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Dear Dr. Zhu,
Happy New Year!
All the three reviewers have recommended and it is my pleasure to accept your revised article.
Thank you,
Best regards,
Debmalya Barh, PhD
[# PeerJ Staff Note - this decision was reviewed and approved by Paula Soares, a PeerJ Section Editor covering this Section #]
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Authors have provided a logical and satisfactory point-by-point explanation for the concerns raised during review process. I think that manuscript has improved substantially and can be considered for publication.
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My previous comments have been addressed satisfactorily.
Dear Dr. Zhu,
Thank you for your submission to PeerJ.
The review comments are now in hand. A few major concerns have been raised by the reviewers and as per my opinion these points are very important.
Therefore, please revise the paper as per the suggestions of the reviewers and resubmit for further processing.
Thank you,
Best regards,
Debmalya Barh, PhD
[# PeerJ Staff Note: Please ensure that all review comments are addressed in a rebuttal letter and any edits or clarifications mentioned in the letter are also inserted into the revised manuscript where appropriate. It is a common mistake to address reviewer questions in the rebuttal letter but not in the revised manuscript. If a reviewer raised a question then your readers will probably have the same question so you should ensure that the manuscript can stand alone without the rebuttal letter. Directions on how to prepare a rebuttal letter can be found at: https://peerj.com/benefits/academic-rebuttal-letters/ #]
[# PeerJ Staff Note: The review process has identified that the English language must be improved. PeerJ can provide language editing services - please contact us at copyediting@peerj.com for pricing (be sure to provide your manuscript number and title) #]
There are some minor grammatical errors, such as the lack of "of" in "expression mRNAn" on line 92, etc.
no comment
In the GEO cohort, compared with other clinical indicators, the "AUC" of "the risk score" is not very good. The author should discuss the possible reasons.
GSEA is used to analyze all possible KEGGs of genes in the model. It is recommended that the author add GO and KEGG analysis to the differential genes of "the high-risk group" and "the low-risk group".
The manuscript by Yuan Nie et al. demonstrates novel prognostic markers which help in early diagnosis in Stomach adenocarcinoma (STAD). Using databases from TCGA authors have established prognostic model systems and validated by GSE84437 cohort. Manuscript is properly referenced and data is professionally tabulated. However, manuscript have grammatical and spelling mistakes at places. It would be useful if manuscript can be prrofread by a native speaker before resubmission
1. Authors have concluded their entire findings based on TCGA and GEO databases only. It would have been more tempting to include other cancer data repository e.g Oncomine ,Bioportal etc.
2. In addition to univariate Cox regression author can also use survival trees (e.g.CART for survival data) analysis which will further support the finding.
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The manuscript by Yuan Nie et al. demonstrates novel prognostic markers which help in early diagnosis in Stomach adenocarcinoma (STAD). Using databases from TCGA authors have established prognostic model systems and validated by GSE84437 cohort. I find manuscript can be considered for publication in PeerJ with addressing a few following minor comments.
1. The authors have concluded their entire findings based on TCGA and GEO databases only. It would have been more tempting to include other cancer data repository e.g Oncomine ,Bioportal etc.
2. In addition to univariate Cox regression author can also use survival trees (e.g.CART for survival data) analysis which will further support the finding.
3. Manuscript have grammatical and spelling mistakes at places. It would be useful if manuscript can be prrofread by a native speaker before resubmission.
The authors proposed a bioinformatics study to identify a metabolic-related gene signature
predicting the overall survival of patients with stomach adenocarcinoma. Some efforts have been done, however, there are some major points that need to be improved:
- The use of English should be improved significantly. There are some grammatical errors and typos. It is better to be checked by a native speaker or an English editing service.
- Lacking literature review. The authors missed to mention and discuss a lot of related bioinformatics studies on stomach adenocarcinoma specifically.
- Quality of figures should be improved.
- Did the authors concern about the batch effect removal in their study?
- TCGA & GEO have been used in previous bioinformatics studies such as https://doi.org/10.1016/j.meomic.2020.100001 and https://doi.org/10.3390/jpm10030128. Therefore, the authors should mention more works to attract a broader readership.
- The authors should release source codes for replicating the methods.
- Why did the authors select median values as the cut-off for risk score?
- Performance results were not so good (about AUC of 0.57 in validation set)
- I cannot see the legends in the heatmap, it is important to improve the quality of figures.
- How did the authors calculate the risk-scores for four-gene signatures together? As I have known, it is easier to calculate risk-score for individual genes.
- ROC curve or AUC has been used in previous bioinformatics studies i.e., PMID: 31362508 and PMID: 31921391. It is suggested to refer to more works in this description.
- In Fig. 5, it is better to use the same color for the same characteristic among TCGA and GEO set. It is not easy to compare with different color use.
- In Fig. 5 also, why did GEO dataset have fewer characteristics (fewer lines) than TCGA?
- There is overfitting on the results (AUC of 0.696 and 0.574 in training and validation set). The authors should discuss how to deal with it.
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