A bioinformatics method for predicting long noncoding RNAs associated with vascular disease

Abstract

Long noncoding RNAs (lncRNAs) play important roles in human diseases including vascular disease. Given the large number of lncRNAs, however, whether the majority of them are associated with vascular disease remains unknown. For this purpose, here we present a genomic location based bioinformatics method to predict the lncRNAs associated with vascular disease. We applied the presented method to globally screen the human lncRNAs potentially involved in vascular disease. As a result, we predicted 3043 putative vascular disease associated lncRNAs. To test the accuracy of the method, we selected 10 lncRNAs predicted to be implicated in proliferation and migration of vascular smooth muscle cells (VSMCs) for further experimental validation. The results confirmed that eight of the 10 lncRNAs (80%) are validated. This result suggests that the presented method has a reliable prediction performance. Finally, the presented bioinformatics method and the predicted vascular disease associated lncRNAs together may provide helps for not only better understanding of the roles of lncRNAs in vascular disease but also the identification of novel molecules for the diagnosis and therapy of vascular disease.