Open AccessWhole‐Genome mRNA Expression Profiling Identifies Functional and Prognostic Signatures in Patients with Mesenchymal Glioblastoma Multiforme
Author(s) -
Bao ZhaoShi,
Zhang ChuanBao,
Wang HongJun,
Yan Wei,
Liu YanWei,
Li MingYang,
Zhang Wei
Publication year - 2013
Publication title -
cns neuroscience and therapeutics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.403
H-Index - 69
eISSN - 1755-5949
pISSN - 1755-5930
DOI - 10.1111/cns.12118
Subject(s) - oncology , microarray analysis techniques , gene signature , medicine , gene expression profiling , proportional hazards model , microarray , bioinformatics , mesenchymal stem cell , glioblastoma , gene expression , gene , computational biology , biology , pathology , cancer research , genetics
Summary Background The Cancer Genome Atlas (TCGA) has divided patients with glioblastoma multiforme (GBM) into four subtypes based on mRNA expression microarray. The mesenchymal subtype, with a larger proportion, is considered a more lethal one. Clinical outcome prediction is required to better guide more personalized treatment for these patients. Aims The objective of this study was to identify a mRNA expression signature to improve outcome prediction for patients with mesenchymal GBM. Results For signature identification and validation, we downloaded mRNA expression microarray data from TCGA as training set and data from Rembrandt and GSE16011 as validation set. Cox regression and risk‐score analysis were used to develop the 4 signatures, which were function and prognosis associated as revealed by Gene Ontology (GO) analysis and Gene Set Variation Analysis (GSVA). Patients who had high‐risk scores according to the signatures had poor overall survival compared with patients who had low‐risk scores. Conclusions The signatures were identified as risk predictors that patients who had a high‐risk score tended to have unfavorable outcome, demonstrating their potential for personalizing cancer management.