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September 20, 2025Frontiers in Cell and Developmental BiologyOpen Access

Integrated machine learning analysis of 30 cell death patterns identifies a novel prognostic signature in glioma

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Authors

MHMinhao HuangKZKai ZhaoYYYongtao Yang

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Overview

Integrated analysis determines a novel prognostic signature related to cell death pathways in glioma, suggesting improved patient stratification and therapy outcomes.

Key Points

  • The pan-death prognostic signature effectively stratifies high-risk glioma patients with a hazard ratio of 3.21.
  • Integrative analysis of 2,743 glioma samples identified 428 cell death-related differentially expressed genes.
  • Machine learning algorithms optimized via CoxBoost constructed the prognostic signature comprising 25 key genes.
  • The study supports the significance of immune dysregulation and therapeutic resistance in glioma progression.

Cite This Study

Huang et al. (2025) studied this question.

synapsesocial.com/papers/68d46aae31b076d99fa6778ehttps://doi.org/10.3389/fcell.2025.1677290
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