Prediction of clinical outcome in glioblastoma using a biologically relevant nine-microRNA signature

Background: Glioblastoma is the most aggressive primary brain tumor, and is associated with a very poor prognosis. In this study we investigated the potential of microRNA expression profiles to predict survival in this challenging disease. Methods: MicroRNA and mRNA expression data from glioblastoma (n=475) and grade II and III glioma (n=178) were accessed from The Cancer Genome Atlas. LASSO regression models were used to identify a prognostic microRNA signature. Functionally relevant targets of microRNAs were determined using microRNA target prediction, experimental validation and correlation of microRNA and mRNA expression data. Results: A 9-microRNA prognostic signature was identified which stratified patients into risk groups strongly associated with survival (p=2.26e-09), significant in all glioblastoma subtypes except the non-G-CIMP proneural group. The statistical significance of the microRNA signature was higher than MGMT methylation in temozolomide treated tumors. The 9-microRNA risk score was validated in an independent dataset (p=4.50e-02) and also stratified patients into high- and low-risk groups in lower grade glioma (p=5.20e-03). The majority of the 9 microRNAs have been previously linked to glioblastoma biology or treatment response. Integration of the expression patterns of predicted microRNA targets revealed a number of relevant microRNA/target pairs, which were validated in cell lines. Conclusions: We have identified a novel, biologically relevant microRNA signature that stratifies high- and low-risk patients in glioblastoma. MicroRNA/mRNA interactions identified within the signature point to novel regulatory networks. This is the first study to formulate a survival risk score for glioblastoma which consists of microRNAs associated with glioblastoma biology and/or treatment response, indicating a functionally relevant signature