{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Cao X"],"funding":["Science and Technology Program of DeYang City","2024 Sichuan Provincial Science and Technology Achievement Transfer and Transformation Guidance Program","2025 Sichuan Provincial Key Research and Development Program"],"pagination":["1744"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12480193"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["16(1)"],"pubmed_abstract":["Background
The intrinsic heterogeneity and invasiveness of diffuse gliomas complicate accurate prognosis. Existing approaches are largely constrained by subtype specificity or limited analytical dimensions. To address this gap, a multi- dimension-based prognostic framework encompassing the full glioma spectrum was developed, accompanied by an analysis of the associated immune microenvironment.Methods
A total of 3,323 glioma samples from the SEER (n = 2181), CGGA (n = 807), and TCGA (n = 335) datasets were integrated. Differentially expressed genes were screened using the limma package, and a Lasso-Cox-based prognostic signature (Glioma-GDPM) was established. Clinical variables such as age, grade, and IDH mutation status were harmonized through propensity score matching to c"],"journal":["Discover oncology"],"pubmed_title":["Multilayered insights into the full spectrum of diffuse gliomas: a novel prognostic evaluation model."],"pmcid":["PMC12480193"],"funding_grant_id":["No.2023SZZ093","No.2024ZHCG0018","No. 25QYCX0359"],"pubmed_authors":["Cao X","Liu M","Zhang J","Huang J","Zou D","Li K"],"additional_accession":[]},"is_claimable":false,"name":"Multilayered insights into the full spectrum of diffuse gliomas: a novel prognostic evaluation model.","description":"Background
The intrinsic heterogeneity and invasiveness of diffuse gliomas complicate accurate prognosis. Existing approaches are largely constrained by subtype specificity or limited analytical dimensions. To address this gap, a multi- dimension-based prognostic framework encompassing the full glioma spectrum was developed, accompanied by an analysis of the associated immune microenvironment.Methods
A total of 3,323 glioma samples from the SEER (n = 2181), CGGA (n = 807), and TCGA (n = 335) datasets were integrated. Differentially expressed genes were screened using the limma package, and a Lasso-Cox-based prognostic signature (Glioma-GDPM) was established. Clinical variables such as age, grade, and IDH mutation status were harmonized through propensity score matching to c","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Sep","modification":"2026-05-03T03:13:33.757Z","creation":"2026-05-03T03:10:18.892Z"},"accession":"S-EPMC12480193","cross_references":{"pubmed":["41020920"],"doi":["10.1007/s12672-025-03551-7"]}}