INTS-MFS: A novel method to predict microRNA-disease associations by integrating network topology similarity and microRNA function similarity

Buwen Cao,Sainan Xiao,Kai Zhao,Jiawei Luo,Shuling Yang

doi:10.32604/biocell.2022.017538

Abstract

Identifying associations between microRNAs (miRNAs) and diseases is very important to understand the occurrence and development of human diseases. However, these existing methods suffer from the following limitation: first, some disease-related miRNAs are obtained from the miRNA functional similarity networks consisting of heterogeneous data sources, i.e., disease similarity, protein interaction network, gene expression. Second, little approaches infer disease-related miRNAs depending on the network topological features without the functional similarity of miRNAs. In this paper, we develop a novel model of Integrating Network Topology Similarity and MicroRNA Function Similarity (INTS-MFS). The integrated miRNA similarities are calculated based on miRNA functional similarity and network topological characteristics. INTS-MFS obtained AUC of 0.872 based on five-fold cross-validation and was applied to three common human diseases in case studies. As a results, 30 out of top 30 predicted Prostatic Neoplasm-related miRNAs were included in the two databases of dbDEMC and PhenomiR2.0. 29 out of top 30 predicted Lung Neoplasm-related miRNAs and Breast Neoplasm-related miRNAs were included in dbDEMC, PhenomiR2.0 and experimental reports. Moreover, INTS-MFS found unknown association with hsa-mir-371a in breast cancer and lung cancer, which have not been reported. It provides biologists new clues for diagnosing breast and lung cancer.

Highlights

MicroRNAs are approximately 22-nucleotide noncoding RNAs, act as an important regulator involved in posttranscriptional regulation of gene expression (Bartel, 2004)
We developed a model of Integrating Network Topology Similarity and MicroRNA Function Similarity (INTS-MFS) for identifying miRNA-disease association
The predicted disease-related miRNAs of three major human diseases: breast neoplasm, lung neoplasm and prostatic neoplasm were respectively confirmed by the human disease databases and experimental reports

Summary

Introduction

MicroRNAs (miRNAs) are approximately 22-nucleotide noncoding RNAs, act as an important regulator involved in posttranscriptional regulation of gene expression (Bartel, 2004). Many computational methods have been developed for miRNA-disease association prediction (Xuan et al, 2015; You et al, 2017; Zeng et al, 2016; Zou et al, 2016). The accuracy of this method was serious restricted by predicted miRNA-target interactions only with the information of miRNA neighbors. Shi et al (2013) developed a random walk analysis method to rank miRNA-disease pairs by searching for functional associations between miRNAs targets and diseases genes in proteinprotein interaction network. Chen et al (2017) proposed a model named RKNNMDA (Ranking-based K-Nearest Neighbors for MiRNA–Disease Association prediction) to search the k-nearest neighbors of miRNAs and diseases The accuracy of this method was serious restricted by predicted miRNA-target interactions only with the information of miRNA neighbors. Shi et al (2013) developed a random walk analysis method to rank miRNA-disease pairs by searching for functional associations between miRNAs targets and diseases genes in proteinprotein interaction network. Chen et al (2017) proposed a model named RKNNMDA (Ranking-based K-Nearest Neighbors for MiRNA–Disease Association prediction) to search the k-nearest neighbors of miRNAs and diseases

Methods

Results

Conclusion