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Dear authors,
The reviewers were satisfied with the revisions and answers provided. I am now accepting your work for publication. Many thanks for your submission and work.
[# PeerJ Staff Note - this decision was reviewed and approved by Paula Soares, a PeerJ Section Editor covering this Section #]
I don't have any further objections. Although the English can still be polished.
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The authors have responded to the reviewer’s comments and revised the manuscript accordingly.
**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.
About the manuscript ID-119392, submitted to PeerJ, entitled: "Hypoxia- and mitochondria-associated hub genes in recurrent pregnancy loss: integrated bioinformatics and experimental validation".
I have read the manuscript, and I consider that it reports results deserving publication, while it can be improved for enhancing its impact.
The study evaluates the role of hypoxia and mitochondrial dysfunction on recurrent pregnancy loss (RPL), a relevant clinical issue affecting about 2.5% of women in reproductive age, occurring before 20-24 weeks of gestation.
The study combines transcriptomic data with functional annotation and immune infiltration profiling and regulating network analysis, uncovering hub genes linked to hypoxia and mitochondrial dysfunction, identifying BSG and TRAK1 as key hub genes through bioinformatic analysis and RT-qPCR validation in endometrial tissue obtained from RPL individuals and matched controls.
The study identified BSC and TRAK1 as key regulators, linking hypoxia-induced mitochondrial dysfunction to immune tolerance disruption in TPL, suggesting potential therapeutic strategies.
A main shortcoming is the lack, at least in my version, of Figure Legends, helping the reader to understand what should be read in the figures, identifying the findings and the rational of the experiments, increasing also insights about the discussed and not discussed results. Indeed, the obtained results deserve an expanded discussion, also discussing further the methodological limitations.
OK
Yes
Read above.
Okay
Okay
Highly speculative and contains excessive and misleading claims
The research paper by Chen et al. (119392-v0, Hypoxia- and mitochondria-associated hub genes in recurrent pregnancy loss: Integrated bioinformatics and experimental validation).
By combining transcriptomic data with a curated list of HRGs and MRGs, the authors performed bioinformatic analyses to identify BSG and TRAK1 as key hub genes in the pathogenesis of RPL. The paper is highly speculative and contains excessive and misleading claims.
Specific/major comments:
Line 40: “Gene set enrichment analysis linked these genes to processes such as …” – the authors’ claim that “GSEA analysis identified the potential role of BSG in RPL” is clearly excessive, as these genes have not been established as causative and no mechanistic insights have been presented.
Lines 44-45: “Drug prediction analyses suggested …” – this section is entirely speculative and misleading. Since causation has not been established and no mechanistic insights have been provided, how can the authors justify the rationale for drug prediction targeting these genes?
Line 47: The conclusion that “BSG and TRAK1 may play crucial roles” is itself very misleading. All the authors have demonstrated is the overexpression of these genes in RPL, which could just as well be an effect rather than a cause. The title and the entire manuscript require revision to reflect this distinction.
Methodology Section 2.2: Differential Expression Analysis – were the data normalized in any way? How were the two datasets integrated? Please clarify.
Methodology Section 2.6: GSEA of Hub Genes – which gene symbols were used, and what was the background dataset? Please specify the number of genes in the foreground (high and low) and background sets.
Lines 267-268: “potential drugs … predicted by BSG and TRAK1” – how these genes predicted drugs?
Conclusion: “TRAK1 may play crucial roles in RPL pathogenesis” – however, PCR validation shows that the difference in TRAK1 expression in RPL is non-significant!
None of the figures have self-explanatory legends. For example, Fig. 2: DEG heatmap – of which genes? Why is it circular? Figures 4 and 5 are difficult to interpret. All other figures have similar issues. Please provide all LASSO-related figures in the supplement, as they are clearly secondary.
There are an excessive 28 figures. Most figures could be meaningfully combined to create composite figures.
Consider English revision. For instance, “Afterthat”, “Used transcriptomic data and machine learning, we reveal …”, etc.
In this manuscript, authors identified Hypoxia- and mitochondria-associated hub genes in recurrent pregnancy loss using bioinformatics tools, followed by validation of two genes, BSG and TRAK1, by quantitative RT-PCR. This is an interesting work; however, certain aspects of the study design or the presentation of results are unclear.
Comment: There are many grammatical and typo errors.
Comments
1. Fold change threshold was set as |log2FoldChange| > 0.5. Should not it be at least |log2FoldChange| > 1 or higher for selecting more specific DEGs?
2. While performing String PPI network analysis, why did you select a lower interaction score of 0.4? Usually, a higher score of 0.7 is recommended to identify potential interactions.
Comments:
1. Line 211-213: “Eleven candidate genes were selected by overlapping 831 DEGs with 2,030 MRGs and 493 HRGs (Fig. 3). Enrichment analysis of these genes revealed 584 GO terms and 16 KEGG pathways.” How is it possible that 584 GO terms enriched among 11 genes? What was your gene list for functional enrichment analysis?
2. Eleven candidate genes were selected by overlapping 831 DEGs with 2,030 MRGs and 493 HRGs. A list of these 11 genes should be provided.
3. Hub genes are usually those genes interacting with a large number of other genes in a network. Also, hub genes are identified when the data set of interest contains a large number of genes. If we look at PPI network BSG and TRAK1 (Figure 6), TRAK1 doesn’t even interact with other genes in the PPI network of 11 selected genes. What was your input for selecting hub genes?
4. Too many unnecessary figures.
While the remaining experiments and procedures appear to be standard, the issues highlighted above make it difficult to follow and review them.
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