Review History


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Summary

  • The initial submission of this article was received on November 11th, 2024 and was peer-reviewed by 3 reviewers and the Academic Editor.
  • The Academic Editor made their initial decision on January 6th, 2025.
  • The first revision was submitted on February 9th, 2025 and was reviewed by 1 reviewer and the Academic Editor.
  • A further revision was submitted on May 3rd, 2025 and was reviewed by 1 reviewer and the Academic Editor.
  • A further revision was submitted on July 18th, 2025 and was reviewed by the Academic Editor.
  • A further revision was submitted on August 27th, 2025 and was reviewed by the Academic Editor.
  • The article was Accepted by the Academic Editor on September 1st, 2025.

Version 0.5 (accepted)

· Sep 1, 2025 · Academic Editor

Accept

Thank you for your detailed responses and thorough revisions addressing all my editorial comments. I have carefully assessed the revised manuscript myself and am satisfied with the current version. I am pleased to recommend acceptance.

[# PeerJ Staff Note - this decision was reviewed and approved by Paula Soares, a PeerJ Section Editor covering this Section #]

Version 0.4

· Jul 29, 2025 · Academic Editor

Major Revisions

Thank you for your detailed point-by-point responses to the comments and for the revisions made to the manuscript and supplementary materials. However, after a careful review of your rebuttal and the revised manuscript, I would like to offer the following suggestions:

1.You have added some clarification regarding batch effect correction, missing value imputation, and dataset selection. However, the description of your data integration and cleaning workflow remains insufficiently detailed. For example, it is still unclear how you handled potential outliers during quality control, how normalization was specifically implemented across datasets, and whether any additional filtering criteria (e.g., for low-quality samples or genes) were applied beyond those described. Additionally, while you mention using “combat” for batch correction and mean imputation for missing values, more justification and potential limitations of these approaches should be discussed, especially given the heterogeneity of public datasets. To improve transparency and reproducibility, I strongly encourage you to provide a stepwise, schematic workflow diagram and to specify all quantitative thresholds applied during data preprocessing, even if this level of detail cannot be fully achieved retrospectively.

2.You have provided some additional information regarding functional assays for KCNQ1 and ITPK1. Nevertheless, the experimental validation remains quite limited in scope. The justification for selecting these two biomarkers is still rather brief and does not fully address the rationale for their prioritization over other candidates. Furthermore, the functional assays performed are relatively basic and do not assess dose-response relationships or broader mechanistic effects, as previously requested. While you state that further functional characterization may be possible in future work, the current data do not provide strong evidence for the clinical relevance of these biomarkers. Please consider discussing these limitations more explicitly in your manuscript and, where possible, provide additional justification for the current experimental design.

3.The inclusion of code on GitHub and additional parameter details in the supplementary materials is a positive step towards reproducibility. However, the manuscript and supplementary files still lack a fully transparent, stepwise account of your cross-validation protocol, model selection criteria, and hyperparameter optimization process. The code available on GitHub (https://github.com/mikelu1997/lasso.code-model) is relatively limited in scope and does not include full documentation or clear annotation of all analytical steps. For full transparency and reproducibility, I recommend that you further expand your codebase to include detailed scripts corresponding to each analytic step described in the manuscript, along with clear instructions for use. If this is not feasible, please explicitly acknowledge the limitations of your code and analysis documentation in the manuscript.

4.Thank you for providing additional technical details regarding antibodies and quantification methods. However, your description of image analysis methods and software remains rather general. For robust reproducibility, please specify the exact image analysis software (including version) and all parameter settings used. Furthermore, please clarify how negative and positive controls were selected and processed, and discuss any potential sources of bias or variability in your immunohistochemical scoring. These details are essential for a thorough assessment of methodological rigor and should be included in the methods section or supplementary materials.

5.You have attempted to expand the discussion regarding the biological and clinical implications of the identified subtypes, as well as broader engagement with the relevant literature. However, the rationale for the number and definition of subtypes remains only partially justified, and the clinical significance of these classifications is not fully explored. Additionally, while you have cited several related studies, the integration of your findings with existing literature remains somewhat superficial, and conflicting findings or limitations are not adequately addressed. Please consider providing a more critical and nuanced discussion of the strengths and limitations of your subtype classification, including potential confounding factors and the implications for clinical translation.

