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I have carefully reviewed the revised manuscript and the point-by-point response to the previous comments. After assessing the revision myself, I am pleased to inform you that all requested changes have been adequately addressed. The current version of the manuscript meets our requirements and addresses all the points raised in the previous review. As such, I am happy to accept this revised version for publication. Thank you for your diligence in making these revisions.
[# PeerJ Staff Note - this decision was reviewed and approved by Celine Gallagher, a PeerJ Section Editor covering this Section #]
After careful consideration of the reviewers' comments and your revisions, I am pleased to inform you that your manuscript is potentially acceptable for publication, pending some minor revisions. Please address the following points in your final revision:
1. Ensure all abbreviations are fully defined at their first use in both the main text and figure legends.
2. Complete the reference list by adding missing page numbers for references 1, 15, and 35.
3. Provide website links for all databases and analysis software mentioned in the manuscript.
4. Improve the resolution of the images to enhance clarity.
5. Double-check that all references are cited correctly throughout the text.
Please submit your revised version along with a point-by-point response to these comments within two weeks. We look forward to receiving your revised manuscript.
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The authors' responses to reviewers' comments have been largely satisfactory, but there remain areas that need further refinement.
1. Please check the entire text and provide the website links for the databases and analysis software used.
2. The clarity of the image is not sufficient; the resolution needs to be increased.
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Appropriately revised and all concerns raised by reviewers have been addressed.
Abbreviations in the Legend should also be annotated.
I am pleased to inform you that your manuscript has been found to be of interest and potentially suitable for publication, pending minor revisions. All three reviewers have provided constructive feedback and have recommended minor revisions to improve the clarity and impact of your paper. Please carefully address all the points raised by the reviewers in your revision. In particular, focus on:
1. Clarifying your methodology and analysis procedures
2. Strengthening the discussion of your results in the context of existing literature
3. Addressing any inconsistencies or ambiguities pointed out by the reviewers
We look forward to receiving your revised manuscript. Please provide a point-by-point response to the reviewers' comments along with your revision, clearly indicating the changes made in the manuscript.
1. The manuscript is well-written and presents an interesting study. However, there are some areas where the language could be improved for clarity and precision.
In line 29, "ox-LDL treatment HUVECs" should be "ox-LDL-treated HUVECs"
In line 54, “revealed the approximately” should be "revealed that there were approximately”;
In line 58 “CDNK2B GUCY1A3” should be “CDNK2B, GUCY1A3”;
In line 114, “were” should be “was”;
In line 266, “the the metadata set” should be “the metadata set”;
In line 272, “protein–protein” should be “protein-protein”;
In line 289 “was” should be “were”; in line 291, “were” should be “was”.
2. Ensure that all references are cited correctly and that the reference list is complete. Page numbers of references 1, 15, and 35 are missing.
Original primary research within the Aims and Scope of the journal. The research question is well-defined, relevant & meaningful. It is stated how research fills an identified knowledge gap. Methods described with sufficient detail & information to replicate.
The research presents a comprehensive approach to identifying a novel gene signature for coronary artery disease (CAD), which is a significant contribution to the field. The study's innovation lies in its integrative analysis of immune cell infiltration's role in CAD pathogenesis, alongside the exploration of the MCEMP1 gene's function under ox-LDL treatment in HUVECs. This dual focus provides a nuanced understanding of the complex interplay between genetic factors and cellular responses in the context of CAD.
All underlying data appear to have been provided, which enhances the transparency of the study. The data are robust and have been statistically soundly analyzed, indicating a well-controlled experimental design. This is commendable and aligns with best practices in scientific research.
The conclusions drawn by the authors are well-stated and are linked to the original research question. They are limited to supporting the results presented in the study, which demonstrates a careful and appropriate interpretation of the findings.
1. The description of the GEO datasets is somewhat brief. The authors should provide more context, such as the specifics of the patient populations, the experimental designs, and the reasons for selecting these particular datasets.
2. While SVM-RFE and LASSO are mentioned, the rationale for choosing these specific algorithms over others should be explained. Additionally, the parameters used for these algorithms should be detailed.
The paper identified a novel immune infiltration-related gene signature, MCEMP1, for coronary artery disease. The methodology is based on applying SVM-RFE and LASSO algorithms to screen Hub genes from GSE24519 and GSE6114 datasets. Moreover, the cell function of MCEMP1 in ox-LDL treatment HUVECs was also explored. The methods and analysis flow are reasonable. The following are some humble comments.
The paper identified a novel immune infiltration-related gene signature, MCEMP1, for coronary artery disease. The methodology is based on applying SVM-RFE and LASSO algorithms to screen Hub genes from GSE24519 and GSE6114 datasets. Moreover, the cell function of MCEMP1 in ox-LDL treatment HUVECs was also explored. The methods and analysis flow are reasonable. The following are some humble comments.
