Integrated pathway analysis identifies prognostically relevant subtypes of glioblastoma characterized by abnormalities in multi‐omics

作者：Pei Zhang, Dan Liu, Tonghui Yu, Yanlin Zhang, Lu Zhong, Xiao Ouyang, Xia Qin, Lei Dong · 发表于：Clinical and Translational Medicine · 年份：2025 · DOI：10.1002/ctm2.70517 · 被引用次数：2 · 研究领域：Glioma Diagnosis and Treatment、Ferroptosis and cancer prognosis、Cancer Immunotherapy and Biomarkers

BACKGROUND: Gene expression-based molecular subtypes in glioblastoma from The Cancer Genome Atlas Network (TCGA-GBM) unraveled the pathological origins by identifying tumour cell driver genes. However, the causal inference between molecular subtype origins and their therapeutic efficacy remains obscure. METHODS: We integrated TCGA-GBM multi-omics (DNA, mRNA, and protein profiles) using correlation analysis to identify cis-regulation. We analyzed the exposure-mediated base substitution-level mutations and their potential triggers. Importantly, we performed Consensus Clustering based on the MSigDB database with Silhouette-correction to identify prognostically relevant pathway-based MSig subtypes. The tumour driver mutations (co-occurrence mutation pattern), aberrant pathways (tumour hallmarks), immune microenvironment (xCell), and pseudo-time analysis (dyno) were used to characterize the MSig subtype landscape. Furthermore, we evaluated potential drug sensitivities across MSig subtypes using the Genomics of Drug Sensitivity in Cancer database. RESULTS: We classified five MSig subtypes, characterized by neural-like, tumour-driving, low tumour evolution, immune-inflamed, and classical tumour features. We observed several key features in 'tumour-driving' GBM patients: (1) mutual exclusivity between prognostic factors TP53 and EGFR; and (2) IDH1 mutations co-occurring with TP53, which account for the protective role of IDH1 in TP53 mutant patients. The immune-inflamed GBM, characte...