actual height. The addition of drive for thinness in step 2 statistically improved model fit, F (1,135) = 4.33, p  =.04, R 2 change  = 0.02. However, drive for thinness was a non-significant predictor of height dissatisfaction, while eating disorder symptoms remained a unique predictor of height dissatisfaction. Step 3 of the model included the drive for muscularity, which improved model fit, F (1,134) = 8.63, p  =.004, R 2 change  = 0.05. The additional predictor significantly explained variance in height dissatisfaction. Importantly, including drive for muscularity rendered eating disorder symptoms a non-significant predictor, which implied drive for muscularity mediated the relationship between eating disorder symptoms and height dissatisfaction. Step 4 of the model explored the possibility that actual height moderated the relationship between each predictor and height dissatisfaction. The addition of the three interaction terms further improved model fit, F (3,131) = 5.77, p  <.001, R 2 change  = 0.09. Height significantly interacted with both eating disorder symptoms and drive for muscularity. We explored the suggested moderation patterns below.

Correlation matrix depicting the Pearson correlation coefficients. Non-significant correlations ( p above 0.05) are crossed out. The colour indicates the strength of the relationship

Exploratory analyses

As Table  3 illustrates, a simple slopes analysis testing the predictive utility of drive for muscularity at below average (-1 SD ), average, and above average height (+ 1 SD ) revealed that women with average and above average height demonstrated height dissatisfaction that was significantly explained by a drive for muscularity, whereas women with below average height showed no evidence for this relationship. The Johnson-Neyman analysis further clarified this patterns, as drive for muscularity significantly predicts height dissatisfaction ( p  <.001) when actual height is 2.73 centimetres or higher above the mean (i.e. taller than ~ 165 cm; see Fig.  2 ), and this relationship is non-significant below this threshold for of actual height. A similar simple slopes analysis testing the predictive utility of eating disorder symptoms revealed no statistically significant evidence for moderation effects.

Johnson-Neyman plot showing the level range of height (at least 2.73 cm above the mean) where the coefficient for drive for muscularity significantly predicts height dissatisfaction ( p  <.001). Note: Height is mean centered, so that scores above zero indicate heights greater than the group mean and scores lower than zero indicate heights below the group mean

The present study provided one of the first examinations of the relationship between height, height dissatisfaction, body dissatisfaction, and eating disorder symptoms in women. In support of the first hypothesis, women on average wanted to be taller than their actual height. This corresponds to Perkins et al. ( 2021 ) study which found analogous results in an Australian community sample of women. Notably, only a third of our sample (33.09%) reported a desire to be taller than their actual height, whilst around half of our sample (48.92%) reported alignment of their actual and ideal height. These results were somewhat lower than expected, given that Perkins et al. ( 2021 ) reported that 45% of women in their sample wanted to be taller. These results were also lower than those reported in male samples. For instance, Talbot and Mahlberg ( 2023 ) found that 43% of their Australian male sample wished that they were taller.

Results of our regression analyses showed that there was a negative association between height dissatisfaction and reported height, indicating that shorter women tend to report greater height dissatisfaction. The direction of this association aligns with that reported by Perkins et al. ( 2021 ) in Australian women, and studies examining height dissatisfaction in men (Griffiths et al., 2017 ; O’Gorman et al., 2019 ; Talbot & Mahlberg, 2023 ); however it is noteworthy that all studies (other than Talbot & Mahlberg, 2023 , that reported r  = −.21) reported marginally stronger relationships (-0.31 for Perkins et al., 2021 , − 0.41 for O’Gorman et al., and − 0.44 for Griffiths et al., 2017 ) than what we observed in this study. As most prior studies have examined men, this could reflect gender norms whereby one’s height is a more central factor for men with respect to their height dissatisfaction, though this is speculative and needs to be evaluated explicitly in future research.

In support of our second hypothesis, our regression also demonstrated that women’s drive for thinness and muscularity uniquely predicted height dissatisfaction: women who had a high drive for thinness and muscularity, respectively, tended to have greater height dissatisfaction. A potential explanation for this result may lie in the largely unchangeable nature of height. Thus, if women are dissatisfied with a smaller stature, they may seek to enhance other more easily changeable domains of body image such as body fat and muscular shape/tone. In this way, some women may be seeking to compensate for a smaller stature through adapting a drive to be thinner and more toned, and hence more in line with the commonly idealised female body type. Another explanation might lie in how height impacts body proportions. Height itself explained very small amounts of variation in our model compared to eating disorder symptoms and drive for thinness and muscularity, which might suggest that dissatisfaction with height might more closely be driven by how height impacts body proportions. For instance, greater height might function to increase perceptions of thinness or make one’s body look leaner, more toned and athletic – the ‘fit ideal’ female body (Bozsik et al., 2018 ; McComb & Mills, 2022 ). Therefore, the desire to be taller might stem from the drive to be perceived as thinner, or more toned/athletic.

Further, although the effect was small, results of our exploratory analysis revealed that for taller than average women, height dissatisfaction was more strongly predicted by drive for muscularity. Essentially, this suggests that amongst taller women, those who want to be taller also want to be more muscular. Simply put, this might reflect a sub-group of our sample who are already tall but generally want to be bigger through both height and muscularity. This novel observation highlights that it is important for researchers and clinicians to consider height dissatisfaction for individuals with body dissatisfaction concerns regardless of their actual height. Prior research has often focused on the vulnerability of shorter people (Favaro et al., 2007 ; O’Gorman et al., 2019 ; Perkins et al., 2021 ) but our data suggests that taller women may also be vulnerable to height dissatisfaction by way of stronger muscularity concerns.

