A novel two-stage method for identifying microRNA-gene regulatory modules in breast cancer

In this paper, we propose a two-stage method for identifying miRNA-gene regulatory modules by integrating miRNA/mRNA expression profiles and miRNA genomic cluster data. We first adopt a Multiple-output Sparse Group Lasso (MSGL) regression model to predict the miRNA-gene regulatory network. Further, we propose a L0-penalized Singular Value Decomposition (L0-SVD) model to identify modules from the predicted network. We apply this method to miRNA and mRNA expression profiles of the breast cancer data from TCGA databases and identify ten miRNA-gene regulatory modules. We find that (1) the modules are significantly associated in a predicted miRNA-gene regulatory network; (2) the modules are significantly enriched in GO biological processes and KEGG pathways, respectively; (3) many miRNAs and genes in the modules are related with breast cancer. On average, 51% of the miRNAs and 30% of the genes are related with breast cancer. The results demonstrate that miRNA-gene regulatory modules provide insights into the mechanisms of the combinatorial regulation between miRNAs and genes.