1. For mRNA expression, it would be nice to add a supplementary table including the fold-change values and the types of mRNA expression data (RNASeq or microarray).
2. On what criteria did the authors choose to try LASSO regression analysis and random forest for feature selection?
3. Why only MCEMP1 was selected for the cell function experiment?
4. How were the concentrations of ox-LDL selected? Please add references.
5. Is it possible to discuss the role of MCEMP1 in immune infiltration. The discussion on its regulation may help explore the possibility whether MCEMP1 can be used as a new therapeutic target in CAD.
6. The authors have only used one cell line for the in vitro studies. Authors need to acknowledge this limitation.
7. The explanation of the captions of the figures is very brief. Please expand them.
The design appears robust and well-structured, with a comprehensive approach to data analysis and validation. Below, I provide some comments and suggestions to further strengthen the study:
Data Reproducibility and Robustness:
Ensure that all datasets used (GSE24519, GSE61145, and GSE113079) are appropriately normalized and preprocessed to maintain data integrity. Consider including a detailed description of the preprocessing steps in the methods section to enhance reproducibility.
Selection of Hub Genes:
While SVM-RFE and LASSO are effective methods for feature selection, it might be beneficial to cross-validate the identified hub genes using an independent dataset or an additional statistical method to confirm their significance in CAD.
Validation of Bioinformatics Results:
The use of Western blot and qRT-PCR for validating the expression levels of hub genes in ox-LDL-treated HUVECs is commendable. However, consider including immunohistochemistry (IHC) or in situ hybridization to validate the expression patterns in tissue samples, providing a more comprehensive validation.
Functional Analysis of MCEMP1:
The examination of cell proliferation and apoptosis upon MCEMP1 knockdown is insightful. Consider expanding this analysis to include other functional assays such as migration, invasion, or angiogenesis assays to gain a broader understanding of MCEMP1's role in endothelial cell function.
Inflammatory Factor Analysis:
The detection of IL-1β, IL-6, and TNF-α levels using ELISA kits is appropriate. However, it would be beneficial to correlate these findings with the expression levels of their respective receptors or downstream signaling molecules to elucidate the potential mechanisms by which MCEMP1 influences inflammation.
Statistical Analysis：
Ensure that appropriate statistical tests are used to analyze the data, and provide a clear description of the p-values, confidence intervals, and effect sizes. Consider including a power analysis to justify the sample size used in the experiments.
Conclusion and Future Directions:
While the conclusions drawn from the study are promising, it would be beneficial to discuss potential limitations and future directions. For instance, exploring the clinical relevance of MCEMP1 in larger patient cohorts or investigating the interaction of MCEMP1 with other key molecules in the CAD pathway.
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Comments
In this study, the authors introduce a novel genetic marker for coronary artery disease (CAD) employing multiple machine learning methods across various databases. Additionally, they utilized pertinent experimental techniques to validate the bioinformatics findings. The analysis is comprehensive, and the manuscript is well-crafted. Before proceeding to the next stage, I have a few questions:
1. Abstract:
Clarity in Presentation: While the manuscript is generally well-written, clarity could be enhanced by providing more detailed explanations of certain experimental procedures and results in the Abstract.
2. Introduction
The manuscript is generally well-structured, providing a clear background on CAD epidemiology, genetic factors, and the role of immune cell infiltration. However, certain sections, particularly those describing the bioinformatics analyses and experimental procedures, could benefit from additional clarity and detail to aid replication by other researchers. Definitions of technical terms and acronyms should be provided where appropriate for readers unfamiliar with specific methodologies or concepts.
3. Methods
The methodology employed, including DEG analysis using GEO database samples and machine learning algorithms for hub gene identification, is robust and well-suited for the study's objectives. However, further details on specific algorithms used and parameters set would enhance transparency and reproducibility. Moreover, the validation of hub gene expression in ox-LDL-treated HUVECs adds valuable experimental insights, though additional statistical analyses and controls for variability should be elaborated upon to strengthen the experimental findings.
Provide detailed descriptions of machine learning algorithms used, including parameters and validation methods employed.
4. Results
-The statistical methods employed, such as SVM-RFE, LASSO logistic regression, and validation using independent datasets, are appropriate and robust. However, the statistical significance and clinical relevance of the findings need to be better emphasized. For example, the discrepancy in PFKFB3 expression between metadata and validation datasets should be discussed in more detail.
- The manuscript touches upon the correlation of DIRC2 and MCEMP1 with immune cell infiltration and signaling pathways, but lacks in-depth mechanistic insights. The authors should discuss potential biological mechanisms linking these genes to CAD pathophysiology based on existing literature or provide hypotheses for future investigation. If it cannot be resolved, this limitation should be discussed in the discussion
5. Language and Style:
The manuscript requires significant improvement in language clarity and scientific writing style. Several sentences are awkwardly phrased or lack necessary details, hindering comprehension.
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