A 4-miRNA signature predicts the therapeutic outcome of glioblastoma
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Maximilian Niyazi1,8,9, Adriana Pitea2,8, Michel Mittelbronn3, Joachim Steinbach4, Carsten Sticht5, Franz Zehentmayr1,6, Daniel Piehlmaier2,8, Horst Zitzelsberger2,8, Ute Ganswindt1,8, Claus Rödel7, Kirsten Lauber1,8, Claus Belka1,8,9, Kristian Unger2,8
1Ludwig-Maximilians-University of Munich, Department of Radiation Oncology, Munich, Germany
2Research Unit of Radiation Cytogenetics, Helmholtz Zentrum München, Neuherberg, Germany
3Institute of Neurology (Edinger Institute), Goethe-University Frankfurt, Frankfurt/Main, Germany
4Dr. Senckenbergisches Institut für Neuroonkologie, Klinikum der J.W. Goethe-Universität, Frankfurt/Main, Germany
5Zentrum für Medizinische Forschung, Medizinische Fakultät Mannheim, Mannheim, Germany
6Department of Radiation Oncology, Paracelsus Medical University, Salzburg, Austria
7Department of Radiation Oncology, University Hospital, Frankfurt, Germany
8Clinical Cooperation Group Personalized Radiotherapy in Head and Neck Cancer, Helmholtz Zentrum München, Neuherberg, Germany
9German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
Kristian Unger, email: firstname.lastname@example.org
Keywords: glioblastoma, miRNA, signature
Received: February 10, 2016 Accepted: May 22, 2016 Published: June 11, 2016
Multimodal therapy of glioblastoma (GBM) reveals inter-individual variability in terms of treatment outcome. Here, we examined whether a miRNA signature can be defined for the a priori identification of patients with particularly poor prognosis.
FFPE sections from 36 GBM patients along with overall survival follow-up were collected retrospectively and subjected to miRNA signature identification from microarray data. A risk score based on the expression of the signature miRNAs and cox-proportional hazard coefficients was calculated for each patient followed by validation in a matched GBM subset of TCGA. Genes potentially regulated by the signature miRNAs were identified by a correlation approach followed by pathway analysis.
A prognostic 4-miRNA signature, independent of MGMT promoter methylation, age, and sex, was identified and a risk score was assigned to each patient that allowed defining two groups significantly differing in prognosis (p-value: 0.0001, median survival: 10.6 months and 15.1 months, hazard ratio = 3.8). The signature was technically validated by qRT-PCR and independently validated in an age- and sex-matched subset of standard-of-care treated patients of the TCGA GBM cohort (n=58). Pathway analysis suggested tumorigenesis-associated processes such as immune response, extracellular matrix organization, axon guidance, signalling by NGF, GPCR and Wnt. Here, we describe the identification and independent validation of a 4-miRNA signature that allows stratification of GBM patients into different prognostic groups in combination with one defined threshold and set of coefficients that could be utilized as diagnostic tool to identify GBM patients for improved and/or alternative treatment approaches.
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