Prediction of clinical outcome in glioblastoma using a biologically relevant nine‐microRNA signature. Issue 3 (28th November 2014) - Hayes, Josie; Thygesen, Helene; Tumilson, Charlotte; Droop, Alastair; Boissinot, Marjorie; Hughes, Thomas A.; Westhead, David; Alder, Jane E.; Shaw, Lisa; Short, Susan C.; Lawler, Sean E.
Abstract : 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. Highlights: Used entire TCGA dataset to identify a 9‐microRNA signature that predicts outcome in gliobastoma. 8 of the 9 microRNAs have proven roles in glioblastoma biology. Lasso regression may be a useful statistical tool to extract prognostic signatures from large databases.