Integrated analysis of the miRNA–mRNA next-generation sequencing data for finding their associations in different cancer types. (February 2020)

• Record Type: Journal Article
• Authors: Bhowmick, Shib Sankar; Bhattacharjee, Debotosh; Rato, Luis
• Is Part Of: Computational biology and chemistry. Volume 84(2020)
• Publication Date: 2020-02
• Subjects: MicroRNA -- Messenger RNA -- Next-generation sequencing data -- Feature selection -- Biomarker; Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2019.107152
• Languages: English
• ISSNs: 1476-9271

Abstract

Abstract: microRNAs (miRNAs) are short, non-coding, endogenous RNA molecule that regulates messenger RNAs (mRNAs) at the post-transcriptional level. The discovery of this regulatory relationship between miRNAs and mRNAs is an important research direction. In this regard, our method proposed an integrated approach to identify the mRNA targets of dysregulated miRNAs using next-generation sequencing data from six cancer types. For this analysis, a sensible combination of data mining tools is chosen. In particular, Random Forest, log-transformed Fold change, and Pearson correlation coefficient are considered to find the potential miRNA–mRNA pairs. During this study, we have identified six cancer-specific overlapping sets of miRNAs whose classification accuracy is always higher than 91%. Furthermore, a promising correlation signature of significantly dysregulated miRNAs and mRNAs are recognized. A comprehensive analysis found that the cumulative percentage of negative correlation coefficients is higher than its positive counterpart. Moreover, experimentally validated miRNA–target interactions databases called miRTarBase is used to validate significantly correlated mRNAs. According to our study, the smallest set of significantly dysregulated miRNAs is 43 in PRAD data, while for mRNAs the smallest set is 238 in the LUAD cancer type. The obtained miRNA–mRNA pairs are subjected to do pathway enrichment analysis and gene ontology analysis. Regulatory roles of these dysregulated miRNAs with associated diseases are identified by constructing a regulatory network between miRNAs and associated diseases. Moreover, the relation between miRNAs expression level and patient survival is also analyzed. To conclude, the miRNA–mRNA pairs identified in this study may serve as promising candidates for subsequent in-vitro validation.