{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Tran PMH"],"funding":["NIDDK NIH HHS","National Institute of Diabetes and Digestive and Kidney Diseases"],"pagination":["20651"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC7692499"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["10(1)"],"pubmed_abstract":["Gliomas are currently classified through integration of histology and mutation information, with new developments in DNA methylation classification. However, discrepancies exist amongst the major classification methods. This study sought to compare transcriptome-based classification to the established methods. RNAseq and microarray data were obtained for 1032 gliomas from the TCGA and 395 gliomas from REMBRANDT. Data were analyzed using unsupervised and supervised learning and other statistical methods. Global transcriptomic profiles defined four transcriptomic glioma subgroups with 91.4% concordance with the WHO-defined mutation subtypes. Using these subgroups, 168 genes were selected for the development of 1000 linear support vector classifiers (LSVC). Based on plurality voting of 1000 L"],"journal":["Scientific reports"],"pubmed_title":["Comparative analysis of transcriptomic profile, histology, and IDH mutation for classification of gliomas."],"pmcid":["PMC7692499"],"funding_grant_id":["F30 DK121461","F30DK121461"],"pubmed_authors":["Nechtman J","Purohit S","Sharma S","She JX","Satter KB","Tran LKH","Kolhe R","Bollag R","Dun B","Tran PMH","Dos Santos B"],"additional_accession":[]},"is_claimable":false,"name":"Comparative analysis of transcriptomic profile, histology, and IDH mutation for classification of gliomas.","description":"Gliomas are currently classified through integration of histology and mutation information, with new developments in DNA methylation classification. However, discrepancies exist amongst the major classification methods. This study sought to compare transcriptome-based classification to the established methods. RNAseq and microarray data were obtained for 1032 gliomas from the TCGA and 395 gliomas from REMBRANDT. Data were analyzed using unsupervised and supervised learning and other statistical methods. Global transcriptomic profiles defined four transcriptomic glioma subgroups with 91.4% concordance with the WHO-defined mutation subtypes. Using these subgroups, 168 genes were selected for the development of 1000 linear support vector classifiers (LSVC). Based on plurality voting of 1000 L","dates":{"release":"2020-01-01T00:00:00Z","publication":"2020 Nov","modification":"2025-04-22T18:24:04.185Z","creation":"2025-04-06T02:24:27.644Z"},"accession":"S-EPMC7692499","cross_references":{"pubmed":["33244057"],"doi":["10.1038/s41598-020-77777-6"]}}