Identification of prognostic biomarkers in glioblastoma using a long non-coding RNA-mediated, competitive endogenous RNA network
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Yuze Cao1, Peng Wang2, Shangwei Ning2, Wenbiao Xiao1, Bo Xiao1, Xia Li2
1Department of Neurology, Xiangya Hospital, Central South University, Changsha, 410008, Hunan Province, China
2College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, Heilongjiang Province, China
Xia Li, email: firstname.lastname@example.org
Bo Xiao, email: email@example.com
Keywords: lncRNA, ceRNA network, topology, prognostic biomarker, synergistic regulation
Received: March 18, 2016 Accepted: May 10, 2016 Published: May 24, 2016
Glioblastoma multiforme (GBM) is a highly malignant brain tumor associated with a poor prognosis. Cross-talk between competitive endogenous RNAs (ceRNAs) plays a critical role in tumor development and physiology. In this study, we present a multi-step computational approach to construct a functional GBM long non-coding RNA (lncRNA)-mediated ceRNA network (LMCN) by integrating genome-wide lncRNA and mRNA expression profiles, miRNA-target interactions, functional analyses, and clinical survival analyses. LncRNAs in the LMCN exhibited specific topological features consistent with a regulatory association with coding mRNAs across GBM pathology. We determined that the lncRNA MCM3AP-AS was involved in RNA processing and cell cycle-related functions, and was correlated with patient survival. MCM3AP-AS and MIR17HG acted synergistically to regulate mRNAs in a network module of the competitive LMCN. By integrating the expression profile of this module into a risk model, we stratified GBM patients in both the The Cancer Genome Atlas and an independent GBM dataset into distinct risk groups. Finally, survival analyses demonstrated that the lncRNAs and network module are potential prognostic biomarkers for GBM. Thus, ceRNAs could accelerate biomarker discovery and therapeutic development in GBM.
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