PONE-D-23-21264Identification of protein signatures for lung cancer subtypes based on BPSO methodPLOS ONE

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Reviewer #1: Yes

Reviewer #2: Partly

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

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5. Review Comments to the Author

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Reviewer #1: The aim of this article is to identify protein biomarkers that can distinguish LUAD from LUSC, which is essential for personalized treatment regimens. Using the BPSO feature selection method, the authors identified 10 candidate protein biomarkers with good classification performance by t-SNE and PCA algorithm analysis. To explore the causal relationship between these proteins and their association with tumor subtypes, a regulatory network was constructed using the PCStable algorithm. The authors also used Mauman's correlation, Cox proportional hazards models, and Kaplan-Meier curves to verify the biological significance of the candidate proteins. However, I have some suggestions and questions about certain parts of the manuscript.

1. According to what criteria are the authors selected 10 candidate protein features by BPSO algorithm?

2. Whether NF2 and INPP4B have been demonstrated as prognostic markers in lung cancer.

3. The authors have repeatedly described that the feature selection method BPSO is superior to other methods, but the speed, computational cost and robustness of other methods are not compared in the results.

4. The experimental verification of the four proteins as lung cancer biomarkers is insufficient, and it is recommended to add more experiments to verify the effects of the four proteins on lung cancer.

Reviewer #2: The overall idea of identifying protein markers in order to differentiate lung adenocarcinoma and squamous cell carcinoma is beneficial and important. The results were obtained using the protein expression dataset for lung cancer from the public TCPA database.

Major concerns are in the following points:

1. In the introduction and in the discussion, the authors claimed they “identify candidate protein biomarkers that outperformed the original features in classifying the two major lung cancer subtypes." and “The classification performance of the 10 protein signatures outperformed that of all proteins, as evidenced by t-SNE and PCA.”

a. Both t-SNE and PCA are graphical visualization approaches, but no quantitative measurements (e.g. sensitivity, specificity, AUC) were used to evaluate the classification accuracy for adenocarcinoma vs squamous cell carcinoma.

b. The authors didn’t compare the classification accuracy of their method to the prior outstanding approaches. For instance, the prior study (Ref. 8: DOI: 10.1038/modpathol.2011.92) demonstrated that “a two-marker panel of TTF-1/p63 is sufficient for subtyping of the majority of tumors as adenocarcinomas vs squamous cell carcinoma”. Did the authors compare the predictive performance of the selected 10 or 4 protein signatures with the two-marker panel of TTF-1/p63?

c. Moreover, the proposed BPSO algorithm is a feature selection approach, the classification accuracy by using the selected features should be validated by using cross-validation and/or on other independent datasets.

2. In Figure 3, the network analysis identified 4 proteins “directly linked” to the classification of adenocarcinoma vs squamous cell carcinoma. It doesn’t currently make scientific sense how the four proteins will directly impact the subtype classification. Please discuss this, otherwise it should be discussed as a limitation.

Minor:

Legends or contents in some figures are too small, or low resolution (e.g. Figures 1 and 5).

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Reviewer #1: No

Reviewer #2: No

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Identification of protein signatures for lung cancer subtypes based on BPSO method

PONE-D-23-21264R1

Dear Dr. Wang,

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,

Bingli Wu

Academic Editor

PLOS ONE

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