PONE-D-24-22148Machine Learning–Based New Classification for Immune Infiltration of GliomasPLOS ONE

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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: Partly

Reviewer #2: Yes

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

Reviewer #1: Yes

Reviewer #2: Yes

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

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Reviewer #1: No

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

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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: The authors examined the immunosuppressive tumor microenvironment characteristics in gliomas using data from the TCGA and CGGA databases, employing machine learning unsupervised cluster analysis and DAVID methods. They classified gliomas into subtypes IM1-IM4 and IMA-IMD through machine learning, revealing distinct TME characteristics including immune cell infiltration proportions, IL-8 and TNF signaling pathways, and various biological processes. However, fundamental flaws in the study are evident:

- Subtypes were ambiguously defined.

- Enrichment analysis linked IM1-IMD subtypes with IL-8 and TNF pathways, IM2-IMB with leukocyte activation and NK cell cytotoxicity, IM3-IMC with mitotic processes, and IM4-IMA with CNS and muscle development. These associations are descriptive and lack unified biological significance, let alone clinical relevance.

- Clustering analysis from figures demonstrates unclear differences among groups, and survival analyses lack reproducibility.

Overall, these limitations suggest the manuscript may not meet the threshold for publication priority in the journal.

Reviewer #2: Summary of the research and the overall impression:

The study’s ultimate goal is to discover precise reasons why glioma treatment fails - researchers investigate the immune microenvironment (IME) of gliomas and develop a machine learning (ML) model to classify these tumors based on their immune infiltration characteristics.

The primary research question is to understand the immune microenvironment of gliomas and predict different immune subtypes using ML methods. This aims to guide personalized immune therapy in glioma patients, potentially improving its effectiveness.

The authors employed a combination of bioinformatics tools, statistical analysis, and ML techniques to classify gliomas based on their immune infiltration characteristics. Unsupervised cluster analysis on immune-related metagene clusters derived 4 immune subtypes of glioma in both datasets. ML algorithms used: logistic regression, decision tree, random forest, MLP, LSTM. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis was performed using DAVID, to understand the biological functions and pathways associated with the different immune subtypes. The distribution of immune cells and their prognostic significance were analyzed using Kaplan-Meier survival curves. The study successfully classified gliomas into 4 distinct immune subtypes using ML: immunogenic subtype, adaptive immune resistance subtype, mesenchymal subtype, and immune tolerance subtype. The immune microenvironment of gliomas shows distinct infiltration patterns of immune cells, with specific subtypes exhibiting high enrichment of certain cell types like Monocytic lineage, Myeloid dendritic cells, NK cells, and CD8 T cells in IM1/IMD subtypes.

A well-written, biologically and clinically relevant discussion includes at the end the recommended treatment strategies for patients with each immune subtype.

The authors have cited relevant literature regarding the challenges in treating gliomas, the role of the immune microenvironment, and the application of machine learning in oncology. This provides a solid reference foundation for their research.

The dataset is obtained from publicly accessible repositories - CGGA and TCGA, encompassing a large sample size of glioma cases.

The authors employ appropriate statistical methods to validate their findings.

The code is not available publicly - it will be shared at the reader’s request.

The authors have followed appropriate protocols for data collection, analysis, and validation.

The manuscript demonstrates a high level of scientific rigor, and relevance to the field of neuro-oncology. It is suitable for publication in its present form, with minor revisions to add clarity and detail.

The manuscript demonstrates conformance to general principles of transparency, data availability, and methodological rigor.

The manuscript is well-organized and easy to follow, making for a pleasant read.

Minor issues:

The manuscript mentions the use of the CGGA and TCGA datasets, but lacks detail on how these datasets were selected, processed, and used in analyses. Please include a detailed description of the datasets, such as the types of data available (e.g., RNA-seq, clinical data), and specific cohorts.

Readers may need more information about the normalization methods for the transcriptomic data.

Please consider describing how the differentially expressed genes (DEGs) were selected, including the criteria for differential expression such as statistical thresholds (e.g., p-value, fold change) and the tools used.

The manuscript briefly mentions using DAVID for GO and KEGG pathway analyses without details on the processes involved. Please consider mentioning more of the DAVID tool overview and specific settings applied.

In the part of the discussion related to proposed strategies for different immune subtypes, it would be beneficial to directly refer to the relevant medical literature to stronger justify the treatment recommendations.

Please acknowledge the study limitations and suggest future research directions.

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Reviewer #1: No

Reviewer #2: No

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Machine Learning–Based New Classification for Immune Infiltration of Gliomas

PONE-D-24-22148R1

Dear Dr. Yuan,

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,

Michael C Burger, M.D.

Academic Editor

PLOS ONE

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