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

• DOI: 10.18632/oncotarget.7181
• Publication Date: 2016-02-04T01:25:24Z
• Authors: Meng Zhou; Xiaojun Wang; Hongbo Shi; Liang Cheng; Zhenzhen Wang; Hengqiang Zhao; Lei Yang; Jie Sun
• Keywords: Ovarian Neoplasms; 0301 basic medicine; Computational Biology; Receptor Cross-Talk; Prognosis; 3. Good health; Gene Expression Regulation, Neoplastic; MicroRNAs; 03 medical and health sciences; Biomarkers, Tumor; Humans; Female; Gene Regulatory Networks; RNA, Long Noncoding; Research Paper

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.