-->PONE-D-26-09632-->-->Identifying key psoriasis driver genes and constructing targeted therapeutic strategies based on single-cell RNA-seq and machine learning-->-->PLOS One

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Reviewers' comments:

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1. Is the manuscript technically sound, and do the data support the conclusions?

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Reviewer #1: Yes

Reviewer #2: Partly

Reviewer #3: Yes

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-->2. Has the statistical analysis been performed appropriately and rigorously? -->

Reviewer #1: Yes

Reviewer #2: No

Reviewer #3: Yes

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-->3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.-->

Reviewer #1: Yes

Reviewer #2: No

Reviewer #3: No

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Reviewer #1: Yes

Reviewer #2: No

Reviewer #3: No

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-->5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)-->

Reviewer #1: This manuscript has several strengths. It addresses a clinically relevant topic by investigating potential driver genes in psoriasis and their possible therapeutic implications. The integration of multiple scRNA-seq and bulk RNA-seq datasets adds value to the study and may improve the robustness of the analysis. The overall workflow is also reasonably comprehensive, incorporating single-cell analysis, deconvolution, WGCNA, machine learning, cross-dataset validation, pathway analysis, and drug-target prediction. In addition, the study provides a relatively detailed view of cellular heterogeneity in psoriasis and links the identified genes to specific cell populations, particularly basal cells. The use of public datasets and the availability of the analysis code further support the transparency and reproducibility of the work.

However, the following three issues should be noted.

1. The threshold stated in the original text, "each cell needed to contain at least 10 features,"seems somewhat lenient.

2. The criterion "each feature needed to be expressed in at least 300 cells" seems rather stringent for retaining low-abundance yet biologically meaningful genes.

3. The authors classified neutrophils under "lymphoid lineage," while B cells, NK cells, and NKT cells appeared under “myeloid lineage.”They explained this using “The explanation that these populations were ‘sandwiched between’ clusters and that this was hypothesized to result from cell-cell interactions,” but this interpretation seems somewhat subjective.

Reviewer #2: This study integrates multiple public single-cell RNA-seq and bulk RNA-seq datasets to identify key driver genes in psoriasis. Using WGCNA and machine learning (SVM and RFC), the authors identified four genes—DEFB4A, GJB2, SERPINB3, and SERPINB13—as potential therapeutic targets, followed by molecular docking to screen for small molecule compounds. The topic has translational potential and the four identified genes have prior literature support. However, the manuscript suffers from logical gaps, insufficient methodological transparency, overinterpretation of preliminary findings, and language issues that compromise reproducibility and clarity.

Recommendation: Major Revision

Major Issues

1. The role and identification of “basal cells” are not adequately established

The abstract and results emphasize a correlation between hub genes and basal cells, yet basal cells are not introduced in the Background, nor is their functional relevance to psoriasis explained. The Methods do not specify how basal cells were defined or isolated from the scRNA-seq data (e.g., which marker genes were used). Without this foundation, the reader cannot assess why basal cells are central to the analysis or whether the identification was appropriate.

What needs to be addressed: The authors must clarify the biological rationale for focusing on basal cells in the Background, and provide a clear, reproducible definition of how basal cells were identified in the scRNA-seq dataset.

2. The filtering process from WGCNA hub genes to machine learning input is not transparent

The Methods state that 271 hub genes were identified by WGCNA (MM > 0.8, GS > 0.2). The Results then mention that these were intersected with “basal cell differential genes” to obtain 74 genes used for machine learning. This critical intermediate step is missing from the Methods section, making the analysis workflow incomplete and non-reproducible.

What needs to be addressed: The authors must describe in the Methods how the 74-gene set was derived, including the criteria used to define “basal cell differential genes” (e.g., fold-change and significance thresholds). The complete analytical pipeline should be transparent.

3. The selection of the four key driver genes lacks justification

The manuscript states that the top 30 features from each of the four machine learning models were intersected to yield four genes. However, no rationale is provided for choosing “top 30” as the cutoff. Moreover, the importance or weight values for the four genes are not reported, leaving the reader unable to evaluate whether these genes were consistently top-ranked across models or whether the cutoff was arbitrary.

What needs to be addressed: The authors should justify the feature selection threshold (e.g., by showing cumulative importance or stability across cross-validation folds) and provide the specific importance/weight values for the four genes across all models. This will allow assessment of the robustness of the selection.

4. The molecular docking findings are overinterpreted as validated therapeutic strategies

The docking results are presented as if they constitute validated therapeutic strategies, but several compounds have weak clinical evidence (e.g., glutathione, lactic acid) or marginal binding energy (e.g., -1.48 kcal/mol for lactic acid). While cyclosporine is a known psoriasis treatment, its docking with SERPINB3 does not in itself establish a new therapeutic approach. Presenting these preliminary in silico findings as “targeted therapeutic strategies” overstates the conclusions.