Version 0.3

· May 22, 2025 · Academic Editor

Major Revisions

Thank you for your efforts in revising the manuscript and addressing the reviewer’s concerns. However, most of the responses are still simple and lack detailed, transparent, and reproducible technical details, and several critical issues remain insufficiently resolved:

1. While you have provided additional information on batch effect correction and missing value imputation, your description of the data integration process remains insufficient. Please provide a more comprehensive, stepwise explanation of your data cleaning, normalization, and outlier handling procedures. Additionally, clarify the specific quantitative criteria for dataset inclusion/exclusion and consider providing a workflow diagram to enhance transparency and reproducibility. This part of the content can be displayed in the Supplementary.

2. Your additional validation of KCNQ1 and ITPK1 at the mRNA and protein levels is appreciated; however, the experimental depth remains limited. Please provide further justification for selecting these biomarkers, include functional assays to elucidate their roles, and, where feasible, establish dose-response relationships in your animal models. This part of the requirement is not to ask you to add detailed mechanism analysis experiments, but to conduct necessary functional analysis and verification to increase the confidence and evidence of its use as a biomarker.

3. Your response adds important details about FDR correction and cross-validation, but the statistical and machine learning methodology is still not fully transparent. Please specify the exact parameters used for your models, outline the cross-validation protocol in detail, and provide code snippets or pseudo-code where possible to enable reproducibility and a rigorous assessment of your approach. Specific parameters can be shown in the Supplementary. All codes need to be uploaded to Github and the access address should be given in the manuscript.

4. Thank you for describing your immunohistochemistry workflow and quantification methods. However, you still need to provide essential technical details such as antibody catalog numbers, sources, dilutions, and the specific negative and positive controls used. Please also specify the image analysis software and parameter settings to allow for a robust assessment of data quality and reproducibility.

5. Although you have clarified that phenotype classification is based on diabetes-related markers and immune microenvironment differences, the biological and clinical significance of these subtypes remains underexplored. Please provide a more rigorous discussion and justification for the number of subtypes identified, and where possible, relate these phenotypes to clinical outcomes or disease progression to strengthen the validity of your classification. Moreover, your discussion of the relevant literature remains limited. Please expand your engagement with existing biomarker studies, address conflicting findings in the field, and clearly position your results within the current state of knowledge.

Reviewer 1 ·

Basic reporting

no comment

Experimental design

no comment

Validity of the findings

no comment

Additional comments

This is a well-conducted and comprehensive study that successfully bridges bioinformatics with experimental validation. The integration of multi-cohort datasets and the identification of diabetes-related CAD phenotypes provide novel insights into disease mechanisms. The construction of a robust diagnostic model and validation of key biomarkers like KCNQ1 and ITPK1 in both human and mouse models significantly enhance the translational relevance of the findings. These results offer promising directions for early diagnosis and personalized treatment strategies in CAD patients with diabetes.

Version 0.2

· Apr 4, 2025 · Academic Editor

Major Revisions

After reviewing your revisions, I have serious concerns including methodological flaws, particularly in statistical analysis and biomarker validation. You must provide complete reanalysis with proper statistical controls and independent validation of key findings.

1. The data integration process, which the authors claim as a strength of their study, is inadequately explained in their response. They have not detailed their data cleaning procedures, batch effect correction methods, handling of missing data, or criteria for dataset selection. Without this critical information, it is impossible to assess the validity of their integrated analysis or the potential for systematic bias in their results.

2. The validation of their key biomarkers, KCNQ1 and ITPK1, lacks sufficient experimental depth and mechanistic insight. The authors have not provided adequate justification for selecting these specific markers, demonstrated their functional roles through comprehensive experimental validation, established dose-response relationships in their mouse model, or validated their findings at the protein level. This simple validation significantly weakens their conclusions about these biomarkers' potential clinical utility.

3. The statistical framework of the study requires substantial clarification. The authors' response fails to address crucial aspects of their statistical analysis, including multiple testing correction methods, specific parameters used in their machine learning model, and cross-validation procedures for their diagnostic model. This lack of statistical rigor raises serious doubts about the reliability of their findings and the robustness of their proposed diagnostic model.

4. The technical aspects of their experimental procedures, particularly regarding immunohistochemistry, lack essential details. Their response fails to provide information about antibody validation, quantification methods, appropriate controls, and image analysis parameters. These technical omissions make it impossible to evaluate the quality and reliability of their experimental data.

5. The authors' explanation of their phenotype classification appears circular and lacks biological justification. Their response does not adequately explain the basis for identifying two subtypes, demonstrate the biological significance of these subtypes, or establish meaningful relationships between these phenotypes and clinical outcomes. This weakness in their foundational analysis calls into question the validity of their subsequent findings.

6. The authors' discussion of their findings in the context of existing literature remains inadequate. Their response shows limited engagement with existing biomarker studies, fails to address contradicting findings in the field, and does not effectively position their work within the current state of knowledge. This lack of thorough literature analysis makes it difficult to assess the true novelty and significance of their findings. Given these substantial concerns about methodology and validation, the authors need to provide more rigorous experimental validation and clearer statistical justification for their findings to make a meaningful contribution to the field.

**PeerJ Staff Note:** Please ensure that all review, editorial, and staff comments are addressed in a response letter and that any edits or clarifications mentioned in the letter are also inserted into the revised manuscript where appropriate.

·

Basic reporting

The author made the suggested changes

Experimental design

Suggested changes are made

Validity of the findings

^The author added the suggested changes in discussion

Version 0.1 (original submission)

· Jan 6, 2025 · Academic Editor

Major Revisions

All three reviewers have called for Major Revisions. All of their comments should be addressed, but in "Validity of the Findings" you must be particularly careful to respond to the points raised.

Reviewer 1 ·

Basic reporting

The abstract and introduction should more clearly articulate the novel aspects of this study and its significance in the context of existing research.

Experimental design

no comment

Validity of the findings

Include more details about the validation process in the mouse model, such as reproducibility and cross-validation metrics, to enhance credibility.

Additional comments

1. Proofread for grammatical inconsistencies and improve readability to ensure clarity, particularly for non-specialist readers.
2. Enhance figure descriptions to ensure clarity, and consider adding error bars or confidence intervals to graphical data representations.
3. Elaborate on how the identified biomarkers could be integrated into clinical workflows, particularly for early CAD diagnosis in diabetic patients.

·

Basic reporting

The introduction : The author is advised to cite other vital pathways in pathogenesis of vascular complications of diabetes at line 73. These include advanced glycaton of proteins, activation of protein kinase C and hexosamine biosynthetic pathways besides oxidative stresss. Cite the role of macrophages and their subsets in the complex pathological processes in both diabetes and CAD.

Experimental design

Cite the diagnostic criteria of both diabetes and CAD.

Validity of the findings

Discuss macrophage-associated molecular mechanisms in diabetes and CAD. Discuss the mechanisms that govern macrophage phenotypic transition, differentiation and the coross-talk between CD36, CD 31 and the detected two genes KCNQ1 and ITPK1. Discuss how to to reprogram adult macrophages to acquire regenerative phenotypic capacity.

Additional comments

Add conclusion as a section heading at line 369. There are huge umber of figures 39 and the auuthor is advised to select the necessary figures and try to fuse figures if feasible.

·

Basic reporting

Title
1. The title of the manuscript is slightly unclear. The study identifies “diabetes-related phenotypes in CAD patients”, not just “diabetes-related biomarkers” per se. Moreover, the use of “...in which” is stylistically incorrect. I suggest considering other titles for the study.

Abstraсt
1. The study's main objective is “… to identify and authenticate the diagnostic and therapeutic significance of DM-related biomarkers in CAD”. However, the study does not indicate the significance of either, rather the study identified CAD-related biomarkers, and their gene expression patterns in diabetic patients. I recommend rewriting the main objective corresponding to the study's main findings.

