Characteristic genes and immune landscape of interstitial cystitis

Junhao Wang; Yan Zhou; Jie Hu; Jianpeng Han; Jianyong Feng; Kuo Guo; Wenbin Chen; Yanrui Yun; Yongzhang Li

doi:10.1371/journal.pone.0320249

Peer Review History

Original SubmissionMay 20, 2024
20 May 2024 Author Response https://doi.org/10.1371/journal.pone.0320249.r001
27 Dec 2024 Decision Letter - Abdul Rauf Shakoori, Editor Dear Dr. Li, Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process. Please submit your revised manuscript by Feb 10 2025 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org . When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file. A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'. 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Have the authors made all data underlying the findings in their manuscript fully available??> The PLOS Data policy Reviewer #1: No ****** 4. Is the manuscript presented in an intelligible fashion and written in standard English??> Reviewer #1: No ****** Reviewer #1: 1. Experimental Validation: While the manuscript successfully identifies three characteristic genes (CCL18, MMP10, and WIF1) for interstitial cystitis (IC) using machine learning, there is a lack of experimental validation. Experimental methods, such as qPCR, Western blotting, or immunohistochemistry, could confirm these genes' differential expression and diagnostic utility. Including at least one validation step would significantly strengthen the study. 2. Functional Analysis of Key Genes: The discussion regarding the biological relevance of CCL18, MMP10, and WIF1 is comprehensive. However, their functional roles in IC pathogenesis remain speculative. Please provide a deeper exploration of how these genes contribute to IC's immune landscape and their interaction with upregulated immune cells, supported by experimental or literature evidence. 3. Immune Landscape Analysis: The manuscript highlights the immune landscape of IC, with specific immune cell subtypes showing differential infiltration. The role of mast cells, regulatory T cells, and macrophages is well-documented in IC. However, linking these findings to therapeutic implications or pathways (e.g., targeting immune checkpoints or cytokines) would enhance the clinical impact of this analysis. 4. Small Sample Size: The study's conclusions are limited by the small sample size, as acknowledged by the authors. It would be valuable to discuss how this limitation might affect the reliability of the identified biomarkers and immune cell correlations. Consider suggesting future directions for validating findings with larger datasets or independent cohorts. 5. Machine Learning Models: The manuscript uses three machine learning algorithms (WGCNA, LASSO, SVM) to identify characteristic genes. While the multi-algorithm approach is commendable, the manuscript would benefit from explaining how the integration of these methods addresses potential biases or variability. Discuss the criteria for selecting overlapping genes and justify the robustness of the final results. 6. Figure Quality: - Font Size: Many figures (e.g., heatmaps, ROC curves) have text that is too small and difficult to read. Increasing font size would improve clarity. - Figure Legends: Ensure figure legends are detailed enough to be understood independently of the main text. 7. Clarity in Methods: - The description of ssGSEA and ESTIMATE algorithms is concise but would benefit from a brief explanation for readers unfamiliar with these techniques. - Specify the thresholds used in WGCNA and other machine learning algorithms, such as the rationale behind choosing \|Cor\| > 0.4. 8. Language and Style: - Minor grammatical errors and awkward phrasing are present throughout the manuscript. For example, "depict the immune landcape" can be revised to "characterize the immune landscape." - Consider a thorough language edit for improved readability and professionalism. 9. Literature Context: - Add comparisons with previous studies that have identified biomarkers or immune cell dynamics in IC. Discuss how the current findings align or differ from these studies. 10. Supplementary Information: - Ensure that the data and supplementary tables are adequately cross-referenced in the main text. ****** what does this mean? ). If published, this will include your full peer review and any attached files. If you choose “no”, your identity will remain anonymous but your review may still be made public. Do you want your identity to be public for this peer review? 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Please note that Supporting Information files do not need this step. https://doi.org/10.1371/journal.pone.0320249.r002
Revision 1
11 Feb 2025 Author Response Dear Editor, We deeply appreciate your constructive comments on our manuscript (Title: Characteristic Genes and Immune Landscape of Interstitial Cystitis; ID: PONE-D-24-19821). We have made corrections according to the comments, and the detailed point-by-point answers to the questions are listed below. In the revised manuscript, the revised portions are marked with red font. Thank you very much. During the manuscript revision process, Professor Yanrui Yun provided extensive guidance. In recognition of her contributions to this research work, we have added her as the co corresponding author of the article. Thank you once again for the suggestions and we hope that our revisions will meet with approval. We look forward to hearing from you. Sincerely yours, Yongzhang Li Department of urology, Hebei Provincial Hospital of Traditional Chinese