Study of the dynamic feature of microRNA-mRNA interaction. The functional study of miRNA is heavily dependent on the reliable identification of miRNA-mRNA interactions, in view of the fact that miRNAs inference the biological systems through binding to their targets. In this respect, many computational methods have been developed to predict miRNA binding sites through exclusively searching for the complementary nucleotide sequence within the seed region of miRNA (2nd-8th bases on the 3’ end). However, recent discoveries suggested that such predictions are highly biased and the miRNA-mRNA interaction represent a highly complex dynamic process, e.g. that multiple miRNA may bind to the same gene, namely combinatory feature and that the single miRNA may be interacted by hundreds of target, namely competitive feature. We have developed a new computational method that integrates both multidimensional genomic data on microRNA and gene in obesity and cancer to model the dynamic regulation of miRNA in different human disease. The estimation of impacts of TF and other factors (copy number variation, DNA methylation, etc.) were performed in this analysis in order to uncover the real miRNA regulators for each gene under each cancer developmental condition. Specifically, this new pipeline was built based on a meta-Lasso regression model and the proof-of-concept study was performed based on a large-scale genomic dataset from ~4,200 patients with 9 cancer types. Through the analysis, 10,726 microRNA-mRNA interactions that are associated with a specific stage and/or type of cancer were identified. Via highly selective microRNA-mRNA binding, we detected 4,134 regulatory modules that demonstrate high fidelity of microRNA function through modulation. For example, (miR-18a*, -320a, -193b, and -92b) co-regulates the glycolysis/gluconeogenesis and focal adhesion in cancers of kidney, liver, lung, and uterus. We have developed a microRNA Dynamic Regulation Database (miRDR) to archive all the identified miRNA regulatory modules in each cancer, which can be accessed at http://sbbi.unl.edu/miRDR. The R implement of the proposed method is also available to download from the miRDR. The discovery has been documented in a manuscript that is currently under review.
Shu J, Silva B, Cui J#. Dynamic and Modularized MicroRNA Regulation and Its Implication in Human Cancer. (under revision at Scientific Report).