Long non-coding RNAs and complex diseases: from experimental results to computational models

Abstract

LncRNAs have attracted lots of attentions from researchers worldwide in recent decades. With the rapid advances in both experimental technology and computational prediction algorithm, thousands of lncRNA have been identified in eukaryotic organisms ranging from nematodes to humans in the past few years. More and more research evidences have indicated that lncRNAs are involved in almost the whole life cycle of cells through different mechanisms and play important roles in many critical biological processes. Therefore, it is not surprising that the mutations and dysregulations of lncRNAs would contribute to the development of various human complex diseases. In this review, we first made a brief introduction about the functions of lncRNAs, five important lncRNA-related diseases, five critical disease-related lncRNAs and some important publicly available lncRNA-related databases about sequence, expression, function, etc. Nowadays, only a limited number of lncRNAs have been experimentally reported to be related to human diseases. Therefore, analyzing available lncRNA-disease associations and predicting potential human lncRNA-disease associations have become important tasks of bioinformatics, which would benefit human complex diseases mechanism understanding at lncRNA level, disease biomarker detection and disease diagnosis, treatment, prognosis and prevention. Furthermore, we introduced some state-of-the-art computational models, which could be effectively used to identify disease-related lncRNAs on a large scale and select the most promising disease-related lncRNAs for experimental validation. We also analyzed the limitations of these models and discussed the future directions of developing computational models for lncRNA research.

Keywords: biological network; complex disease; computational model; lncRNA–disease association prediction; long non-coding RNA; machine learning.

Figure 1.

The flowchart of LRLSLDA which have described the basic steps to predict lncRNA–disease associations based on LRLSLDA.

Figure 2.

The flowchart of LNCSIM which have described the basic ideas of calculating functional similarity between two lncRNAs: (A) constructed the DAGs for disease A and B which are associated with lncRNA u and v; (B) calculated semantic similarity between disease A and B; (C) calculate the similarity score between two disease groups associated with lncRNA u and v. and then obtained functional similarity between them.

Figure 3:

The flowchart shows the three steps of RWRHLD: (A) constructing the lncRNA-miRNA interaction network based on the ‘‘ceRNA hypothesis’’ and the disease–disease similarity network based on disease DAG structure; (B) constructing the heterogeneous lncRNA–disease network by integrating lncRNA crosstalk network, disease similarity network, and experimentally confirmed lncRNA–disease association network; (C) implementing random walk on the heterogeneous network and obtaining a stable probability to rank candidate lncRNAs. A colour version of this figure is available at BIB online: https://academic.oup.com/bib.

Figure 4:

The flowchart of KATZLDA which demonstrates the basic ideas of adopting Katz measure for predicting lncRNA–disease associations. A colour version of this figure is available at BIB online: https://academic.oup.com/bib.

Figure 5:

This method consists the following four steps: calculating tissue specificity score and dividing all the lncRNAs into tissue-specific and non-tissue-specific lncRNAs; predicting potential lncRNA–disease associations for tissue-specific lncRNAs; constructing gene–lncRNA co-expression relationships for all the non-tissue-specific lncRNAs by computing Spearman’s correlation coefficients between their expression profiles; performing disease enrichment and predicting potential lncRNA–disease associations for non-tissue-specific lncRNAs. A colour version of this figure is available at BIB online: https://academic.oup.com/bib.

Figure 6:

The flowchart of HGLDA which showed the basic idea of predicting potential lncRNA–disease associations by integrating disease–miRNA associations and lncRNA-miRNA interactions. The P value was obtained for each lncRNA–disease pair to examine whether they have significantly common associated miRNAs. Then FDR correction was implemented to all these P values. At last, the lncRNA–disease pairs whose FDR was less than 0.05 were selected for experimental validation. A colour version of this figure is available at BIB online: https://academic.oup.com/bib.