medicine, 389 Zhongshan East Road, Shijiazhuang, Hebei Province, 050017 China Email:hbzylyz@126.com Phone: 08617532150046 Reviewer #1: 1. Experimental Validation: While the manuscript successfully identifies three characteristic genes (CCL18, MMP10, and WIF1) for interstitial cystitis (IC) using machine learning, there is a lack of experimental validation. Experimental methods, such as qPCR, Western blotting, or immunohistochemistry, could confirm these genes' differential expression and diagnostic utility. Including at least one validation step would significantly strengthen the study. Re: Thank you for your comments. We agree with you. The lack of experimental verification is one of the biggest defects of this paper. We are collecting samples, which may take a while, and we are trying to do a continuity study. In our discussion, we highlighted our shortcomings. 2. Functional Analysis of Key Genes: The discussion regarding the biological relevance of CCL18, MMP10, and WIF1 is comprehensive. However, their functional roles in IC pathogenesis remain speculative. Please provide a deeper exploration of how these genes contribute to IC's immune landscape and their interaction with upregulated immune cells, supported by experimental or literature evidence. Re: We sincerely appreciate your comments. The inability to elucidate the role of key genes in interstitial cystitis represents a limitation of this study, which we have emphasized in the discussion section. There is no existing literature that clarifies the relationship between CCL18, MMP10, and WIF1 with interstitial cystitis. We were unable to find support from previous studies. However, our research may have expanded the direction of interstitial cystitis studies. CCL18, MMP10, and WIF1 could potentially serve as biological markers, warranting further exploration. 3. Immune Landscape Analysis: The manuscript highlights the immune landscape of IC, with specific immune cell subtypes showing differential infiltration. The role of mast cells, regulatory T cells, and macrophages is well-documented in IC. However, linking these findings to therapeutic implications or pathways (e.g., targeting immune checkpoints or cytokines) would enhance the clinical impact of this analysis. Re: Thank you very much for your comments. We have made supplementary additions during our discussion, which are as follows: The roles of mast cells, regulatory T cells, and macrophages in interstitial cystitis (IC) have been well established. Several immune-related targets are currently under close scrutiny. Potential therapeutic strategies may include the inhibition of various cytokines, chemokines, growth factors, and mast cells. Certolizumab pegol and adalimumab have undergone clinical evaluation. Tanezumab and fulranumab have become subjects of research. Gene therapies with immunomodulatory properties may play a significant role in addressing diseases such as IC, although further exploration is still required. 4. Small Sample Size: The study's conclusions are limited by the small sample size, as acknowledged by the authors. It would be valuable to discuss how this limitation might affect the reliability of the identified biomarkers and immune cell correlations. Consider suggesting future directions for validating findings with larger datasets or independent cohorts. Re: Thank you for your comments. We concur with your perspective and have made additional clarifications in our discussion, which are as follows: The primary limitation of this study is the relatively small sample size, which precludes the acquisition of a more diverse set of samples, a situation that is indeed regrettable. This limitation is associated with the relatively low level of attention directed towards interstitial cystitis (IC). Additionally, the heterogeneity of the samples may have a catastrophic impact on the stability of the results. We recommend addressing the shortcomings associated with the small sample size from the following perspectives: expanding the sample size by further collecting samples from interstitial bladder studies for comprehensive transcriptomic sequencing; conducting multi-omics integrative analyses to validate conclusions across different omics; and performing foundational experiments to elucidate the causal relationships between genes and disease. 5. Machine Learning Models: The manuscript uses three machine learning algorithms (WGCNA, LASSO, SVM) to identify characteristic genes. While the multi-algorithm approach is commendable, the manuscript would benefit from explaining how the integration of these methods addresses potential biases or variability. Discuss the criteria for selecting overlapping genes and justify the robustness of the final results. Re: Thank you for your feedback; we wholeheartedly agree with your perspective. We have supplemented our discussion as follows: We selected the overlapping genes from three algorithms as key genes and validated the reliability of the results through differential analysis and ROC curve assessment. The combined application of the three machine learning algorithms effectively reduces overfitting, enhances model performance, and strengthens robustness. 