A heterogeneous label propagation approach to explore the potential associations between miRNA and disease

Xing Chen,Zhu-Hong You,De-Hong Zhang

doi:10.1186/s12967-018-1722-1

Abstract

BackgroundResearch on microRNAs (miRNAs) has attracted increasingly worldwide attention over recent years as growing experimental results have made clear that miRNA correlates with masses of critical biological processes and the occurrence, development, and diagnosis of human complex diseases. Nonetheless, the known miRNA-disease associations are still insufficient considering plenty of human miRNAs discovered now. Therefore, there is an urgent need for effective computational model predicting novel miRNA-disease association prediction to save time and money for follow-up biological experiments.MethodsIn this study, considering the insufficiency of the previous computational methods, we proposed the model named heterogeneous label propagation for MiRNA-disease association prediction (HLPMDA), in which a heterogeneous label was propagated on the multi-network of miRNA, disease and long non-coding RNA (lncRNA) to infer the possible miRNA-disease association. The strength of the data about lncRNA–miRNA association and lncRNA-disease association enabled HLPMDA to produce a better prediction.ResultsHLPMDA achieved AUCs of 0.9232, 0.8437 and 0.9218 ± 0.0004 based on global and local leave-one-out cross validation and 5-fold cross validation, respectively. Furthermore, three kinds of case studies were implemented and 47 (esophageal neoplasms), 49 (breast neoplasms) and 46 (lymphoma) of top 50 candidate miRNAs were proved by experiment reports.ConclusionsAll the results adequately showed that HLPMDA is a recommendable miRNA-disease association prediction method. We anticipated that HLPMDA could help the follow-up investigations by biomedical researchers.

Highlights

Research on microRNAs has attracted increasingly worldwide attention over recent years as growing experimental results have made clear that miRNA correlates with masses of critical biological processes and the occurrence, development, and diagnosis of human complex diseases
In pursuit of the higher predictive accuracy, we proposed heterogeneous label propagation for MiRNA-disease association prediction (HLPMDA) for underlying miRNA-disease association prediction
long non-coding RNA (LncRNA)‐disease associations Because we aim to predict latent miRNA-disease association, we looked for the long non-coding RNA (lncRNA) that associate with the disease contained in S1,2, or interacted with the miRNAs contained in S1,2

Summary

Introduction

Research on microRNAs (miRNAs) has attracted increasingly worldwide attention over recent years as growing experimental results have made clear that miRNA correlates with masses of critical biological processes and the occurrence, development, and diagnosis of human complex diseases. The known miRNA-disease associations are still insufficient considering plenty of human miRNAs discovered now. There is an urgent need for effective computational model predicting novel miRNA-disease association prediction to save time and money for follow-up biological experiments. MicroRNAs (miRNAs) consist of about 22 nucleotides and they are one category of endogenous short noncoding RNAs (ncRNAs) that could regulate the expression of target messenger RNAs (mRNAs) at the level of transcription and post-translation [1,2,3,4]. The transcripts of certain let-7 homologs would be downregulated in human lung cancer and the low levels of let-7 would link to poor prognosis [25]. Non-small-cell lung cancer relates to many other miRNAs [26,27,28,29]

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