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Authors have addressed all of the reviewers' comments and manuscript is ready for publication.
[# PeerJ Staff Note - this decision was reviewed and approved by Paula Soares, a PeerJ Section Editor covering this Section #]
no comment.
no comment.
The authors have addressed my previous comments in the second round of review.
The comments from Reviewer 2 were not fully addressed. The authors are advised to address these comments to ensure their manuscript is considered for publication.
The authors have provided responses and carried out revisions in accordance with majority of the comments. The responses have addressed the questions I raised to a considerable extent, and the manuscript has been enhanced. No further comments.
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The authors have addressed my previous comments.
The authors have addressed my previous comments.
Correction for multiple comparison is a must-do in differential gene expression, as I pointed out in the first round of review. The revised manuscript did not fix it.
In addition, the authors did not address my previous concern about training/testing split: they added that "parameter used for Lasso was determined by 10-fold cross-validation", but still haven't reported model performance on independent testing set.
This manuscript requires a thorough revision and additional data/ analysis before its consideration of publication in PeerJ. Please address comments of all reviewers and provide responses in a point wise manner.
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[# PeerJ Staff Note: It is PeerJ policy that additional references suggested during the peer-review process should *only* be included if the authors are in agreement that they are relevant and useful #]
This study employed bioinformatics and wet experiments to divide laryngeal cancer into two groups based on the expression levels of immune cell-related genes in laryngeal cancer, evaluated the immune microenvironment of the two groups, and then constructed a prognostic model. Studies on the expression of immune cell-related genes in relation to the immune microenvironment and prognosis have been conducted in malignant tumors, but there has been no relevant report in laryngeal cancer yet. Therefore, this study has certain innovativeness. Generally, this manuscript adopted appropriate methods, and the results are logical and can answer the hypothesis proposed by the author. Nevertheless, there are still some issues that need to be modified:
1) The INTRODUCTION section adopts a funnel structure, gradually focusing and guiding to the research aim. However, since this manuscript mainly focuses on the immune microenvironment, a brief summary of immunotherapy for laryngeal cancer is needed in the introduction, as well as a brief discussion on the relationship between the efficacy of immunotherapy and the immune microenvironment, so as to lead to the purpose of this study more clearly.
2) For the incidence and mortality data, please cite the latest literature: Bray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, Jemal A. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2024 May-Jun;74(3):229-263.
3) Language proofreading is needed. For instance, “could promoting” (Line 82) should be corrected as “could promote”. Some typos need to be corrected, for example, “immunetherapy” (Line 43). Please carefully check the entire manuscript.
Generally, the research question was defined clearly and the investigations were conducted relatively rigorously with good technical standard. The methods were described in detail, which facilitates other investigators to replicate. Nevertheless,
1) Lines 225 – 226, please provide the number of samples (patients).
2) Lines 227 – 228, please provide the ethical approval number.
1) Lines 271 – 272, the manuscript only uses TCGA to screen for differentially expressed genes, so the reliability of the results is insufficient. Usually, at least two datasets should be used. First, differentially expressed genes are screened separately in each dataset, and then the intersection is taken to generate result. Moreover, in the TCGA cohort selected in this study, the difference between the number of laryngeal cancer samples and the number of normal tissue samples is notable (112 and 11 respectively), which may bias the result. Therefore, it is recommended to adopt at least one more dataset.
2) As is mentioned in 3.8, the expression level of PD - 1 is significantly higher in the low-risk group. Thus, can this study measure the PD-L1 expression level of the clinical specimens by immunohistochemistry?
3) The manuscript found that OLR1 and RENBP are up-regulated in laryngeal cancer. So, what are the upstream mechanisms for the up-regulation? For example, are there any changes in methylation levels, amplification levels, mutations, lncRNA - miRNA - mRNA regulation, etc.? Of course, since this study focuses on the downstream results caused by the up-regulation of OLR1 and RENBP, and the research on the mechanisms of up-regulation will also take up a lot of space, so it is possible to include the upstream mechanisms or simply skip it, both are Ok.
None.
This following text uses weird abbreviation and it’s difficult to find what does “N” mean in the text: “clinicopathological characteristics (M (M0, MX), Age (> 60 and =<181 60), T (T1, T2, T3, T4), Gender (Male, Female), N (N0, N1, N2, N3, NX)”.
The results are poorly organized. They only report something like which is higher than which, without further explanation, interpretation, or comments.
Figures are poorly organized. First, figures’ text are not visible. Second, there are many plots that are redundant as they are not even mentioned in the manuscript (e.g., Fig. 4B right panel). Third, each plot’s meaning is barely explained.
This manuscript follows very commonly used bioinformatics analyses to quickly produce a paper. Meanwhile, the manuscript generally lacks insights and interpretations on the results. For example, the authors did not describe how to interpret results of WGCNA.
The Differential Gene Expression results are not corrected for multiple comparison and are therefore not valid.
The results “RENBP and OLR1 were Prognostic Biomarkers” is not valid. A prognostic biomarker should be able to predict prognosis on unseen samples, not on training samples. The main text only reports the ROC-AUC of the LASSO model on the training set and suspiciously placed the validation result into the supplementary material. Besides, a rigorous procedures require splitting the data into three parts: training, validation (or n-fold cross-validation from training), and testing. The testing data should be independent from the training and validation data, and the validation data should be used for parameter tuning (in this case, the alpha parameter).
The results “Prediction of Nomogram was Accurate” is not valid. The manuscript says “N, Age and RiskScore could be considered prognosis factor” because p-value is less than 0.05. However, p-value does not suggest anything about accuracy. Second, the authors did not specify, again, whether the AUCs are derived from training set or validation set.
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