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The manuscript is acceptable at this stage.
[# PeerJ Staff Note - this decision was reviewed and approved by Fanglin Guan, a PeerJ Section Editor covering this Section #]
I suggest authors to make sure the additional corrections/comments from the reviewer addressed in the final version
No comment.
No comment.
No comment.
No comment.
Most of the comments have been addressed; however, some minor revisions are still required
Figure 2B: The x-axis labels appear to be identical. Please correct them and clearly distinguish between TU212HPV and TU212CON.
Figure 5: Since the p-values were not significant, it is risky to assess that ‘we discovered that the expressions of CDC42, FGF2, ICAM1 and GAPDH genes were higher than those in both HPV-negative and normal tissues’ (line 339-340). Replace the sentence like ‘Although the p-values were not statistically significant, we observed a trend toward higher expression levels of the CDC42, FGF2, ICAM1, and GAPDH genes compared to both HPV-negative and normal tissues.’
As references are generally not included in abstracts, I suggest removing the citation of DAVID from the abstract and instead incorporating it into the Methods section (‘Functional analysis of differentially expressed genes’).
I want to add a clarification to the following comment I made: ‘While DESeq2 was used to identify differentially expressed genes (DEGs), the method used to identify differentially expressed proteins (DEPs) is not specified. Please clarify which statistical test was applied for protein data and whether multiple testing correction (e.g., False Discovery Rate, FDR) was performed’.
My question was to specify which statistical test or tool was used to calculate p-values for the protein data (e.g. t-test, Wilcoxon-test?). Additionally, as another reviewer also pointed out, the identification of DEPs (lines 179–180) appears to be based solely on p-values, with no mention of multiple testing correction (e.g., False Discovery Rate, FDR). Please explain why multiple testing correction was not applied to identify DEPs.
no comment
I suggest the authors to go through all the comments from the reviewers and address them in the revised version of the manuscript
**PeerJ Staff Note:** Please ensure that all review, editorial, and staff comments are addressed in a response letter and that any edits or clarifications mentioned in the letter are also inserted into the revised manuscript where appropriate.
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1.Please incorporate a detailed discussion of this research limitations, along with specific recommendations and future research directions, in the discussion section.
2.The authors should clearly explain in the introduction why only HPV E7 was selected for transfection instead of co-transfecting both E6 and E7.
3.The current figure7 legend does not clearly indicate that the data originated from the TCGA database, potentially misleading readers to assume that the validation results were derived from the cell line experiments performed in the present study. Additionally, the legend lacks critical methodological details, including sample type (e.g., tumor vs. normal tissue), sample sizes, and analytical methods (e.g., bioinformatics tools, statistical approaches). Furthermore, there is insufficient interpretation of results, as the legend does not clearly describe the expression trends (upregulated or downregulated) or statistical significance (such as p-values or statistical methods).
Patient Cohort: Provide detailed demographic and clinical characteristics (e.g., age, tumor stage, smoking status) between HPV-positive and HPV-negative patients to assess potential confounders and enable subgroup analyses.
Cell Line Model: Given the potential for contamination in the TU212 cell line, please provide the STR profiling results. . Address this limitation in the discussion and consider including additional cell lines.
Statistical analysis: It is recommended to perform an integrated correlation analysis between proteomic and transcriptomic datasets to further elucidate the relationship between protein and gene expression levels.
Statistical analysis: It is recommended to perform an integrated correlation analysis between proteomic and transcriptomic datasets to further elucidate the relationship between protein and gene expression levels.
It is recommended to quantitatively validate the differential expression of CDC42, PXN, and PGK1 in an independent patient cohort (e.g., HPV-positive vs. HPV-negative tissues), using techniques such as qPCR. Additionally, functional validation experiments—including knockdown or overexpression assays and evaluation of cell proliferation, migration, and invasion—should be conducted to further elucidate the biological roles of these genes. Furthermore, the authors should clearly indicate where the RNA-seq and proteomics data from this study have been made publicly available, which would help other researchers easily access the data and verify the study's findings.
No comment.
The manuscript is generally well-written in professional English. However, some sentences are overly complex or awkwardly phrased (e.g., "PCR-reverse dot blot detected HPV genotypes" → "PCR-reverse dot blot was used to detect HPV genotypes"). Minor grammatical revisions and sentence restructuring are recommended for improved readability. The manuscript adheres to standard scientific structure. However, figure legends (e.g., Figure 1) lack sufficient detail.
1. The methods do not mention the authentication of the TU212 cell line. Include this detail to ensure reproducibility.
2. The fold-change threshold for differential proteins (1.2,0.84) is unusually low for proteomics studies. Justify this choice or consider using a higher threshold for technical variability. And the proteomics analysis uses a p-value cutoff of <0.05 but does not mention correction for multiple testing (e.g., FDR). Address this to strengthen statistical rigor.
The high HPV prevalence (55.65%) in Northwest China is notable. However, the introduction could better contextualize how this regional focus adds to global HPV-LSCC literature. Compare findings explicitly with similar studies from other regions.
This study offers valuable insights into HPV-associated LSCC in a high-prevalence region and identifies potential biomarkers. With minor revisions to address methodological and presentational issues, the manuscript will be suitable for publication.
Background
In the introduction, the authors assert that Human Papillomavirus (HPV) plays a crucial role in the development of laryngeal cancer and that a better understanding of the underlying biological mechanisms is needed. To investigate the pathogenesis of HPV-positive laryngeal cancer, they employ transcriptomic and proteomic approaches. However, the manuscript does not reference any previous studies on HPV-associated head and neck cancers, particularly HPV-positive laryngeal squamous cell carcinoma. The authors should review and cite relevant prior literature to position their study within the existing research landscape. This would help clarify the novelty and significance of their findings and explain how their work addresses gaps in the current understanding of HPV-related laryngeal squamous cell carcinoma.
