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Risk Score based on microRNA expression signature is independent prognostic classifier of glioblastoma patients

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Authors

ŠÁNA Jiří RADOVÁ Lenka LAKOMÝ Radek KŘEN Leoš FADRUS Pavel SMRČKA Martin BEŠŠE Andrej NEKVINDOVÁ Jana HERMANOVÁ Markéta JANČÁLEK Radim SVOBODA Marek HAJDÚCH Marian ŠLAMPA Pavel VYZULA Rostislav SLABÝ Ondřej

Year of publication 2014
Type Article in Periodical
Magazine / Source Carcinogenesis
MU Faculty or unit

Central European Institute of Technology

Citation
Doi http://dx.doi.org/10.1093/carcin/bgu212
Field Oncology and hematology
Keywords microRNA; expression profiling; glioblastoma multiforme; risc score; prognostic classifier
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Description Glioblastoma multiforme (GBM) is the most malignant primary brain tumor. The prognosis of GBM patients varies considerably and the histopathological examination is not sufficient for individual risk estimation. MicroRNAs (miRNAs) are small, non-coding RNAs that function as post-transcriptional regulators of gene expression and were repeatedly proved to play important roles in pathogenesis of GBM. In our study, we performed global miRNA expression profiling of 58 glioblastoma tissue samples obtained during surgical resections and 10 non-tumor brain tissues. The subsequent analysis revealed 28 significantly deregulated miRNAs in GBM tissue, which were able to precisely classify all examined samples. Correlation with clinical data led to identification of six-miRNA signature significantly associated with progression free survival [hazard ratio (HR) 1.98, 95% confidence interval (CI) 1.33-2.94, P < 0.001] and overa+ll survival (HR 2.86, 95% CI 1.91-4.29, P < 0.001). O(6)-methylguanine-DNA methyltransferase methylation status was evaluated as reference method and Risk Score based on six-miRNA signature indicated significant superiority in prediction of clinical outcome in GBM patients. Multivariate Cox analysis indicated that the Risk Score based on six-miRNA signature is an independent prognostic classifier of GBM patients. We suggest that the Risk Score presents promising prognostic algorithm with potential for individualized treatment decisions in clinical management of GBM patients.
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