Prediction of Potential Small Molecule-Associated MicroRNAs Using Graphlet Interaction.

Na-Na Guan,Ya-Zhou Sun,Jian-Qiang Li,Zhong Ming,Xing Chen

doi:10.3389/fphar.2018.01152

Abstract

MicroRNAs (miRNAs) have been proved to be targeted by the small molecules recently, which made using small molecules to target miRNAs become a possible therapy for human diseases. Therefore, it is very meaningful to investigate the relationships between small molecules and miRNAs, which is still yet in the newly-developing stage. In this paper, we presented a prediction model of Graphlet Interaction based inference for Small Molecule-MiRNA Association prediction (GISMMA) by combining small molecule similarity network, miRNA similarity network and known small molecule-miRNA association network. This model described the complex relationship between two small molecules or between two miRNAs using graphlet interaction which consists of 28 isomers. The association score between a small molecule and a miRNA was calculated based on counting the numbers of graphlet interaction throughout the small molecule similarity network and the miRNA similarity network, respectively. Global and two types of local leave-one-out cross validation (LOOCV) as well as five-fold cross validation were implemented in two datasets to evaluate GISMMA. For Dataset 1, the AUCs are 0.9291 for global LOOCV, 0.9505, and 0.7702 for two local LOOCVs, 0.9263 ± 0.0026 for five-fold cross validation; for Dataset 2, the AUCs are 0.8203, 0.8640, 0.6591, and 0.8554 ± 0.0063, in turn. In case study for small molecules, 5-Fluorouracil, 17β-Estradiol and 5-Aza-2′-deoxycytidine, the numbers of top 50 miRNAs predicted by GISMMA and validated to be related to these three small molecules by experimental literatures are in turn 30, 29, and 25. Based on the results from cross validations and case studies, it is easy to realize the excellent performance of GISMMA.

Highlights

MicroRNAs are a family of small non-coding RNAs, having about 22 nucleotides in length, which regulate gene expression at a post-transcriptional level (Ambros, 2003)
This paper presented a graphlet interaction based method GISMMA to infer the potential associations between small molecules and miRNAs by combining small molecule similarity, miRNA similarity and known associations between small molecules and miRNAs
The performance of GISMMA on predicting novel small molecule-miRNA associations was evaluated with four validation approaches that were global and two types of local leave-one-out cross validation (LOOCV), as well as five-fold cross validation

Summary

Introduction

MicroRNAs (miRNAs) are a family of small non-coding RNAs, having about 22 nucleotides in length, which regulate gene expression at a post-transcriptional level (Ambros, 2003). The first miRNA was discovered over 30 years ago in the Caenorhabditis elegans. Small Molecule-microRNA Association Prediction complementarity is imperfect (He and Hannon, 2004) In this manner, they play a critical role in a variety of crucial processes such as tissue development, morphogenesis, apoptosis, signal transduction pathways, etc., (Esquela-Kerscher and Slack, 2006; Spizzo et al, 2009; Wang and Lee, 2009). They play a critical role in a variety of crucial processes such as tissue development, morphogenesis, apoptosis, signal transduction pathways, etc., (Esquela-Kerscher and Slack, 2006; Spizzo et al, 2009; Wang and Lee, 2009) This implicates them in an array of disease associated processes. The development of large-expression screens has been proven useful in identifying novel miRNAs involved in diseases, which could potentially become an attractive therapeutic target (Monroig and Calin, 2013; Chen et al, 2017a, 2018a,b,c; Matsui and Corey, 2017)

Methods

Results

Conclusion