Single-cell and machine learning-based pyroptosis-related gene signature predicts prognosis and immunotherapy response in glioblastoma.

ABSTRACT:

Background

Glioblastoma (GBM) is the most aggressive primary malignancy of the central nervous system, characterized by profound heterogeneity and an immunosuppressive microenvironment, leading to dismal prognosis. Pyroptosis, an inflammatory form of programmed cell death, has been increasingly linked to tumor immunity and progression; however, its molecular roles and clinical implications in GBM remain insufficiently understood.

Methods

We integrated bulk transcriptome profiles from TCGA-GBM, CGGA, and GEO datasets with single-cell RNA sequencing data from GSE141383 and GSE223063. A comprehensive GBM single-cell atlas was constructed using Seurat and Harmony, and malignant epithelial cells were inferred via inferCNV. Pyroptosis activity was quantified by five complementary

SUBMITTER: Fang L 

PROVIDER: S-EPMC12592165 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature