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

Josie Hayes; Helene Thygesen; Charlotte Tumilson; Alastair Droop; Marjorie Boissinot; Thomas A Hughes; David Westhead; Jane E Alder; Lisa Shaw; Susan C Short; Sean E Lawler

doi:10.1016/j.molonc.2014.11.004

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

Mol Oncol. 2015 Mar;9(3):704-14. doi: 10.1016/j.molonc.2014.11.004. Epub 2014 Nov 28.

Authors

Affiliations

¹ Leeds Institute of Cancer and Pathology, St James's University Hospital, Leeds LS9 7TF, UK.
² School of Pharmacy and Biomedical Sciences, University of Central Lancashire, Preston, Lancashire PR1 2HE, UK.
³ Leeds Institute of Biomedical and Clinical Sciences, St James's University Hospital, Leeds LS9 7TF, UK.
⁴ School of Molecular and Cellular Biology, Faculty of Biological Sciences, University of Leeds, Leeds LS2 9JT, UK.
⁵ Harvey Cushing Neurooncology Laboratories, Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA. Electronic address: [email protected].

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.

Keywords: Glioblastoma; MicroRNA; Prognosis; Signature; TCGA.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Aged
Brain Neoplasms / drug therapy
Brain Neoplasms / genetics*
Cell Line, Tumor
Dacarbazine / analogs & derivatives
Dacarbazine / pharmacology
Dacarbazine / therapeutic use
Databases, Genetic
Female
Gene Expression Profiling*
Gene Expression Regulation, Neoplastic* / drug effects
Glioblastoma / drug therapy
Glioblastoma / genetics*
Humans
Male
MicroRNAs / genetics*
MicroRNAs / metabolism
Middle Aged
Neoplasm Grading
Prognosis
Regression Analysis
Risk Factors
Survival Analysis
Temozolomide
Treatment Outcome

Substances

MicroRNAs
Dacarbazine
Temozolomide