Identifying miRNA-mRNA Integration Set Associated With Survival Time.

Yongkang Kim,Taesung Park,Jin-Young Jang,Seungyeoun Lee,Sungyoung Lee

doi:10.3389/fgene.2021.634922

Abstract

In the “personalized medicine” era, one of the most difficult problems is identification of combined markers from different omics platforms. Many methods have been developed to identify candidate markers for each type of omics data, but few methods facilitate the identification of multiple markers on multi-omics platforms. microRNAs (miRNAs) is well known to affect only indirectly phenotypes by regulating mRNA expression and/or protein translation. To take into account this knowledge into practice, we suggest a miRNA-mRNA integration model for survival time analysis, called mimi-surv, which accounts for the biological relationship, to identify such integrated markers more efficiently. Through simulation studies, we found that the statistical power of mimi-surv be better than other models. Application to real datasets from Seoul National University Hospital and The Cancer Genome Atlas demonstrated that mimi-surv successfully identified miRNA-mRNA integrations sets associated with progression-free survival of pancreatic ductal adenocarcinoma (PDAC) patients. Only mimi-surv found miR-96, a previously unidentified PDAC-related miRNA in these two real datasets. Furthermore, mimi-surv was shown to identify more PDAC related miRNAs than other methods because it used the known structure for miRNA-mRNA regularization. An implementation of mimi-surv is available at http://statgen.snu.ac.kr/software/mimi-surv.

Highlights

MicroRNAs are small, non-coding RNAs that function to regulate target messenger RNAs, based on sequence complementarity
Hierarchical clustering on miRNA expression profiles found that the expression levels of the tumor suppressor messenger RNAs (mRNAs)-miRNA Integration Set Survival Analysis gene, TP53 are associated with specific clusters (Enerly et al, 2011)
The proposed model introduces a latent variable for each miRNA and its target mRNAs as a component and fits one augmented model including all latent variables to determine the associations with the survival phenotype

Summary

INTRODUCTION

MicroRNAs (miRNAs) are small, non-coding RNAs that function to regulate target messenger RNAs (mRNAs), based on sequence complementarity. Like HisCoMmimi, mimi-surv is a component-based analysis, such as pathway models we developed for rare variant pathway analysis (Lee et al, 2016, 2019) In this respect, the proposed model introduces a latent variable for each miRNA and its target mRNAs as a component and fits one augmented model including all latent variables to determine the associations with the survival phenotype. We considered two real PDAC datasets; one is a microarray-based dataset from PDAC patients from Seoul National University Hospital (SNUH), and the other is high-throughput sequencing data, obtained from The Cancer Genome Atlas (TCGA) From those datasets, we tried to find prognostic factors for survival after surgery of PDAC by survival analysis on integrated miRNA-mRNA sets, using mimi-surv. The proposed mimi-surv was compared with many other survival analysis methods throughout the simulation studies

MATERIALS AND METHODS

RESULTS

Simulation Results

DISCUSSION