All reviews of published articles are made public. This includes manuscript files, peer review comments, author rebuttals and revised materials. Note: This was optional for articles submitted before 13 February 2023.
Peer reviewers are encouraged (but not required) to provide their names to the authors when submitting their peer review. If they agree to provide their name, then their personal profile page will reflect a public acknowledgment that they performed a review (even if the article is rejected). If the article is accepted, then reviewers who provided their name will be associated with the article itself.
All issues indicated by the reviewers were addressed and the revised manuscript is acceptable now.
[# PeerJ Staff Note - this decision was reviewed and approved by Vladimir Uversky, a PeerJ Section Editor covering this Section #]
good
good
good
good
Great
Looks great
Well validated
Please address all the issues pointed out by the reviewers and amend 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.
The manuscript titled "Identification of mitochondrial-related genes to evaluate the immune infiltration and prognosis of lung adenocarcinoma" presents a comprehensive multi-omics analysis integrating bioinformatics and experimental validation to identify a mitochondrial-related (MTR) gene signature (SFXN1, CPS1, and MTFR2) for prognostic stratification in lung adenocarcinoma (LUAD). The study demonstrates robust methodology, including differential expression analysis, LASSO regression, and functional validation via siRNA knockdown, to elucidate the roles of these genes in mitochondrial dynamics and immune modulation. The findings highlight the clinical relevance of the MTR signature in predicting survival and immune phenotypes, offering potential therapeutic targets. However, the manuscript would benefit from clearer discussion of limitations, expanded mechanistic insights, and improved language precision.
Major Comments
Clarity and Flow
The manuscript is well-structured but could improve transitions between sections, particularly between bioinformatics and experimental validation (e.g., Sections 3.5 to 3.9). A brief summary sentence linking these sections would enhance readability.
I would suggest that the authors expand their discussion by incorporating recent advancements in cancer biomarker detection, which could potentially enhance the depth of their study. Specifically, research on liquid biopsies has shown great promise in improving cancer diagnostics and monitoring. For example, the study "Updates on liquid biopsies in neuroblastoma for treatment response, relapse, and recurrence assessment" (2024) demonstrates the effectiveness of circulating tumor DNA (ctDNA) detection through liquid biopsy techniques.
Additionally, "Development of a molecular barcode detection system for pancreaticobiliary malignancies and comparison with next-generation sequencing" (2024) highlights how emerging sequencing technologies improve DNA analysis sensitivity and specificity.
In the context of bladder cancer, the "Landscape of urine biomarkers for bladder cancer: molecular function, cell-of-origin, and bibliometric trend" (2024) offers valuable insights into the potential of urine-based biomarkers for early diagnosis.
Furthermore, methylation signatures have been explored for non-invasive early detection, as discussed in "Methylation signatures as biomarkers for non-invasive early detection of breast cancer: A systematic review of the literature" (2024). I recommend that the authors read these related studies and discuss whether the mechanisms explored in their paper could be detected through these advanced diagnostic methods. This would not only broaden the scope of the current work but also place it in the context of cutting-edge diagnostic techniques that could improve clinical applicability.
Discussion of Limitations
The limitations section (Page 17, lines 377–380) briefly mentions reliance on public datasets, but should expand on:
- The single-cell RNA-seq validation’s reliance on one paired sample may limit generalizability. Read previous studies using single-cell analysis, such as “Single-cell RNA sequencing and machine learning provide candidate drugs against drug-tolerant persister cells in colorectal cancer, 2024”, then discuss the advantages and disadvantages of single-cell analysis compared to bulk sequencing analysis.
- Discuss potential biases from batch effects in TCGA/GEO data integration.
- The need for prospective clinical validation to confirm the model’s utility.
