ArticleFrontiers in cell and developmental biology2025
Integrated machine learning analysis of 30 cell death patterns identifies a novel prognostic signature in glioma.
Article in Frontiers in cell and developmental biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- All-Trans Retinoic Acid and Curcumin Exhibit Hormesis or Synergistic Anticancer Effects in U87 Glioblastoma Cells: Defining the Proteome Accompanying Synergism.International journal of molecular sciences · 2026Article
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12 authors.
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Abstract
Background: Glioma heterogeneity and therapeutic resistance are closely linked to dysregulated programmed cell death (PCD). While individual PCD pathways have been studied, the integrated network of multi-modal PCD interactions and their clinical implications in glioma remain poorly understood. This study aims to decipher the interplay between 30 distinct PCD modalities and the immune microenvironment, developing a robust prognostic signature to guide therapy. Methods: This study integrated 2,743 glioma samples from TCGA, CGGA, and GEO databases, encompassing RNA-seq, single-cell transcriptomic (GSE167960), and mutational data. Through literature mining and GeneCards database screening, 30 programmed cell death (PCD)-related gene sets (total 11,681 genes) were curated, identifying 428 differentially expressed genes (DEGs; |log Results: Integrated analysis of 2,743 public gliomas samples identified 428 cell death-associated differentially expressed genes, enriched in neuroactive ligand-receptor interactions and extracellular matrix regulation. Unsupervised clustering revealed distinct immune-activated and immune-silent patient subtypes. A pan-death prognostic signature (Cell-Death Score, CDS), constructed via multi-algorithm machine learning and optimized using CoxBoost to incorporate 25 key genes, demonstrated robust performance in training (1-/3-year AUC = 0.894/0.943) and validation cohort (C-index = 0.717), effectively stratifying high-risk patients (HR = 3.21, p < 0.0001). High-CDS patients displayed elevated tumor mutational burden, homologous recombination deficiency, and immune checkpoint expression, alongside enhanced sensitivity to 11 therapeutic agents, including gemcitabine. Single-cell trajectory analysis confirmed significant activation of model genes during glioma progression. A clinical nomogram integrating CDS, WHO grade and radiotherapy further improved prognostic utility. Based on Conclusion: The CDS signature unravels the molecular interplay between glioma cell death heterogeneity, immune dysregulation, and therapeutic resistance. This biomarker system provides both prognostic and therapeutic insights for precision oncology, paving the way for personalized combination therapies in glioma management.
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