title: An integrated mRNA and microRNA expression signature for glioblastoma multiforme prognosis.
abstract: Although patients with Glioblastoma multiforme (GBM) have grave prognosis, significant variability in patient outcome is observed. The objective of this study is to identify a molecular signature for GBM prognosis. We subjected 355 mRNA and microRNA expression profiles to elastic net-regulated Cox regression for identification of an integrated RNA signature for GBM prognosis. A prognostic index (PI) was generated for patient stratification. Survival comparison was conducted by Kaplan-Meier method and a general multivariate Cox regression procedure was applied to evaluate the independence of the PI. The abilities and efficiencies of signatures to predict GBM patient outcome was assessed and compared by the area under the curve (AUC) of the receiver-operator characteristic (ROC). An integrated RNA prognostic signature consisted by 4 protective mRNAs, 12 risky mRNAs, and 1 risky microRNA was identified. Decreased survival was associated with being in the high-risk group (hazard ratio = 2.864, P<0.0001). The prognostic value of the integrated signature was validated in five independent GBM expression datasets (n = 201, hazard ratio = 2.453, P<0.0001). The PI outperformed the known clinical factors, mRNA-only, and miRNA-only prognostic signatures for GBM prognosis (area under the ROC curve for the integrated RNA, mRNA-only, and miRNA-only signatures were 0.828, 0.742, and 0.757 at 3 years of overall survival, respectively, P<0.0001 by permutation test). We describe the first, to our knowledge, robust transcriptome-based integrated RNA signature that improves the current GBM prognosis based on clinical variables, mRNA-only, and miRNA-only signatures.
authors.affiliation: Department of Statistics, College of Mathematics and Computer Science, Hunan Normal University, Changsha, China; Department of Molecular Epidemiology, College of Medicine, Hunan Normal University, Changsha, China.
authors.forename: Jie
authors.initials: J
authors.lastname: Xiong
authors.affiliation: Department of Computational Physics, Institute of Modern Physics of Chinese Academy of Sciences, Lanzhou, China.
authors.forename: Zhitong
authors.initials: Z
authors.lastname: Bing
authors.affiliation: Department of Molecular Epidemiology, College of Medicine, Hunan Normal University, Changsha, China.
authors.forename: Yanlin
authors.initials: Y
authors.lastname: Su
authors.affiliation: Department of Cancer Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America.
authors.forename: Defeng
authors.initials: D
authors.lastname: Deng
authors.affiliation: Department of Statistics, College of Mathematics and Computer Science, Hunan Normal University, Changsha, China; Department of Molecular Epidemiology, College of Medicine, Hunan Normal University, Changsha, China.
authors.forename: Xiaoning
authors.initials: X
authors.lastname: Peng
mesh_terms: D019540:Area Under Curve; D015331:Cohort Studies; D063990:Gene Ontology; D005909:Glioblastoma / Q000175:diagnosis* / Q000235:genetics*; D006801:Humans; D053208:Kaplan-Meier Estimate; D035683:MicroRNAs / Q000235:genetics / Q000378:metabolism*; D011379:Prognosis; D016016:Proportional Hazards Models; D012333:RNA, Messenger / Q000235:genetics / Q000378:metabolism*; D059467:Transcriptome
chemical_list: D035683:MicroRNAs; D012333:RNA, Messenger