Gene clusters based on OLIG2 and CD276 could distinguish molecular profiling in glioblastoma

• DOI: 10.1186/s12967-021-03083-y
• Publication Date: 2021-09-26T21:04:34Z
• Authors: Minjie Fu; Jinsen Zhang; Weifeng Li; Shan He; Jingwen Zhang; Daniel Tennant; Wei Hua; Ying Mao
• Keywords: Subtyping; OLIG2

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 cost-intensive hindering its clinical application. A simple efficient method for classification was imperative. Methods In this study, to simplify GBM more efficiently, we applied a random forest algorithm conduct 26 genes as cluster featured with hub genes, OLIG2 CD276. Functional enrichment Protein–protein interaction were performed using the in gene cluster. efficiency validated by WGCNA LASSO algorithms, tested GSE84010 Gravandeel’s datasets. Results (n = 26) distinguish mesenchymal proneural excellently (AUC 0.92), which be multiple algorithms (WGCNA, LASSO) datasets (GSE84010 dataset). functionally enriched DNA elements T cell associated pathways. Additionally, five signature predict prognosis well ( p 0.0051 training cohort, 0.065 test cohort). Conclusions Our study proved accuracy classifier subtyping, convenient Proneural Mesenchymal GBM.