{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Fu M"],"funding":["Natural Science Foundation of China","Shanghai Development and Reform Commission Major Project","National Natural Science Foundation of China"],"pagination":["404"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC8474912"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["19(1)"],"pubmed_abstract":["Background
The molecular profiling of glioblastoma (GBM) based on transcriptomic analysis could provide precise treatment and prognosis. However, current subtyping (classic, mesenchymal, neural, proneural) is time-consuming and cost-intensive hindering its clinical application. A simple and efficient method for classification was imperative.Methods
In this study, to simplify GBM subtyping more efficiently, we applied a random forest algorithm to conduct 26 genes as a cluster featured with hub genes, OLIG2 and CD276. Functional enrichment analysis and Protein-protein interaction were performed using the genes in this gene cluster. The classification efficiency of the gene cluster was validated by WGCNA and LASSO algorithms, and tested in GSE84010 and Gravandeel's GBM dataset"],"journal":["Journal of translational medicine"],"pubmed_title":["Gene clusters based on OLIG2 and CD276 could distinguish molecular profiling in glioblastoma."],"pmcid":["PMC8474912"],"funding_grant_id":["82072784","82072785","2018SHZDZX01"],"pubmed_authors":["Li W","He S","Tennant D","Zhang J","Hua W","Fu M","Mao Y"],"additional_accession":[]},"is_claimable":false,"name":"Gene clusters based on OLIG2 and CD276 could distinguish molecular profiling in glioblastoma.","description":"Background
The molecular profiling of glioblastoma (GBM) based on transcriptomic analysis could provide precise treatment and prognosis. However, current subtyping (classic, mesenchymal, neural, proneural) is time-consuming and cost-intensive hindering its clinical application. A simple and efficient method for classification was imperative.Methods
In this study, to simplify GBM subtyping more efficiently, we applied a random forest algorithm to conduct 26 genes as a cluster featured with hub genes, OLIG2 and CD276. Functional enrichment analysis and Protein-protein interaction were performed using the genes in this gene cluster. The classification efficiency of the gene cluster was validated by WGCNA and LASSO algorithms, and tested in GSE84010 and Gravandeel's GBM dataset","dates":{"release":"2021-01-01T00:00:00Z","publication":"2021 Sep","modification":"2026-05-08T22:47:00.522Z","creation":"2026-05-04T03:09:36.183Z"},"accession":"S-EPMC8474912","cross_references":{"pubmed":["34565408"],"doi":["10.1186/s12967-021-03083-y"]}}