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Both reviewers are satisfied with your revisions and believe the manuscript is now suitable for publication. Thank you for your careful attention to the reviewers' comments and suggestions.
[# PeerJ Staff Note - this decision was reviewed and approved by Paula Soares, a PeerJ Section Editor covering this Section #]
The author provided a detailed response to my question, and the manuscript met my expectations. I have no further comments.
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This study collected 1327 samples of lung adenocarcinoma (LUAD) from five datasets, including one training set (TCGA-LUAD) and four validation sets (GSE30219, GSE31210, GSE50081, GSE72094). Ten machine learning algorithms were used to identify immune derived gene features to improve the immune therapy response and clinical outcomes of LUAD. In addition, the stability of the immune prognostic features (IMMPS) model constructed in this study was compared with 154 existing prognostic gene features of LUAD proposed in the past five years, indicating that IMMPS has higher stability and lower detection costs compared to the previous 154 gene features. Overall, this study has certain innovation and scientific research value.
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Based on the reviewers' comments, we believe that your manuscript addresses an important topic and has merit. However, substantial revisions are needed before it can be considered for publication. Please carefully address all concerns raised by both reviewers, and provide a point-by-point response to all reviewers' comments along with your revised manuscript.
[# 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. #]
Main Comments:
1. For the acquisition of LUAD patient datasets, please provide the specific addresses of the datasets rather than the homepage URLs of the databases.
2. Experimental Verification in cell lines: There is a lack of validation experiments for gene knockout. It is necessary to validate the successful knockout of BTNL9 at both the genetic and protein levels in these cells.
3. In Fig7, are there no significant differences in the results of the three cell lines after BTNL9 knockout? If this is the intended message in the text, please polish the text and add necessary significance symbols in the figure.
4. The images in this manuscript are all very blurry and the text in the images is illegible. Please provide high-resolution images.
Additional Comments:
1. Add transfection sequences.
2. The images are blurry; please provide high-resolution images.
3. In Fig1c, what do the three groups represent? The images are blurry, and there is no description in the text. Please provide a detailed explanation.
4. The meaning of the gray and blue bar charts in Fig7 needs to be explained.
5. The English needs polishing. For example, "one of the most lethal and prevalent cancers in the world" is not formal enough; please revise.
6. All instruments and products must provide specific information in the format: Material/Instrument Name (Catalog Number, Company, City, Country).
7. All experimental group names and methods must be mentioned in the methods section.
8. All fonts in the figures should be uniform, using Times New Roman, and the font size should be consistent.
9. All result comparisons in the abstract must include p-values.
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This study collects 1327 lung adenocarcinoma (LUAD) samples from 5 datasets including one training set (TCGA-LUAD) and four validation sets (GSE30219, GSE31210, GSE50081, GSE72094) to identify immune-derived gene signature for improving the immunotherapy responses and clinical outcomes in LUAD through 10 machine learning algorithms. Furthermore, the stability of immune prognostic signature (IMMPS) model constructed in this study is compared with 154 existing prognostic gene features proposed in the last five years for LUAD, showing that the IMMPS have the advantage of higher stability but lower detection cost in comparison with the previous 154 gene signatures. Overall, this study has certain innovative and scientific research value. However, there are some shortcomings in the writing of this manuscript.
1. Several abbreviations in the manuscript lack definitions and full names, such as “ssGSEA”, “TIDE”, “IPS”, “TME”, “OS”, “Lasso”, “GSEA”, “AUC”. Please correct and supplement in accordance with the principle of first appearance.
2. There are many referential pronouns in the abstract, like “the latter”, “it exhibited..”, “these 7 genes”, the description of which seems vague. Hence, it is recommended to describe specifically. In addition, the name of 7 genes employed to establish IMMPS model is suggested to supplement into the abstract.
3. The keyword of “drug discovery” seems to be inappropriate since this current study does not involve drug discovery or drug prediction. Moreover, considering the main object of this research, it is suggested to add “lung adenocarcinoma” into the keywords.
4. In the introductory section, the involvement of TME immune infiltration in the prognosis and immunotherapy response of colorectal cancer and colon cancer is list, more literatures implicated in LUAD are encouraged to supplement.
5. The cohort of “TCGA-KIRC” in the title of Table 1 is incorrectly described, which is specifically for the study of Kidney Renal Clear Cell Carcinoma (KIRC) and is irrelevant to this study. Furthermore, what is “ARNT2 expression” in the title of Table 1?
6. In the Materials and methods section Lines 142-166 “Development of tumor-infiltrating immune-related gene markers”, it is suggested to split this section into two methodologies of WGCNA and Machine learning for presenting.
7. Lines 176-177 of the methodology need to clarify why BTNL9 is chosen for silencing to explore its effect on the expression of 7 signatures in LUAD cell lines. Besides, the specific operation process of cell transfection and the use of instruments require to be supplemented into the manuscript.
8. The table of PCR primers is not standardized, which needs to be presented as a three-line table.
9. In the Statistical analyses, the reference of the R package "timeROC" needs to be supplemented; Significance P-value definition is lack. Additionally, functional enrichment analysis (GSEA) including KEGG, GO (BP, MM, CC) is recommended to be presented separately rather than appearing in the statistical analysis section.
10. The meanings of the gray and blue columns in Figure 7 need to be clarified in the figure legend.
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