Suggest future studies that could validate these findings in patient-derived xenograft models. You may read previous studies using xenograft models of cancer, such as "Deficiency of BCAT2-mediated branched-chain amino acid catabolism promotes colorectal cancer development, 2024" and "hsa-miR-CHA2, a novel microRNA, exhibits anticancer effects by suppressing cyclin E1 in human non-small cell lung cancer cells, 2024”
Mechanistic Insights
The Discussion (Page 16–17) thoroughly describes the roles of SFXN1, CPS1, and MTFR2, but could better integrate how their mitochondrial functions (e.g., fission/fusion, ROS modulation) directly influence immune evasion or therapy resistance. For example, link CPS1’s role in urea cycle dysregulation to TME ammonia levels and immune suppression. Discuss how MTFR2-driven metabolic shifts (e.g., glycolysis) might alter checkpoint inhibitor responses.
Technical Justifications
The choice of LASSO regression (Page 8, line 105) should be justified with references to similar studies in LUAD or other cancers. Clarify why CPS1, despite lacking prognostic significance in univariate analysis (Page 12, line 249), was retained in the final model.
Discuss how new technology can help in the future. You may read the paper “Integration in Biomedical Science 2024: Emerging Trends in the Post-Pandemic Era”.
**PeerJ Staff Note:** PeerJ's policy is that any additional references suggested during peer review should only be included if the authors find them relevant and useful.
-
Justification for Inclusion of CPS1 in the Prognostic Signature
Although CPS1 is included in the 3-gene risk model, its individual prognostic significance was not supported (log-rank P = 0.294). The rationale for retaining it in the final model should be clarified, or the model should be reevaluated for robustness without CPS1.
Lack of Mechanistic Insights into Immune Modulation
The study links MTR gene expression with immune cell infiltration and checkpoint marker expression; however, mechanistic connections (e.g., cytokine profiles, immune pathway alterations) remain speculative. Including further experimental validation or a detailed discussion would strengthen the immunological relevance.
Incomplete Validation of siRNA Knockdown
While qPCR results are shown, protein-level validation (e.g., Western blot) for knockdown efficiency is missing. Since functional assays (ROS, membrane potential) depend on successful knockdown, this validation is essential for the credibility of the experimental findings.
Minor Comments
Language Clarification in Abstract and Introduction
Sentence in line 73: “Upregulation of a key regulator of mitochondrial biogenesis...” — the specific gene/regulator is not named. Please revise for clarity.
Formatting and Figure Referencing
Several figures in the supplementary section are referenced (e.g., Fig. S2, S5), but their legends are not clearly provided within the main text. Consider adding legends or summarizing their content briefly for reader clarity.
Statistical Thresholds for Functional Assays
For confocal/flow cytometry results (Figures 8–10), mention the number of replicates, statistical tests used, and significance thresholds (e.g., p-values, error bars) in the figure legends or methods.
The manuscript evaluates mitochondrial dynamics and ROS levels after siRNA knockdown of SFXN1, CPS1, and MTFR2 in LUAD cell lines. However, no mention is made of non-targeting siRNA controls or rescue experiments (e.g., re-expression of the gene after knockdown) to validate that observed effects are specific to gene silencing. Including these controls, or at least discussing their absence as a limitation, is critical to confirm that the mitochondrial and phenotypic changes observed are not due to off-target effects or generalized siRNA-induced stress.
-
Please separate the tables included in Fig. 2.
The X and Y labels in Fig. 2 are tiny.
I do have the following suggestions to improve the quality of the manuscript:
A. When the RNA-seq data and the clinical information of 550 LUAD samples were downloaded from
The Cancer Genome Atlas? Please include the date and database version, if any.
B. Please include the version number of DESeq2 used in this manuscript for analysis.
C. Please mention the name of the library used to generate the Venn diagram and its version number.
D. Please include the name and program used for univariate Cox regression, Lasso regression, and multivariate Cox regression-based analysis.
E. Please include the version numbers for the ggpubr, ggthemes, survival, survminer, rms, maftools, and clusterProfiler packages.
Conclusions were not well stated; they should be linked to the original research question.
All the codes used for bioinformatics analysis should be kept on GitHub to ensure the reproducibility of the results, and github link should be included in the
All text and materials provided via this peer-review history page are made available under a Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.