Improved method for prioritization of disease associated lncRNAs based on ceRNA theory and functional genomics data

Although several computational models that predict disease-associated lncRNAs (long non-coding RNAs) exist, only a limited number of disease-associated lncRNAs are known. In this study, we mapped lncRNAs to their functional genomics context using competing endogenous RNAs (ceRNAs) theory. Based on the criteria that similar lncRNAs are likely involved in similar diseases, we proposed a disease lncRNA prioritization method, DisLncPri, to identify novel disease-lncRNA associations. Using a leave-one-out cross validation (LOOCV) strategy, DisLncPri achieved reliable area under curve (AUC) values of 0.89 and 0.87 for the LncRNADisease and Lnc2Cancer datasets that further improved to 0.90 and 0.89 by integrating a multiple rank fusion strategy. We found that DisLncPri had the highest rank enrichment score and AUC value in comparison to several other methods for case studies of alzheimer's disease, ovarian cancer, pancreatic cancer and gastric cancer. Several novel lncRNAs in the top ranks of these diseases were found to be newly verified by relevant databases or reported in recent studies. Prioritization of lncRNAs from a microarray (GSE53622) of oesophageal cancer patients highlighted ENSG00000226029 (top 2), a previously unidentified lncRNA as a potential prognostic biomarker. Our analysis thus indicates that DisLncPri is an excellent tool for identifying lncRNAs that could be novel biomarkers and therapeutic targets in a variety of human diseases.