Characterization of long non-coding RNA-associated ceRNA network to reveal potential prognostic lncRNA biomarkers in human ovarian cancer
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Meng Zhou1,*, Xiaojun Wang1,*, Hongbo Shi1, Liang Cheng1, Zhenzhen Wang1, Hengqiang Zhao1, Lei Yang1 and Jie Sun1
1 College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, PR China
* These authors have contributed equally to this work
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
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.
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