Raw data
The current submission does not provide all the necessary raw data. To ensure transparency and reproducibility, the authors should make the full gene and protein expression datasets publicly available. If file size is a limitation, the data can be uploaded to a public repository such as Zenodo (https://zenodo.org), with the corresponding links cited in the manuscript.
Figures and Tables
The figures are generally relevant to the content of the article, though several require improvements for clarity and accuracy:
• Figure 1: There appears to be a discrepancy in the number of patients. While the figure title and corresponding text indicate a total of 115 patients, the sum of the bar values is 119. This inconsistency should be reviewed and corrected, both in the figure and throughout the text, including the abstract. Additionally, the abstract states that there are 64 HPV-positive patients, whereas the figure shows 68. Please resolve this discrepancy. Specify in the figure caption which tool or software was used to generate the barplot, and replace the term ‘people’ with ‘patients’in both the figure title and y-axis label.
• Figure 2b: The x-axis label for ‘TU212CON’ is missing—please add it. Also, indicate which tool or software was used to generate this figure in the caption.
• Figure 3A: Correct the typographical error: ‘doen’ should be ‘down’.
• Figure 4A: The selected thresholds for fold change (>1.2 and <0.83) correspond to log2-scale values of approximately 0.263 and -0.269, respectively. However, the volcano plot indicates a cutoff at -0.263, which is inconsistent with the calculated value. Please clarify and correct this inconsistency in the figure and its description.
• Figure 4D–E: These panels present KEGG analysis results for differentially expressed proteins with elevated expression. However, it is unclear why the results are split into two separate plots featuring different pathways. A single, combined barplot would enhance clarity and improve interpretation.
• Figure 5: This figure illustrates the expression distribution of ten genes across three groups (normal, HPV+, HPV−) using boxplots. However, the statistical significance of group comparisons is not provided. Please include p-values for each comparison directly on the plot.
• Figure 6: To improve readability and interpretation, add the gene name as a title above each boxplot. If feasible, include a legend to clarify the meaning of each color. If not, provide a clear explanation in the caption.
• Figure 7: Inconsistency is noted in the survival analysis approach. For most genes, analysis is based on expression level, whereas panel A includes stratification by both expression level and race (for CDC42). To maintain consistency, consider replacing panel A with an analysis based solely on gene expression. Additionally, increase the label font size to enhance figure readability.
• Table 1: Although Table 1 is referenced in the main text (line 318), it is missing from both the main manuscript and the supplementary materials. Please ensure it is included in the appropriate section.
• Supplementary Materials: The manuscript references supplementary materials, but they are not cited or discussed in the main text. Incorporate references to the supplementary materials where appropriate within the manuscript.
Language
Ensure consistent use of the first-person plural ("we") throughout the manuscript. In paragraph 4, several sentences use the first-person singular ("I"). These should be revised to maintain a consistent narrative voice.
All acronyms should be defined in full the first time they appear in the text. For example:
• Revise line 178 to: “Functional enrichment analyses, including Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG), were performed to…”
• Revise line 184 to: “The KEGG database is a publicly available resource for pathway data…”
Some acronyms are introduced but not used elsewhere in the manuscript. For instance, "RROSCC" appears in line 374 but is never used again. Acronyms should only be included if they are referenced subsequently in the text; otherwise, they should be omitted to avoid confusion.
The research question—identifying biomarkers of HPV-related laryngeal cancer—is well-defined and relevant. However, several aspects of the methodology require clarification or improvement:
- The authors state: “ProteomeDiscoverer (v.2.4) was used to search all of the raw data thoroughly against the Uniprot Mus Musculus database.” Given that TU212 is a human cell line, it is unclear why the Homo sapiens Uniprot database was not used. Please clarify or correct this point.
- The manuscript does not report the total number of genes and proteins detected. This information is essential to understand the depth of the data and should be included.
- While DESeq2 was used to identify differentially expressed genes (DEGs), the method used to identify differentially expressed proteins (DEPs) is not specified. Please clarify which statistical test was applied for protein data and whether multiple testing correction (e.g., False Discovery Rate, FDR) was performed.
- The identification of 11 key genes appears to be primarily based on GO and KEGG enrichment analysis of highly expressed proteins. Since transcriptomic data were also generated, it would be valuable to examine potential overlaps or correlations between DEGs and DEPs. An integrative analysis is currently lacking. Consider quantifying the intersection between upregulated/downregulated genes and highly/lowly expressed proteins using a Venn diagram or other visual summary.
- GO and KEGG analyses were performed using all DEGs (without separating up- and down-regulated genes), while DEPs were analyzed separately (highly and lowly expressed). To ensure consistency and interpretability, it is recommended to perform the GO and KEGG analyses for DEGs using the same approach—i.e., separating up- and down-regulated genes—and to compare the results for shared or distinct pathway enrichments.
- In paragraph 4.4, the authors state: “Then, the obtained PPI network was re-screened to eliminate nodes with values less than the average BC, average CC, and average degree value.” Please clarify what BC and CC refer to—presumably betweenness centrality (BC) and closeness centrality (CC)—and briefly define these terms to improve reader understanding.
- Please provide proper citations for the tools and software used in the analysis. For example: DESeq (DOI: 10.1186/s13059-014-0550-8) and DAVID (doi:10.1093/nar/gkac194).
I recommend uploading the complete DESeq2 output for differentially expressed genes (DEGs), including all relevant statistical parameters (e.g., p-values, adjusted p-values/FDR, and fold changes). Similarly, the results for differentially expressed proteins (DEPs) should be shared with the same level of detail. In addition, the complete outputs of the DAVID and KEGG pathway analyses for both gene and protein datasets should be provided.
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