LncNetP, a systematical lncRNA prioritization approach based on ceRNA and disease phenotype association assumptions

Abstract

// Chaohan Xu 1, * , Yanyan Ping 1, * , Hongying Zhao 1, * , Shangwei Ning 1 , Peng Xia 1 , Weida Wang 1 , Linyun Wan 1 , Jie Li 1 , Li Zhang 1 , Lei Yu 1 and Yun Xiao 1, 2 1 College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China 2 Key Laboratory of Cardiovascular Medicine Research, Harbin Medical University, Ministry of Education, Harbin, China * These authors contributed equally to this work Correspondence to: Chaohan Xu, email: chaohanxu@hrbmu.edu.cn Yun Xiao, email: xiaoyun@ems.hrbmu.edu.cn Keywords: lncRNA prioritization; ceRNA theory; disease phenotype association; pan cancer Received: July 21, 2017      Accepted: November 14, 2017      Published: December 08, 2017 ABSTRACT Our knowledge of lncRNA is very limited and discovering novel disease-related long non-coding RNA (lncRNA) has been a major research challenge in cancer studies. In this work, we developed an LncRNA Network-based Prioritization approach, named “LncNetP” based on the competing endogenous RNA (ceRNA) and disease phenotype association assumptions. Through application to 11 cancer types with 3089 common lncRNA and miRNA samples from the Cancer Genome Atlas (TCGA), our approach yielded an average area under the ROC curve (AUC) of 83.87%, with the highest AUC (95.22%) for renal cell carcinoma, by the leave-one-out cross validation strategy. Moreover, we demonstrated the excellent performance of our approach by evaluating the influencing factors including disease phenotype associations, known disease lncRNAs and the numbers of cancer types. Comparisons with previous methods further suggested the integrative importance of our approach. Taking hepatocellular carcinoma (LIHC) as a case study, we predicted four candidate lncRNA genes, RHPN1-AS1, AC007389.1, LINC01116 and BMS1P20 that may serve as novel disease risk factors for disease diagnosis and prognosis. In summary, our lncRNA prioritization strategy can efficiently identify disease-related lncRNAs and help researchers better understand the important roles of lncRNAs in human cancers.