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Dear Dr. Li,
Thank you for your submission to PeerJ.
I am writing to inform you that your manuscript - Study of LY9 as a Potential Biomarker for Prognosis and Prediction of Immunotherapy Efficacy in Lung Adenocarcinoma - has been Accepted for publication. Congratulations!
Warm regards,
Abhishek Tyagi
Academic Editor
PeerJ Life & Environment
[# PeerJ Staff Note - this decision was reviewed and approved by Paula Soares, a PeerJ Section Editor covering this Section #]
Please see Additional Comments.
Please see Additional Comments.
Please see Additional Comments.
Thank the authors for responding to all of my comments. The current version has been much improved.
Dear Dr. Li,
Thank you for your revised submission.
After careful review of the revised manuscript, the reviewer still raised some minor concerns. We kindly advise you to address those concerns and revise the manuscript again in a point-by-point manner.
Best regards,
Thanks
Abhishek Tyagi, PhD
Academic Editor
The authors mentioned they would upload the code they used with the manuscript. But I didn't find it. Please double-check.
no comment
no comment
All my other concerns have been addressed.
Please see Additional Comments.
Please see Additional Comments.
Please see Additional Comments.
Thank the authors for responding to my comments. However, the revised version and response did not seem to address the following issues thoroughly:
1. As to the previous comment 5.5 (In "3.4. Construction of the lncRNA-miRNA-LY9 ceRNA network", please explain the rationale of the notion that one miRNA has either a strong or weak correlation with LY9. In other words, how did the authors assess whether the correlation between a miRNA and a gene mRNA could be strong or weak?),
the authors explained how they calculate the correlation by citing a reference (PMID: 31743268). However, this reference was using an algorithm to establish a linkage between disease symptoms and prognosis. This seems not clear to me how this reference helped the authors work out the correlation between miRNAs and mRNAs. Thus, please explain thoroughly (via words, data, and examples) how they calculated this miRNA-RNA correlation, with a calculated "correlation values" and "significant difference".
2. As to the previous comment 4 (In MATERIALS AND METHODS ("2.3. Construction of the lncRNA-miRNA-LY9 ceRNA network"), please expand on how the authors used TargetScan 7.2 to filter the predicted upstream miRNAs of LY9 according to "correlation > 0.2 and p < 0.05" — two parameters that do not seem to exist in TargetScan. In addition, it would be more rigorous to mention that "LY9-related miRNAs" are miRNAs predicted to target LY-9.),
similar to the above, the authors did not answer clearly how they performed the "Spearman correlation analysis to obtain specific correlations and p-values". Please provide more details (via words, data, and examples) about how they did the calculation.
Dear Dr. Li,
Thank you for submitting your manuscript "Study of LY9 as a Potential Biomarker for Prognosis and Prediction of Immunotherapy Efficacy in Lung Adenocarcinoma" to PeerJ. We have now received reports from the reviewers, and after careful consideration internally, we have decided to invite a major revision of the manuscript.
As you will see from the reports copied below, the reviewers raise some concerns regarding the detailed methodology, the experimental validation, and the data availability. We find that these concerns limit the strength of the study, and therefore we ask you to address them with additional work. Without substantial revisions, we will be unlikely to send the paper back for review.
If you feel that you are able to comprehensively address the reviewers’ concerns, please provide a point-by-point response to these comments along with your revision. Please show all changes in the manuscript text file with track changes or color highlighting. If you are unable to address specific reviewer requests or find any points invalid, please explain why in the point-by-point response.
Thanks
Abhishek Tyagi, PhD
Academic Editor,
PeerJ
[# PeerJ Staff Note: Please ensure that all review and editorial comments are addressed in a response letter and any edits or clarifications mentioned in the letter are also inserted into the revised manuscript where appropriate. #]
[# 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 [email protected] for pricing (be sure to provide your manuscript number and title) #]
1. The article is predominantly clear and easy to understand. However, I would recommend revising lines 63-65 "Cervical squamous cell carcinoma (CESC) ..." as it appears somewhat perplexing.
2. I suggest supplementing more literature references for lines 65-66 "Moreover, tumor-infiltrating lymphocytes have been shown to ..." as the solitary reference employed is insufficient to bolster the background.
3. Considering that the public data was analyzed using multiple packages and statistical methods, could you please make the code accessible to the public? One way to do this is by sharing your code on Github or other public platforms.
4. In line 297, it should be "Fig. 7C and D", which show your IHC results.
1. Regarding the paper, the data mining of public databases seems adequate, but the validation experiment conducted is insufficient to substantiate the findings.
