miRGTF-net: Integrative miRNA-gene-TF network analysis reveals key drivers of breast cancer recurrence.

Analysis of regulatory networks is a powerful framework for identification and quantification of intracellular interactions. We introduce miRGTF-net, a novel tool for construction of miRNA-gene-TF networks. We consider multiple transcriptional and post-transcriptional interaction types, including regulation of gene and miRNA expression by transcription factors, gene silencing by miRNAs, and co-expression of host genes with their intronic miRNAs. The underlying algorithm uses information on experimentally validated interactions as well as integrative miRNA/mRNA expression profiles in a given set of samples. The latter ensures simultaneous tissue-specificity and biological validity of interactions. We applied miRGTF-net to paired miRNA/mRNA-sequencing data of breast cancer samples from The Cancer Genome Atlas (TCGA). Together with topological analysis of the constructed network we showed that considered players can form reliable prognostic gene signatures for ER-positive breast cancer. A number of signatures demonstrated remarkably high accuracy on transcriptomic data obtained by both microarrays and RNA sequencing from several independent patient cohorts. Furthermore, an essential part of prognostic genes were identified as direct targets of transcription factor E2F1. The putative interplay between estrogen receptor alpha and E2F1 was suggested as a potential recurrence factor in patients treated with tamoxifen. Source codes of miRGTF-net are available at GitHub (https://github.com/s-a-nersisyan/miRGTF-net).