Prioritizing candidate disease-related long non-coding RNAs by walking on the heterogeneous lncRNA and disease network

Abstract

Accumulated evidence has shown that long non-coding RNAs (lncRNA) act as a widespread layer in gene regulatory networks and are involved in a wide range of biological processes. The dysregulation of lncRNA has been implicated in various complex human diseases. Although several computational methods have been developed to predict disease-related lncRNA, this still remains a considerable challenging task. In this study, we tried to construct an lncRNA-lncRNA crosstalk network by examining the significant co-occurrence of shared miRNA response elements on lncRNA transcripts from the competing endogenous RNAs viewpoint. As expected, functional analysis showed that lncRNA sharing significantly enriched interacting miRNAs tend to be involved in similar diseases and have more functionally related flanking gene sets. We further proposed a novel rank-based method, RWRHLD, to prioritize candidate lncRNA-disease associations by integrating three networks (miRNA-associated lncRNA-lncRNA crosstalk network, disease-disease similarity network and known lncRNA-disease association network) into a heterogeneous network and implementing a random walk with restart on this heterogeneous network. We used leave-one-out cross-validation to test the performance of this rank-based method in this study based on known experimentally verified lncRNA-disease associations and obtained a reliable AUC value of 0.871, which is much higher than RWR merely based on an lncRNA network, hypergeometric test and random situation. Furthermore, several novel lncRNA-disease associations predicted in case studies of ovarian cancer and prostate cancer have been confirmed in new studies by literature surveys.

DeepLNC, a long non-coding RNA prediction tool using deep neural network

“…Since the characterization lacks fully fledged evidence, the advent of the prediction models can be successful in reconnecting the broken thread, in upcoming years, which may further be appreciated in understanding the disease etiology. Unresolved issues like functional role of lncRNAs in a particular disease ranging from neurodegeneration to cancer, differential regulation of transcripts in condition-specific disease, delocalization, dysregulation, and mutation of lncRNAs in various biological processes can help us in identifying candidate lncRNAs and linked biomarkers for disease diagnosis, treatment, and prognosis (Chen and Gui 2013;Zhou et al 2015). A newly developed high-quality classification approach can highlight basic Fig.…”

Long Noncoding RNA Identification: Comparing Machine Learning Based Tools for Long Noncoding Transcripts Discrimination

“…This method (HGLDA) circumvents the utility of LncRNADisease database but still presents the desired results. Currently, many other tools, such as RWRlncD [17] and RWRHLD [18], were designed aiming at predicting lncRNA-disease association and obtaining more reliable results. Unfortunately, they have their own limitations [16].…”

Ontology-based disease similarity network for disease gene prediction

“…We also note that a disease similarity network can be constructed based on shared disease gene [30], shared pathways [35], shared miRNA [36], shared protein complex [37], shared disease ontology [38] and disease comorbidity [39]. Similarly to RWR, RWRH algorithm has been successfully applied to other problems such as prediction of novel drugtarget interactions [40] as well as novel disease-associated miRNAs [41] and long non-coding RNAs [42].…”