biostudies-literatureZhang PShandong Provincial Natural Science FoundationFundamental Research Funds for the Central UniversitiesHebei Natural Science FoundationNational Natural Science Foundation of Chinae70517https://www.ebi.ac.uk/biostudies/studies/S-EPMC12647366biostudies-literatureUnknown15(12)<h4>Background</h4>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.<h4>Methods</h4>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.<h4>Results</h4>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, characterized as a 'hot' tumour, exhibited upregulation of immune-related pathways, including PD-1 and IFN-γ signalling responses. DNA methylation landscape revealed 14 MGMT CpG-rich regions regulating expression. Evolutionary trajectories revealed progression from a primary tumour state (close to normal tissue) to two distinct endpoints (tumour-driving and immune-inflamed subtypes).<h4>Conclusions</h4>Our findings reveal interactions between tumour cells and their surrounding immune environment, classifying GBM into two newly identified subtypes: (1) the tumour-driving subtype is characterized by multiple oncogenic mutations, while (2) the immune-blockade subtype is marked by a high presence of immune cells. We highlight the importance of integrating multi-type data (somatic mutations, DNA methylation, and RNA transcripts, etc.) to decipher GBM biology and potential therapeutic implications.<h4>Highlights</h4>We report the interaction between tumor cells and environmental immune cells, classifying GBM into two main subtypes: 1) The tumor-driving subtype is characterized by multiple oncogenic mutations, while 2) the immune-blockage subtype is marked by a high presence of immune cells. We used integrated multidimensional analyses of somatic mutations, DNA methylation, and RNA transcripts to gain a deeper understanding of GBM biology and potential therapeutic implications.Clinical and translational medicineIntegrated pathway analysis identifies prognostically relevant subtypes of glioblastoma characterized by abnormalities in multi-omics.PMC12647366U21A20200325709062024CX06057C2025105003ZR2024MC035ZR2024QC039Ouyang XZhang YXia QDong LYu TLiu DZhong LZhang PfalseIntegrated pathway analysis identifies prognostically relevant subtypes of glioblastoma characterized by abnormalities in multi-omics.<h4>Background</h4>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.<h4>Methods</h4>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.<h4>Results</h4>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, characterized as a 'hot' tumour, exhibited upregulation of immune-related pathways, including PD-1 and IFN-γ signalling responses. DNA methylation landscape revealed 14 MGMT CpG-rich regions regulating expression. Evolutionary trajectories revealed progression from a primary tumour state (close to normal tissue) to two distinct endpoints (tumour-driving and immune-inflamed subtypes).<h4>Conclusions</h4>Our findings reveal interactions between tumour cells and their surrounding immune environment, classifying GBM into two newly identified subtypes: (1) the tumour-driving subtype is characterized by multiple oncogenic mutations, while (2) the immune-blockade subtype is marked by a high presence of immune cells. We highlight the importance of integrating multi-type data (somatic mutations, DNA methylation, and RNA transcripts, etc.) to decipher GBM biology and potential therapeutic implications.<h4>Highlights</h4>We report the interaction between tumor cells and environmental immune cells, classifying GBM into two main subtypes: 1) The tumor-driving subtype is characterized by multiple oncogenic mutations, while 2) the immune-blockage subtype is marked by a high presence of immune cells. We used integrated multidimensional analyses of somatic mutations, DNA methylation, and RNA transcripts to gain a deeper understanding of GBM biology and potential therapeutic implications.2025-01-01T00:00:00Z2025 Dec2026-05-19T03:20:09.804Z2026-05-19T03:11:48.598ZS-EPMC126473664129218510.1002/ctm2.70517