2. For point 3.4 "Construction of the lncRNA-miRNA-LY9 ceRNA network," I noticed that there is a lack of reasoning and logic behind this section. Merely presenting the data without justifying the need to explore the ceRNA network seems inadequate. It would be beneficial to provide a rationale for the exploration and highlight the potential implications of the constructed network.
1. Regarding Figure 7C, I noticed that the tumor samples yielded negative results for IHC experiments. To ensure the accuracy of the findings, it might be worthwhile to verify whether the antibody utilized in the experiment is suitable for IHC. You can consider testing it with positive control and also testing the LY9 protein using alternative methods such as Western blot. Additionally, it would be necessary to determine whether the LY9 antibody can bind to the LY9 protein by conducting a positive control experiment, such as using purified protein or LY9 overexpressing cell extraction. This would help strengthen the reliability of the results obtained from Figure 7C.
Please see Additional comments.
Please see Additional comments.
Please see Additional comments.
The authors combined bioinformatics and cell experiments to explore the role of LY9 in lung adenocarcinoma pathogenesis and prognosis. Overall, this study is suitable for publication, only if the authors address the following issues:
1. Throughout the manuscript, it seems better to use Grammarly (https://www.grammarly.com/) to check & correct potential grammatical errors. For example,
1.1 In Figure 1, it seems better to change "Exploring the KEGG pathways and functions involved in LY9" into "Exploring the KEGG pathways and functions enriched for LY9".
1.2 In the Background of ABSTRACT, it seems better to change "Finally, Validation of differential expression at the mRNA level" into "Finally, validation of differential expression at the mRNA level".
2. In all FIGURES, it would be clearer and more readable to expand on figure legends by explaining the meanings of colors, groups, lines, and abbreviations. For example,
2.1 In the legend of Figure 1A, it seems better to explain the meaning of "Significant Unique Analyses" (in other words, whether the numbers signal how many cancer datasets LY9 expression level was significantly up- or downregulated in); in addition, it seems more accurate to put "*p < 0.05, **p < 0.01, ***p < 0.001" in the legend of Figure 1C rather than Figure 1A, because Figure 1A did not have asterisks.
2.2 In the legend of Figure 1B, it seems more readable to explain the meaning of the dots and black lines (see how all elements of a box blot were well explained by an exemplary paper: PMID_27518660). In addition, it would be easier to understand to provide the full names of at least those colorful (risky or protective) cancer abbreviations. Furthermore, it could be more understandable to use red or green to highlight the abbreviations of cancer names, in which LY9 was risky or protective.
2.3 Similar to 2.2, in the legend of Figure 1C, it would be easier to understand to provide the full names of at least those cancer abbreviations with statistically significant results. Furthermore, it could be more understandable to highlight the abbreviations of cancer names, in which LY9 was significantly up- or down-regulated.
2.4 In the legend of Figure 3, it would be more readable to give full names of the cancer abbreviations.
2.5 In the legend of Figure 3A, it seems better to change "Association of LY9 expression in the Kaplan-Meier plotter database with the overall survival in patients with six tumor types" into "Association of LY9 expression in the Kaplan-Meier plotter database with the overall survival in patients with six tumor types in the Kaplan-Meier plotter database", which would be parallel to the sentence after it ("... in the GEPIA database"). In addition, it would be more readable to explain the meaning of numbers in color ("low" in black and "high" in red) below the figures.
2.6 In the legend of Figure 3B, it would be more understandable to explain what the red and green dotted lines mean.
2.7 In the legend of Figure 4, it would be more accurate to change "Correlation analyses of clinical characteristics and Gene Set Enrichment Analysis (GSEA) in LUAD from the TCGA dataset" into "Correlation of LY9 expression with clinical characteristics and Gene Set Enrichment Analysis (GSEA) of LUAD-derived differentially expressed genes based on the TCGA dataset". Furthermore, please refine this legend as accurately as possible according to the results in the figure. In addition, it would be more rigorous to state clearly the sample size included in each panel.
2.8 In the legend of Figure 4A, it seems better to change "LY9 expression in LUAD" into "LY9 expression in LUAD and normal tissues", which would be more accurate.
2.9 In the legend of Figure 4B, it seems better to change "Paired diûerential expression analysis of LY9 in LUAD" into "Paired diûerential expression analysis of LY9 between LUAD and normal tissues", which would be more accurate.
2.10 In the legend of Figure 4C, it seems better to change "KM curves of LY9 expression and survival in LUAD" into "KM survival curves of LUAD patients with high and low expression of LY9", which would be more accurate.
2.11 The legends of Figure 4D–L seem confusing, because some legends did not appear to correspond with the figure panels. Please double check the correspondence between them.
2.12 In the legend of Figure 4J–K, it would be more readable to explain the meaning of red & green dots and black lines.
2.13 In Figure L, some lines seem hardly distinguishable, so it would be more readable to split the figure into several panels. In addition, please expand on the figure's legend, instead of just saying "Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways of GSEA in LUAD". Also, it seems clearer to delete "KEGG_" at the beginning of every term.