What needs to be addressed: The conclusions must be revised to reflect the preliminary nature of the docking results. The authors should clearly differentiate between well-established drugs (e.g., cyclosporine) and speculative candidates, and avoid claiming therapeutic strategies without experimental validation.

5. Multiple testing correction is not addressed

Multiple comparisons are reported throughout (e.g., differential gene expression, pathway enrichment) using nominal P-values without mention of correction for false discovery rate. This raises concerns about inflated type I error rates.

What needs to be addressed: The authors must specify in the Statistical Analysis section whether and how multiple testing was corrected (e.g., Benjamini-Hochberg). If corrections were applied, adjusted P-values should be reported; if not, the authors should explain why and acknowledge this limitation.

Minor Issues

6. Writing quality and formatting consistency

The manuscript contains non-standard terminology, informal phrasing, and inconsistent formatting (e.g., figure citation styles, italicization of P-values). These issues detract from readability and professionalism.

What needs to be addressed: The authors should engage a native English speaker or professional editing service to improve language clarity and scientific tone. Formatting (figure references, statistical notation) should be standardized throughout.

7. Data availability statement requires revision

The statement includes “or available from the corresponding author upon request,” which is not consistent with PLOS’s open data policy. All data used are from public GEO datasets.

What needs to be addressed: The data availability statement should be revised to state that all data are publicly accessible, with accession numbers provided, and remove the “upon request” option.

Reviewer #3: This study brings together multiple cohorts and combines single-cell analysis, deconvolution, WGCNA, machine learning, and functional enrichment to identify psoriasis-associated candidate genes and explore their possible biological relevance. There is clearly a substantial amount of work here, and the overall analytical framework is fairly complete. One of the stronger parts of the paper is the use of WGCNA to connect the cell-related findings with the later gene-prioritization steps, which helps the different parts of the analysis hold together. The study therefore provides some potentially useful information on psoriasis-associated genes. Even so, there are still several concerns about methodological rigor, clarity of presentation, and the extent to which some of the interpretations are supported by the data. For this reason, I think the manuscript would need major revision before further consideration.

1 The scRNA-seq QC thresholds need to be checked more carefully. The requirement that each cell contain at least 10 features is far below. It is usually used in single-cell quality control and would not effectively remove low-quality cells. At the same time, requiring each feature to be expressed in at least 300 cells seems unusually stringent and could remove subpopulation-specific genes or markers from rare cell types. These two thresholds are difficult to understand and may reflect either incorrect parameter reporting or insufficient methodological description. A clearer explanation of the actual settings and the rationale for them would be helpful.

2 The construction of the CIBERSORT reference matrix is not entirely clear. The phrase “Top50 differential genes for each cell subpopulation in psoriasis and healthy samples” can be read in more than one way, and it is hard to tell whether these genes are intended to be cell-type-specific signature genes or genes identified by comparing psoriasis and healthy samples within each cell subpopulation. If the reference was indeed based on within-cell-subpopulation psoriasis-versus-healthy differential genes rather than conventional cell-type-specific signatures, this should probably be acknowledged in the limitations, because such a strategy could mix disease-state signals into the deconvolution and make the estimated cell fractions harder to interpret.

3 In the further subdivision of lymphoid and myeloid cells, several apparently cross-lineage cell types were reported. The manuscript mainly attributes this to lineage-feature mixing caused by cell-cell interactions, but I am not sure the current evidence is sufficient to support such a large interpretive leap. Annotation accuracy, subclustering purity, and the possibility of doublets seem equally important here. At minimum, this issue should be discussed more cautiously, and some uncertainty in these subtype annotations should be acknowledged .

4 The integrated single-cell cohort includes both tissue-derived and peripheral-blood-derived samples.It naturally differ quite substantially in cellular composition. Similar designs do exist in the literature, so this is not inherently unreasonable, but the manuscript does not fully explain whether downstream analyses were stratified by sample source or otherwise controlled for it. Because this kind of heterogeneity could affect single-cell integration, cell-composition comparisons, and later interpretation, it would be better to state this more explicitly, at least in the limitations.

5 There also seems to be an unedited residual sentence in the Methods section on cell annotation, for example “method for cellular annotation of cell clusters.” This should simply be cleaned up.

6 The machine-learning workflow would benefit from a more explicit description. The manuscript mentions train/validation split, grid search, and cross-validation, but it is still not clear whether all of these steps were carried out strictly within the training set. It would be useful to state clearly whether the 74 candidate genes were selected only within the training data, whether grid search and 10-fold cross-validation were restricted to the training set, and whether extraction of the top 30 features was fully isolated from the validation set. If not, that should at least be acknowledged as a limitation.

