An efficient framework to identify key miRNA–mRNA regulatory modules in cancer

• Published in: Bioinformatics 2020-12, Vol.36 (Supplement_2), p.i592-i600
• Main Authors: Mokhtaridoost, Milad, Gönen, Mehmet
• Format: Article
• Language: English
• Subjects: Computational Biology; Gene Expression Profiling; Gene Expression Regulation; Gene Expression Regulation, Neoplastic; Gene Regulatory Networks; Humans; MicroRNAs - genetics; Neoplasms - genetics; RNA, Messenger - genetics
• ISSN: 1367-4803; 1460-2059; 1367-4811
• DOI: 10.1093/bioinformatics/btaa798

Abstract Motivation Micro-RNAs (miRNAs) are known as the important components of RNA silencing and post-transcriptional gene regulation, and they interact with messenger RNAs (mRNAs) either by degradation or by translational repression. miRNA alterations have a significant impact on the formation and progression of human cancers. Accordingly, it is important to establish computational methods with high predictive performance to identify cancer-specific miRNA–mRNA regulatory modules. Results We presented a two-step framework to model miRNA–mRNA relationships and identify cancer-specific modules between miRNAs and mRNAs from their matched expression profiles of more than 9000 primary tumors. We first estimated the regulatory matrix between miRNA and mRNA expression profiles by solving multiple linear programming problems. We then formulated a unified regularized factor regression (RFR) model that simultaneously estimates the effective number of modules (i.e. latent factors) and extracts modules by decomposing regulatory matrix into two low-rank matrices. Our RFR model groups correlated miRNAs together and correlated mRNAs together, and also controls sparsity levels of both matrices. These attributes lead to interpretable results with high predictive performance. We applied our method on a very comprehensive data collection by including 32 TCGA cancer types. To find the biological relevance of our approach, we performed functional gene set enrichment and survival analyses. A large portion of the identified modules are significantly enriched in Hallmark, PID and KEGG pathways/gene sets. To validate the identified modules, we also performed literature validation as well as validation using experimentally supported miRTarBase database. Availability and implementation Our implementation of proposed two-step RFR algorithm in R is available at https://github.com/MiladMokhtaridoost/2sRFR together with the scripts that replicate the reported experiments. Supplementary information Supplementary data are available at Bioinformatics online.