2.14 In the legends of Figures 5A, 5D–E, and 5H–I, it seems better to explain the meaning of black dots, blue lines, and grey shadows.
2.15 In the legends of Figures 6E–K, it seems better to explain the meaning of black dots, blue lines, and grey shadows.
2.16 In the legends of Figures 7A–B, it would be more rigorous to mention the sample size.
2.17 In Figures 7C–D, it would be more readable to add arrows to point out areas with positive stainings.
These revisions would greatly help readers, who do not specialize in bioinformatics, to understand the results and their implications easily and efficiently.
3. In ABSTRACT:
3.1 In Results, it would be more rigorous and accurate to rewrite "The expression of LY9 in LUAD promotes the infiltration of multiple immune cells", because the authors did not seem to offer the evidence from experiments (those confirming that LY9 overexpression can promote "the infiltration of multiple immune cells").
4. In MATERIALS AND METHODS ("2.3. Construction of the lncRNA-miRNA-LY9 ceRNA network"), please expand on how the authors used TargetScan 7.2 to filter the predicted upstream miRNAs of LY9 according to "correlation > 0.2 and p < 0.05" — two parameters that do not seem to exist in TargetScan. In addition, it would be more rigorous to mention that "LY9-related miRNAs" are miRNAs predicted to target LY-9.
5. In RESULTS:
5.1 It would be clearer to end each paragraph in RESULTS with one sentence: "Together, these results suggest that ..." (a pattern like PMID: 34715879, PMID: 34384362, PMID: 35965679, and PMID: 34537192), summarizing a paragraph AND highlighting the implications of all results in the paragraph.
5.2 In "The mRNA expression levels of LY9 in pan-cancer", it seems better to change The risk volcano map results showed that LY9 was a risk factor for Uveal Melanoma (UVM). Meanwhile, LY9 can play a protective role in SKCM, SARC, LUAD, HNSC, CESC, BRCA, and ACC" into "The risk volcano map results showed that LY9 could be a risk factor for Uveal Melanoma (UVM). Meanwhile, LY9 may play a protective role in SKCM, SARC, LUAD, HNSC, CESC, BRCA, and ACC", which would be more rigorous.
5.3 In both "The mRNA expression levels of LY9 in pan-cancer" and "Prognostic value of LY9 in pan-cancer", it would be more readable to mention the full names of cancer abbreviations.
5.4 In "3.4. Construction of the lncRNA-miRNA-LY9 ceRNA network", it seems better to change "Results showed that seven miRNAs, including hsa-miR-141-3p, hsa-miR-4746-5p, hsa-miR-151a-5p, hsa-miR-17-3p, hsa-miR-301b5p, hsa-miR-4786-3p, and hsa-miR-629-5p might be potential targets for LY9" into "Results showed that seven miRNAs, including hsa-miR-141-3p, hsa-miR-4746-5p, hsa-miR-151a-5p, hsa-miR-17-3p, hsa-miR-301b5p, hsa-miR-4786-3p, and hsa-miR-629-5p were predicted to target LY9", which would be more accurate.
5.5 In "3.4. Construction of the lncRNA-miRNA-LY9 ceRNA network", please explain the rationale of the notion that one miRNA has either a strong or weak correlation with LY9. In other words, how did the authors assess whether the correlation between a miRNA and a gene mRNA could be strong or weak?
5.6 In "3.4. Construction of the lncRNA-miRNA-LY9 ceRNA network", it would be clearer to explain that "hsa-miR-141-3p-related lncRNAs" are lncRNAs predicted to bind hsa-miR-141-3p.
5.7 In "3.6. Validation of GEO dataset, qRT-PCR and IHC experiments", it seems better to change the title into "3.6. Validation using GEO dataset, qRT-PCR and IHC experiments", which would be more accurate.
5.8 In "3.6. Validation of GEO dataset, qRT-PCR and IHC experiments", it seems better to delete "Therefore, the conclusions of this study were based only on the gene expression level of LY9" or change it into "Therefore, the conclusions of this study were based only on the mRNA level of LY9."
6. In CONCLUSION, it seems better to change "Overall, our results suggested that LY9 can be used as a novel tumor biomarker to assess the prognosis of LUAD patients, as well as to evaluate the sensitivity of immunotherapy, and as a potential therapeutic target for immunotherapy" into "Overall, our results suggested that LY9 could be used as a novel tumor biomarker to assess the prognosis of LUAD patients as well as the sensitivity of immunotherapy and as a potential therapeutic target for immunotherapy", which would be more rigorous and clearer.
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