Characterization of long non-coding RNA-associated ceRNA network to reveal potential prognostic lncRNA biomarkers in human ovarian cancer

Abstract

// Meng Zhou 1,* , Xiaojun Wang 1,* , Hongbo Shi 1 , Liang Cheng 1 , Zhenzhen Wang 1 , Hengqiang Zhao 1 , Lei Yang 1 and Jie Sun 1 1 College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, PR China * These authors have contributed equally to this work Correspondence to: Jie Sun, email: // Keywords : biomarker, competing endogenous RNA, long non-coding RNA, ovarian cancer Received : October 12, 2015 Accepted : January 24, 2016 Published : February 03, 2016 Abstract Accumulating evidence has underscored the important roles of long non-coding RNAs (lncRNAs) acting as competing endogenous RNAs (ceRNAs) in cancer initiation and progression. In this study, we used an integrative computational method to identify miRNA-mediated ceRNA crosstalk between lncRNAs and mRNAs, and constructed global and progression-related lncRNA-associated ceRNA networks (LCeNETs) in ovarian cancer (OvCa) based on “ceRNA hypothesis”. The constructed LCeNETs exhibited small world, modular architecture and high functional specificity for OvCa. Known OvCa-related genes tended to be hubs and occurred preferentially in the functional modules. Ten lncRNA ceRNAs were identified as potential candidates associated with stage progression in OvCa using ceRNA-network driven method. Finally, we developed a ten-lncRNA signature which classified patients into high- and low-risk subgroups with significantly different survival outcomes. Our study will provide novel insight for better understanding of ceRNA-mediated gene regulation in progression of OvCa and facilitate the identification of novel diagnostic and therapeutic lncRNA ceRNAs for OvCa.