A New Algorithm for Integrated Analysis of miRNA-mRNA Interactions Based on Individual Classification Reveals Insights into Bladder Cancer

Abstract

BackgroundMicroRNAs (miRNAs) are small non-coding RNAs that regulate gene expression. It has been proposed that miRNAs play an important role in cancer development and progression. Their ability to affect multiple gene pathways by targeting various mRNAs makes them an interesting class of regulators.Methodology/Principal FindingsWe have developed an algorithm, Classification based Analysis of Paired Expression data of RNA (CAPE RNA), which is capable of identifying altered miRNA-mRNA regulation between tissues samples that assigns interaction states to each sample without preexisting stratification of groups. The distribution of the assigned interaction states compared to given experimental groups is used to assess the quality of a predicted interaction. We demonstrate the applicability of our approach by analyzing urothelial carcinoma and normal bladder tissue samples derived from 24 patients. Using our approach, normal and tumor tissue samples as well as different stages of tumor progression were successfully stratified. Also, our results suggest interesting differentially regulated miRNA-mRNA interactions associated with bladder tumor progression.Conclusions/SignificanceThe need for tools that allow an integrative analysis of microRNA and mRNA expression data has been addressed. With this study, we provide an algorithm that emphasizes on the distribution of samples to rank differentially regulated miRNA-mRNA interactions. This is a new point of view compared to current approaches. From bootstrapping analysis, our ranking yields features that build strong classifiers. Further analysis reveals genes identified as differentially regulated by miRNAs to be enriched in cancer pathways, thus suggesting biologically interesting interactions.

A review of computational approaches detecting microRNAs involved in cancer

“…The aim of the methodologies belonging to group (i) is the identification of co-expressed miRNA-mRNA couples whose behavior is altered in cancer. Such couples are generally identified by combining miRNA target prediction with miRNA/mRNA expression profiles correlation (57)(58)(59)(60)(61), or by more sophisticated approaches integrating miRNA/mRNA expression with sequence complementarity (62)(63)(64), or with other strategies capturing the miRNA-mRNA pairs without taking into account target complementarity (65-68). Among the results obtained by these methods, the miR-29 family was identified to recurrently regulate the DNA demethylation pathway in (60) and a signature of four miRNAs (miR-320d, miR-139-5p, miR-567 and let-7c) was proposed for breast cancer grade classification in (61).…”

The modularity and dynamicity of miRNA–mRNA interactions in high-grade serous ovarian carcinomas and the prognostic implication

“…We proposed a novel algorithm to measure the strength of sample-specific miRNA-mRNA interaction (Hecker et al , 2013). It includes the following steps.…”

Screening biomarkers of bladder cancer using combined miRNA and mRNA microarray analysis

“…The GSE40355 expression profile was obtained from the Gene Expression Omnibus (http://www.ncbi.nlm.nih.gov/geo/), which was based on the platforms of GPL8227-Agilent-019118 human miRNA microarray 2.0 G4470B (miRNA ID version) and GPL13497-Agilent-026652 whole human genome microarray 4x44K v2 (probe name version). This dataset was deposited by Hecker et al (18). Using healthy bladder tissue samples (n=8) and urothelial carcinoma samples (low-grade, n=8; high-grade, n=8), miRNA and mRNA microarray expression profiling analyses were performed.…”