7 The sentence “extremely strong predictive performance, which is closely related to the importance and weight of the traits” does not read like a standard machine-learning interpretation. The current wording makes the high AUC sound as though it can be directly explained by the “importance and weight of the traits,” which is not very precise. It would read more naturally to say that the selected gene features showed strong discriminatory power.

8 The phrase “gene inlinearity” in the WGCNA section is unusual and likely to confuse readers. Something like “based on gene co-expression patterns” or “based on gene expression correlations” would be much clearer.

9 The pseudotime interpretation for basal cells also feels a little too strong in places. What the current results support more directly is that these key genes show dynamic expression across different differentiation stages. That is not quite the same as showing that they “play a regulatory role” or are specifically related to basal-cell development or senescence. Since this is not the central focus of the paper, a more restrained wording would probably make the manuscript feel more rigorous rather than less.

10 The repeated use of “driver genes” throughout the manuscript may also be stronger than the current evidence allows. In many contexts, “driver” implies a more direct causal or functionally driving role, whereas the present study mainly relies on in silico analyses .So more neutral alternatives such as “key genes,” “candidate genes,” or “psoriasis-associated genes” may be more appropriate unless direct functional validation is added.

11 The latter half of the section on altered pathway activity in Results reads much more strongly than the earlier, more results-oriented parts of the paper. The discussion around Staphylococcus aureus infection, DEFB4A, and estrogen signaling moves rather quickly from pathway-level association to a fairly complete upstream-trigger/downstream-pathogenesis narrative. This part would be more convincing if it were framed more explicitly as hypothesis-generating.

12 The molecular docking analysis is better treated as a supplementary component rather than a strong part of studies of this type. Some of the drug-related interpretations are written too firmly, and the wording in the Discussion and Conclusion around “therapeutic strategies” and “therapeutic promise” would be better toned down.

13 Because of so many analytical modules are integrated in this study, a simple workflow schematic would make the paper much easier to follow. This is important for showing how the analysis moves.

14 The readability of Fig. 1F could be improved. The connection between the bars and the sample labels below is not very clear, which makes the figure a bit hard to follow. It would help to adjust the layout so that each sample label lines up more clearly with its corresponding bar.

15 The current Data Availability Statement may still need some strengthening to better align with the PLOS ONE data policy. Providing the GEO accession numbers and the analysis code is helpful, but if the GitHub repository mainly contains code rather than the processed outputs behind the main figures and statistical analyses, it is still unclear whether all data needed to reproduce the study’s findings are publicly available without restriction. For a study of this type, it would be helpful to make the main source data publicly available as well, including at least the cell-annotation results, sample-level cell proportion tables, CIBERSORT outputs and reference-gene lists, WGCNA module and hub-gene tables, the 74 intersecting genes and top feature lists from the machine-learning analysis, differential gene and enrichment results, AUCell-related outputs, pseudotime-related results, and docking summary tables. In addition, “available from the corresponding author upon request” does not seem fully consistent with the PLOS ONE policy unless specific restrictions are clearly stated. It would therefore be helpful to revise the Data Availability Statement and indicate more clearly where these processed datasets can be accessed.

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Reviewer #1: No

Reviewer #2: No

Reviewer #3: Yes: shixian cui

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<p>Transcriptomic characterization of key psoriasis-associated genes based on single-cell RNA-seq and machine learning

PONE-D-26-09632R1

Dear Dr. Xu,

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.

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Kind regards,

Zhengrui Li

Academic Editor

PLOS One

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

-->Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.-->

Reviewer #1: All comments have been addressed

Reviewer #3: All comments have been addressed

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-->2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. -->

Reviewer #1: Yes

Reviewer #3: Yes

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-->3. Has the statistical analysis been performed appropriately and rigorously? -->

Reviewer #1: Yes

Reviewer #3: Yes

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-->4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.-->

Reviewer #1: Yes

Reviewer #3: Yes

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-->5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.-->

Reviewer #1: Yes

Reviewer #3: Yes

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-->6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)-->

Reviewer #1: The authors have adequately addressed the comments raised in a previous round of review and I feel that this manuscript is now acceptable for publication.

Reviewer #3: The authors have addressed my previous comments satisfactorily. The revised manuscript includes clearer methodological details, more cautious interpretation of the molecular docking results, appropriate discussion of limitations, and improved data availability through the updated GitHub repository with additional processed files.

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-->7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

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Reviewer #1: No

Reviewer #3: Yes: shixian cui

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