Integrated microRNA and proteome analysis of cancer datasets with MoPC

MicroRNAs (miRNAs) are small molecules that play an essential role in regulating gene expression by post-transcriptional gene silencing. Their study is crucial in revealing the fundamental processes underlying pathologies and, in particular, cancer. To date, most studies on miRNA regulation consider the effect of specific miRNAs on specific target mRNAs, providing wet-lab validation. However, few tools have been developed to explain the miRNA-mediated regulation at the protein level. In this paper, the MoPc computational tool is presented, that relies on the partial correlation between mRNAs and proteins conditioned on the miRNA expression to predict miRNA-target interactions in multi-omic datasets. MoPc returns the list of significant miRNA-target interactions and plot the significant correlations on the heatmap in which the miRNAs and targets are ordered by the chromosomal location. The software was applied on three TCGA/CPTAC datasets (breast, glioblastoma, and lung cancer), returning enriched results in three independent targets databases. Author summary According to the central dogma of molecular biology, DNA is transcribed into RNA and subsequently translated into proteins. However, many molecules affect the amount of protein produced, including microRNAs (miRNAs). They can inhibit the translation or intervene by implementing the decay of target mRNAs. In literature, most works focus on describing the effect of miRNAs on mRNA targets, while only a few tools integrate protein expression profiles. MoPc predicts miRNA-targets interaction by considering the expression of mRNA, proteins, and miRNAs simultaneously. The method is based on the partial correlation measure between mRNAs and proteins conditioned by the expression of the miRNAs. The results on TCGA/CPTAC datasets prove the relevance of the MoPc method both from a computational and a biological point of view.