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We are pleased to accept your manuscript, "A nicotinamide metabolism-related gene signature for predicting immunotherapy response and prognosis in lung adenocarcinoma patients" for publication in PeerJ. Your study provides important insights into the role of nicotinamide (NAM) metabolism in lung adenocarcinoma (LUAD) progression and offers a novel gene signature to assess immunotherapy response and prognosis. The meticulous methods employed, including the use of TCGA and GEO datasets, consensus clustering, and comprehensive immune microenvironmental profiling, demonstrate a high level of rigor and scientific excellence. The identification of four independent prognostic nicotinamide metabolism-related genes (NMRGs) and their application in constructing a RiskScore model is particularly noteworthy. The strong predictive performance of the model, as well as the findings regarding immune cell infiltration, TIDE score, and drug sensitivity, are of significant clinical relevance. Furthermore, the cellular assays validating the mRNA expressions of the NMRGs and their impact on LUAD cell migration and invasion add to the robustness of your findings. Overall, your manuscript makes a substantial contribution to the field and will be of great interest to researchers and clinicians in the oncology community. Congratulations on your excellent work and thank you for submitting your manuscript to PeerJ.
[# PeerJ Staff Note - this decision was reviewed and approved by Sonia Oliveira, a PeerJ Section Editor covering this Section #]
This study is based on nicotinamide metabolism related genes (NMRG) and uses a series of bioinformatics tools to explore genes related to immune response and prognosis in lung adenocarcinoma (LUAD). It is expected to provide some guidance for the improvement of clinical LUAD treatment strategies. Ideologically speaking, this study reveals prognostic biomarkers for LUAD in a more traditional way. Firstly, this study identified LUAD molecular subtypes with different nicotinamide metabolism characteristics through consensus clustering, and evaluated the clinical features between these subtypes using COX regression analysis and survival analysis. Then, based on the differences and enrichment analysis, the differential molecular pathways between molecular subtypes were revealed. Subsequently, this study constructed a prognostic evaluation model for LUAD and established its correlation with cancer immunotherapy, clinical features, and drug resistance. Then, cell experiments were used to verify the tangible regulatory role of the prognostic feature genes identified in this study in the progression of LUAD. In summary, this is a relatively comprehensive study and the main issues have been resolved. I have no further comments.
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In this study, the authors explored the potential association between the characteristics of nicotinamide metabolism related genes and the development and prognosis of lung adenocarcinoma. Identifying prognostic factors and mutation characteristics of lung adenocarcinoma through bioinformatics methods. Further in vitro experiments were conducted and fully validated at the biomarker level. This is a complete and comprehensive study that complies with peerj's publishing policy.
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It is my opinion as the Academic Editor for your article - Nicotinamide metabolism-related gene signature predicts immunotherapy response and prognosis in lung adenocarcinoma - that it requires some revisions according to the detailed comments from the 2 reviewers.
[# PeerJ Staff Note: The review process has identified that the English language must be improved. PeerJ can provide language editing services if you wish - please contact us at [email protected] for pricing (be sure to provide your manuscript number and title). Your revision deadline is always extended while you undergo language editing. #]
In terms of results, this study mined genes related to immune response and prognosis of lung adenocarcinoma (LUAD) based on nicotinamide metabolism-related genes (NMRGs) by a series of bioinformatics tools, which is expected to provide some guidance for the improvement of treatment strategies for clinical LUAD. Ideologically, this study reveals LUAD prognostic biomarkers in a more conventional manner. First, the study identified molecular subtypes of LUAD with different nicotinamide metabolic profiles by consensus clustering, and assessed the clinical characteristics among these subtypes in conjunction with COX regression analysis and survival analysis. Then, based on difference-in-difference and enrichment analyses, differential molecular pathways between molecular subtypes were revealed. Subsequently, this study constructed a prognostic assessment model for LUAD and established the correlation between the model and cancer immunotherapy, clinical features and drug resistance. Cellular experiments were then used to validate the tangible regulatory role of the prognostic signature genes mined in this study in LUAD progression. In conclusion, this is a relatively comprehensive study, but the following questions still need to be addressed before publication:
1. Since this study focused on NAM features to construct a prognostic model, why was it followed up with an analysis of the link between the model and the cancer immune response? Are there any reports in the literature that clarify a potential link between the two? Please expand on this and reflect it in the introduction section.