Eating disorder symptoms did not uniquely account for significant variance in height dissatisfaction once drive for thinness and muscularity were entered into our regression model, despite accounting for 31% of the variance in height dissatisfaction in step 1. Therefore, our results support the association between height dissatisfaction and eating disorder symptoms, yet highlight the important role that body image concerns – specifically, muscularity desires– play in promoting the connection between eating disorder symptoms and height dissatisfaction. Ultimately, our results highlight that body dissatisfaction may underpin women’s height dissatisfaction and its link with eating disorder symptoms. This completements the results of Favaro et al. ( 2007 ), which showed a strong link between women with eating disorders and smaller stature, by suggesting that it will be important for future research to consider body dissatisfaction motives when attempting to understand the link between height and eating disorders in future studies.

Limitations and conclusion

Limitations of the present study are noted. First, we utilised self-report measures which may have limited reliability of results (Haeffel & Howard, 2010 ). Second, our study utilised a W.E.I.R.D sample, thus limiting generalisability of results to non-Western cutlures (Henrich et al., 2010 ). Third, the validity of collecting data through Amazon Mturk has been called into question for eating disorders-related research (Burnette et al., 2022 ), thus results should be taken with caution. Last, the cross-sectional design of our study as well as the moderate sample size and our exploratory approch means that generalisability of results is somewhat limited and results should be interpreted with caution.

Despite these limitations, our study provides one of the first examinations of height, height dissatisfaction, body dissatisfaction, and eating disorder symptoms in women. Ultimately, our study adds to the evidence that height and height dissatisfaction are unique significant contributors to women’s body image, and therefore should be integrated into theoretical models of female body dissatisfaction. It also highlights the need to consider height dissatisfaction in assessment. Indeed, no published scale of body dissatisfaction includes a measure of height dissatisfaction beyond the MBAS, which is designed for men and only includes two items to measure height dissatisfaction. A more thorough and validated assessment of height dissatisfaction could be used as part of assessment for women presenting with body image-related disorders, and if relevant, factored into the thus including formulation and treatment of these disorders.

Future studies should aim to replicate our results with larger and more diverse samples of women, and further investigate this relationship in clinical populations such as women with eating disorders. Future studies should also further consider the curvilinear relationships between height, body dissatisfaction, and eating disorder symptoms (i.e., focusing on body dissatisfaction in both taller and shorter women).

Data availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

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Department of Psychiatry, Western Sydney Local Health District, Wentworthville, NSW, 2145, Australia

Daniel Talbot

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Talbot, D., Mahlberg, J. An examination of the relationship between height, height dissatisfaction, drive for thinness and muscularity, and eating disorder symptoms in north American women. Curr Psychol (2024). https://doi.org/10.1007/s12144-024-06108-z

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DOI : https://doi.org/10.1007/s12144-024-06108-z

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N-acetylcysteine as a treatment for substance use cravings: A meta-analysis

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N-acetylcysteine (NAC) may serve as a novel pharmacotherapy for substance use and substance craving in individuals with substance use disorders (SUDs), possibly through its potential to regulate glutamate. Though prior meta-analyses generally support NACs efficacy in reducing symptoms of craving, individual trials have found mixed results. The aims of the this updated meta-analysis were to (1) examine the efficacy of NAC in treating symptoms of craving in individuals with a SUD and (2) explore subgroup differences, risk of bias, and publication bias across trials. Database searches of PubMed, Cochrane Library, and ClinicalTrials.gov were conducted to identify relevant randomized control trials (RCTs). The meta-analysis consisted of 9 trials which analyzed data from a total of 623 participants. The most targeted substance in the clinical trials was alcohol (3/9; 33.3%), followed by tobacco (2/9; 22.2%) and multiple substances (2/9; 22.2%). Meta-analysis, subgroup analyses, and leave-one-out analyses were conducted to examine treatment effect on craving symptoms and adverse events. Risk of bias assessments, Eggers tests, and funnel plot tests were conducted to examine risk of bias and publication bias. NAC did not significantly outperform placebo in reducing symptoms of craving in the meta-analysis (SMD = 0.189, 95% CI = -0.015 - 0.393). Heterogeneity was very high in the meta-analysis (99.26%), indicating that findings may have been influenced by clinical or methodological differences in the study protocols. Additionally, results indicate that there may be publication bias present. There were no between-group differences in risk of AEs. Overall, our findings are contrary to those of prior meta-analyses, suggesting limited impact of NAC on substance craving. However, the high heterogeneity and presence of publication bias identified warrants cautious interpretation of the meta-analytic outcomes.

Competing Interest Statement

The authors have declared no competing interest.

Funding Statement

This work was supported by funding from the National Institute on Alcohol Abuse and Alcoholism (R01-AA030041) and the Department of Defense (HU0001-22-2-0066).

Author Declarations

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

The meta-analysis used only openly available data that were originally presented by trial authors of each trial included in the meta-analysis. One study (Roten et al., 2013) did not include follow-up data in their publication but provided it upon request. DOIs/ClinicalTrial ID of included trials: Schmaal et al., 2011: https://doi.org/10.1159/000327682 Yoon, 2013: NCT00568087 Roten et al., 2013: https://doi.org/10.1016/j.addbeh.2012.11.003 Back et al., 2016: https://doi.org/10.4088/JCP.15m10239 Schulte et al., 2017: https://doi.org/10.1177/0269881117730660 Back, 2021: NCT02911285 McKetin et al., 2021: https://doi.org/10.1016/j.eclinm.2021.101005 Back, 2023: NCT02966873 Morley et al., 2023: https://doi.org/10.1093/alcalc/agad044

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

Data Availability

The R code used to conduct analyses and create forest and funnel plots can be accessed using this link: https://github.com/ewinterli/NAC-meta-analysis

https://github.com/ewinterli/NAC-meta-analysis

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