{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Zhou N"],"funding":["Chongqing Postgraduate Innovation and Entrepreneurship Project","The Eaglet Program of Young Innovative Talents Cultivation","Chongqing Municipal Training Program of Innovation and Entrepreneurship for Undergraduate"],"pagination":["5714"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11832913"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["15(1)"],"pubmed_abstract":["Glioblastoma (GBM), a highly heterogeneous and aggressive brain tumor, presents significant clinical challenges due to its frequent recurrence and poor prognosis. In this study, we employed high-dimensional weighted gene co-expression network analysis (hd-WGCNA) and single-cell transcriptomic analysis to investigate the molecular heterogeneity of GBM. We identified functional gene modules associated with tumor cell subpopulations exhibiting highly malignant traits, particularly linked to proteasome dysregulation. Intercellular communication analysis revealed extensive interactions between malignant tumor subpopulations and tumor microenvironment (TME), highlighting critical crosstalk with tumor-associated macrophages (TAMs) and T cells. Using machine learning, we developed risk scores base"],"journal":["Scientific reports"],"pubmed_title":["Decoding the heterogeneous subpopulations of glioblastoma for prognostic stratification and uncovering the promalignant role of PSMC2."],"pmcid":["PMC11832913"],"funding_grant_id":["S202310635405","No. CYS22199","CY230235"],"pubmed_authors":["Zhou N","Yan J","Xiong M","Zhu S"],"additional_accession":[]},"is_claimable":false,"name":"Decoding the heterogeneous subpopulations of glioblastoma for prognostic stratification and uncovering the promalignant role of PSMC2.","description":"Glioblastoma (GBM), a highly heterogeneous and aggressive brain tumor, presents significant clinical challenges due to its frequent recurrence and poor prognosis. In this study, we employed high-dimensional weighted gene co-expression network analysis (hd-WGCNA) and single-cell transcriptomic analysis to investigate the molecular heterogeneity of GBM. We identified functional gene modules associated with tumor cell subpopulations exhibiting highly malignant traits, particularly linked to proteasome dysregulation. Intercellular communication analysis revealed extensive interactions between malignant tumor subpopulations and tumor microenvironment (TME), highlighting critical crosstalk with tumor-associated macrophages (TAMs) and T cells. Using machine learning, we developed risk scores base","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Feb","modification":"2025-04-03T23:16:46.232Z","creation":"2025-04-03T23:16:46.232Z"},"accession":"S-EPMC11832913","cross_references":{"pubmed":["39962070"],"doi":["10.1038/s41598-024-83571-5"]}}