2. The study did not analyze NAM-related genes in depth, please describe in general terms the starting point of this article in the introduction section of the article, and what medical challenges led to this study?
3. The abstract section describes the main findings of this paper including elucidating the differences in mutation frequencies among LUAD subtypes and revealing several genes with higher mutation frequencies, but these genes are not systematically elucidated in the discussion section, why is this? Please give reasons and add descriptions where necessary.
4. Does the fact that the high-risk score group presented higher sensitivity to various chemotherapeutic agents imply that these agents are uniquely advantageous for the treatment of LUAD? If so, please highlight the scientific significance of this article by discussing the current status of treatment with these agents in LUAD.
5. What is the impact of GJB3 on the progression of LUAD in the existing literature studies? Has its role in regulating the malignant phenotype of cancer cells been elucidated by existing reports? Please describe in the results section.
6. Major therapeutic strategies for LUAD have made great progress in the treatment of this disease, so what is the significance of conducting this study? Does the mining of LUAD-related prognostic markers in this study have important roles for the improvement of existing treatment strategies? What are the specific aspects of these important roles? It is recommended that this be described.
7. It is well known that immunotherapy plays an ideal role in the treatment of LUAD, but what is the correlation between its efficacy and the metabolism of cancer cells? This paper does not describe the correlation between the two, and only transitions rigidly from immunotherapy to cancer cell energy metabolism, which obviously fails to highlight the profound intention of this paper, and thus it is recommended to add literature to describe the intrinsic correlation between the two.
8. The description of the four LUAD prognosis-associated genes in the Discussion section is not insightful and does not elaborate on how the characterization of these genes as reported in the existing literature relates to the present study. Please add to this, e.g., by clarifying whether the association of these genes with cancer prognosis is related to the pathways and immune characteristics revealed in this study.
9. The limitations of this paper are not just the limited sample of datasets, but also the fact that the relationship between metabolism-related genes and LUAD immunoregulation has not been elucidated, and thus, please briefly outline the general ideas for follow-up cellular or tissue experiments to be carried out.
10. Metabolic reprogramming is an important factor in cancer progression, and the mechanisms by which metabolic reprogramming affects cancer progression are not well known to most researchers, so it is suggested that the Discussion section should explain what types of metabolic reprogramming in cancer are included, and by what mechanisms these regulatory processes promote the malignant phenotype of cancer cells.
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In this study, the author explores the potential link between the nicotinamide metabolism-correlated gene signature and the development and prognosis of lung adenocarcinoma. Prognostic factors and mutational signatures in lung adenocarcinoma were identified through bioinformatics methods. The experimental design is rigorous. However, there are still some deficiencies in details in the manuscript.
1. In the methods of abstract. Please indicate the training and validation sets and their sources.
2. Line 51-54 (page 1), “Based on the…in GSE31210 dataset”, this is a description of the result. Please describe the specific method and tool package, and review this section and revise it carefully.
3. Line 46 (page 2), the LUAD was first appearance in main text. Please mark its full name.
4. Line 56-61 (page 2), the great advancement has made in diagnostic and therapeutic techniques of LUAD. What accounts for its poor 5-year survival rate.
5. Line 10-21 (page 3), the limited responder to immunotherapy is a main difficulty of immunotherapy, so the more effectively treatment targets are necessary. Developing novel and reliable signatures to evaluate immunotherapy response may not significantly improve patient outcomes. This sentence does not quite convey the purpose of this article.
6. Line 21 (page 5), the Somatic gene mutation landscape analysis was performed. The candidate genes of model were screened from the DEGs. So what is the point of performing analysis of Somatic gene mutation landscape, and is it necessary for this article
7. The GSEA between two subtypes was performed, so Is it necessary to perform this analysis for patients in the high - and low-risk groups.
8. What is the specific clinical application of RiskScore and how does it improve patient outcomes.
9. The immune infiltration characteristic is an important feature of patients. Please discuss the results appropriately.
10. Why only the function of gene GJB3 is analyzed. Please explain the basis for selecting this gene.
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