Introduction
1. The introduction part clearly describes the clinical manifestations of CAD and DM. However, since the study illustrates the CAD-related biomarkers in the context of DM and the authors highlight the importance of “identification of effective diagnostic markers for CAD patients”, I recommend adding more CAD and DM interplay at the molecular/cellular level relevant to the study objectives instead of listing the clinical manifestations.
2. The lines 80-84 describe the main findings of the study. The introduction should indicate the main objectives of the study instead. The main findings should be reserved in discussion and conclusion sections. Please rewrite the last paragraph.
Material and methods
1. Lines 98-102: please add the reference to the WGCNA definition.
2. Lines 137-156 use the protocol of mouse euthanasia operation. The lines “…do not inject CO2 into the euthanasia box” and “…do not store or discard at will and make a record on the registration form” should be corrected as the method section describes what has been done for the research not how it should be handled.
3. The sample sizes are not provided. If only two mice were used for the animal model, please clarify that in the section. Is there information about the population in the GEO database (number of patients with CAD and DM, demographic data)? If yes, please provide them in the methods section.
Results
1. Overall, 8 figures were provided in the results section. However, the manuscript attachments provide 33 figures making it very hard to follow. Please correct the numbering and labelling of the figures accordingly.
2. Figures do not have annotations, making it difficult to make sense of illustrations. For example, figure 22 provides the heatmap visualisation of metabolic differences. The illustration is hard to understand and analyse for the reader without any annotation and a short description of the figure. Please provide short descriptions for all figures in the text. Indicate what “group 1 (cluster 1 and 2)” and “group (cluster 1 and 2)” mean.
3. Lines 210-212: “Consequently, CAD patients were divided into phenotype 1 and phenotype 2” Please briefly explain why and based on what characteristics these phenotypes were divided.
4. Same with subtypes 1 and 2, please briefly define what these subtypes are.
5. Lines 263-269: the listing of all 16 genes with their corresponding lasso coefficients is redundant in the main text and should be moved to the figure annotation instead. Same for lines 270-276.
6. Line 294: the speculations on “suggesting roles in regulating diabetogenesis and affecting the immune microenvironment during myocardial injury” of KCNQ1, ATP6V1B1, MTDH, and ITPK1 genes should be in the discussions section as it is not appropriate to add interpretations of the study in the results section.
7. Same for lines 306-309, “This suggests that KCNQ1 307 and ITPK1 are significant in the pathogenesis of CAD…”: the interpretations of the study results should be in the Discussion, not in the result section.

Discussion
1. 313-321: reconsider the placement of the first half of the paragraph. Structurally, I strongly suggest describing the main findings and the interpretations of the study first.
2. Lines 319-321 “However, Combined analysis of diabetes and coronary heart disease based on multi-omics data remain a vacuum, which may bring breakthroughs in the identification for early diagnosis biomarkers” are not clear, please paraphrase.
3. Lines 339-351 repeat the findings described in the Results section. I suggest removing this paragraph as it doesn’t bring any considerable new information to the text.
4. The discussion part needs major revisions. More references and comparisons in the similar studies are needed. For example, the authors found that KCNQ1 and ITPK1 may influence the immune microenvironment of CAD. However, they do not provide any relevant studies that exhibit similar or contradicting results. The discussion part should compare their results with other existing studies to provide a comprehensive view of their main findings.
5. Lines 354-356 “Our data imply that phenotype-associated genes lead to changes in different pathways, including the Hippo system 356 and NRF2 pathway”: Please support your claims with other studies. Same with lines 360-361 “These findings imply that metabolic reprogramming frequently occurs in tandem with changes in the diabetes-related phenotype”.
6. Lines 364-365: list what critical indices for CAD and Dm were not obtained.
7. Lines 369-370 “To conclude, we constructed a diabetes-related CAD phenotype and an optimal diagnostic model”: not entirely clear what the optimal diagnostic model means, please provide the algorithm of a suggested diagnostic model if applicable.
8. Lines 228-232 "Additionally, we examined the strength of endogenous and exogenous immunomodulation between the subtypes. While endogenous immunity strength was similar between subtypes, exogenous immunity was significantly stronger in subtype 1 compared to subtype 2": Please describe the interpretation and the significance of these findings in the discussion section.
9. Adding the future applications of the study in the conclusion part is suggested.

Experimental design

The investigation is rigorous and of high quality. The research design is clear, comprehensive, and replicable. However, the methods section needs minor revisions described in the basic reporting.
The aim of the study needs to be more clearly defined in the Introduction section as it was requested in the basic reporting.

Validity of the findings

The research findings need to be supported or provided with contradicting findings from other research papers. More concrete suggestions are provided in the basic reporting section.

Moreover, I suggest separating the last paragraph of the manuscript into the conclusions section.

Additional comments

Minor corrections:

Affiliations info – correct double commas after “…Hospital of Xinjiang Medical University,,” and same mistakes in the affiliations.

Use the full name of “WGCNA analysis” in line 22.

Line 39 “exhibited highly co-expression with macrophage biomarkers in heart tissue” is unclear and seem to be unfinished.

Please use the full names of abbreviations when they appear in the text, ex. Lines 52 and 53 - “DM”, “CVD”. Please make sure to provide full names before using abbreviations throughout the text.

Line 22 “phenotypic 1” should be corrected to “phenotype 1” and line 226 “phenotypic 2” should be “phenotype 2”. The same mistakes are present throughout the text. Please correct them all.

Line 335 “In our current research, Differential analysis and WGCNA...” – Differential should be in lower case.

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