{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Zhang P"],"funding":["Shandong Provincial Natural Science Foundation","Fundamental Research Funds for the Central Universities","Hebei Natural Science Foundation","National Natural Science Foundation of China"],"pagination":["e70517"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12647366"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["15(12)"],"pubmed_abstract":["<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."],"journal":["Clinical and translational medicine"],"pubmed_title":["Integrated pathway analysis identifies prognostically relevant subtypes of glioblastoma characterized by abnormalities in multi-omics."],"pmcid":["PMC12647366"],"funding_grant_id":["U21A20200","32570906","2024CX06057","C2025105003","ZR2024MC035","ZR2024QC039"],"pubmed_authors":["Ouyang X","Zhang Y","Xia Q","Dong L","Yu T","Liu D","Zhong L","Zhang P"],"additional_accession":[]},"is_claimable":false,"name":"Integrated pathway analysis identifies prognostically relevant subtypes of glioblastoma characterized by abnormalities in multi-omics.","description":"<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.","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Dec","modification":"2026-05-19T03:20:09.804Z","creation":"2026-05-19T03:11:48.598Z"},"accession":"S-EPMC12647366","cross_references":{"pubmed":["41292185"],"doi":["10.1002/ctm2.70517"]}}