6. Figure Quality: - Font Size: Many figures (e.g., heatmaps, ROC curves) have text that is too small and difficult to read. Increasing font size would improve clarity. - Figure Legends: Ensure figure legends are detailed enough to be understood independently of the main text. Re: Thank you very much for your feedback. We have adjusted the font size of the text in the images. We have also revised the legends to make them more comprehensive, allowing them to stand independently from the main text. 7. Clarity in Methods: - The description of ssGSEA and ESTIMATE algorithms is concise but would benefit from a brief explanation for readers unfamiliar with these techniques. - Specify the thresholds used in WGCNA and other machine learning algorithms, such as the rationale behind choosing \|Cor\| > 0.4. Re: Thank you for your suggestions; we have made additional clarifications. The ssGSEA algorithm (Single Sample Gene Set Enrichment Analysis) is a method utilized for gene set enrichment analysis, primarily aimed at assessing the enrichment level of a specific gene set within an individual sample. ssGSEA computes the enrichment score for each gene set by comparing the gene expression matrix with predefined gene sets. The ESTIMATE algorithm (Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression data) serves as a quantitative method to estimate the levels of stromal and immune cell infiltration within the tumor microenvironment, leveraging gene expression data from cancer tissues. This algorithm predicts the levels of stromal and immune cell infiltration in tumor tissues through a pre-selected set of stromal/immune-related genes and generates three principal scores: Stromal Score, Immune Score, and Estimate Score. The Stromal Score assesses the level of stromal cell infiltration in tumor tissues, the Immune Score evaluates the level of immune cell infiltration, while the Estimate Score, being the sum of the two, can be used to infer tumor purity. In the context of WGCNA (Weighted Gene Co-expression Network Analysis), the criteria for retaining modules are determined by \|Cor\| > 0.4 and p-value < 0.05. A correlation of \|Cor\| > 0.4 is considered relatively significant. If the correlation coefficient is too low, it indicates that the importance of the module is minimal. 8. Language and Style: - Minor grammatical errors and awkward phrasing are present throughout the manuscript. For example, "characterize the immune landscape" can be revised to "characterize the immune landscape." - Consider a thorough language edit for improved readability and professionalism. Re: Thank you for your comments. We have polished the language. 9. Literature Context: - Add comparisons with previous studies that have identified biomarkers or immune cell dynamics in IC. Discuss how the current findings align or differ from these studies. Reply: Thank you for your comments. We have supplemented the discussion as follows: This study differs from previous research in that the biomarkers we have identified have not been reported in the existing literature, making them worthy of further exploration. 10. Supplementary Information: - Ensure that the data and supplementary tables are adequately cross-referenced in the main text. Re: Thanks for your comments, we have provided supplementary tables. Attachments Attachment Submitted filename: Response to Reviewers.docx https://doi.org/10.1371/journal.pone.0320249.r003
17 Feb 2025 Decision Letter - Abdul Rauf Shakoori, Editor Characteristic Genes and Immune Landscape of Interstitial Cystitis. PONE-D-24-19821R1 Dear Dr. Li, We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements. Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication. An invoice will be generated when your article is formally accepted. Please note, if your institution has a publishing partnership with PLOS and your article meets the relevant criteria, all or part of your publication costs will be covered. Please make sure your user information is up-to-date by logging into Editorial Manager at Editorial Manager® and clicking the ‘Update My Information' link at the top of the page. If you have any questions relating to publication charges, please contact our Author Billing department directly at authorbilling@plos.org. If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org. Kind regards, Abdul Rauf Shakoori, PhD Academic Editor PLOS ONE Additional Editor Comments (optional): Reviewers' comments: https://doi.org/10.1371/journal.pone.0320249.r004
Formally Accepted
Acceptance Letter - Abdul Rauf Shakoori, Editor PONE-D-24-19821R1 PLOS ONE Dear Dr. Li, I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now being handed over to our production team. At this stage, our production department will prepare your paper for publication. This includes ensuring the following: * All references, tables, and figures are properly cited * All relevant supporting information is included in the manuscript submission, * There are no issues that prevent the paper from being properly typeset If revisions are needed, the production department will contact you directly to resolve them. If no revisions are needed, you will receive an email when the publication date has been set. At this time, we do not offer pre-publication proofs to authors during production of the accepted work. Please keep in mind that we are working through a large volume of accepted articles, so please give us a few weeks to review your paper and let you know the next and final steps. Lastly, if your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org. If we can help with anything else, please email us at customercare@plos.org. Thank you for submitting your work to PLOS ONE and supporting open access. Kind regards, PLOS ONE Editorial Office Staff on behalf of Prof. Dr. Abdul Rauf Shakoori Academic Editor PLOS ONE https://doi.org/10.1371/journal.pone.0320249.r005

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