Abstract
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.
Highlights
In recent years, a large number of non-coding RNAs have been identified by large-scale genomic studies
We found that DisLncPri had the highest rank enrichment score and area under curve (AUC) value in comparison to several other methods for case studies of alzheimer's disease, ovarian cancer, pancreatic cancer and gastric cancer
We identified the lncRNA-mRNA competing endogenous RNAs (ceRNAs) pairs by an integrated pipeline and experimentally validated the disease-associated lncRNAs from the LncRNADisease database
Summary
A large number of non-coding RNAs (ncRNAs) have been identified by large-scale genomic studies. A type of ncRNAs are called microRNAs (miRNAs) that act by destabilizing and repressing target mRNAs post-transcriptionally and are widely studied in several human diseases [1]. Studies have shown that lncRNAs are involved in a wide range of biological functions, such as chromatin modification [2], the regulation of apoptosis and invasion [3] and genomic imprinting [4] as well as in many human diseases including cancers [5, 6]. Many novel lncRNA-disease associations have been identified by in vivo or in vitro experimental methods, identifying new lncRNA-disease associations based on large scale experimental studies is expensive, complex and time-consuming. There is a need to develop better bioinformatic methods that accurately predict potential lncRNA-disease associations and analyze lncRNA functions in humans
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