CanMod

Abstract

Transcription factors (TFs) and microRNAs (miRNAs) are two important classes of gene regulators that govern many critical biological processes. Dysregulation of TF-gene and miRNA-gene interactions can lead to the development of multiple diseases including cancer. Many studies aimed to identify interactions between target genes and their regulators in both normal and disease settings. However, few studies attempted to elucidate the collaborative relationship between TFs and miRNAs in regulating genes involved in cancer-associated biological processes. Identification of the co-regulatory functions of those regulators in cancer would provide a better understanding of gene regulation at different layers and may also suggest better approaches for targeted therapy. This study proposes a computational pipeline called CanMod to identify cancer-associated gene regulatory modules. CanMod was designed so that it could infer gene regulatory modules that meet three criteria. First, within a module, target genes should involve in similar biological processes; thus, the modules are distinguishable based on their biological functions. Second, the expression of target genes in a module should be collectively dependent on the expression of their regulators. Third, a regulator and a target should be allowed to be included in multiple modules to reflect the diverse biological roles that the genes and the regulators may be responsible for. CanMod also incorporates other regulatory factors such as copy number alteration and DNA methylation data to infer regulator-target gene interactions with higher accuracy. We applied CanMod on the breast cancer dataset (BRCA) from The Cancer Genome Atlas (TCGA). We found that modules found by CanMod were associated with distinguishable biological functions and the expression of target genes in the modules were significantly correlated. In addition, many hub regulators in CanMod were known cancer genes, and CanMod was able to find experimentally validated regulator-target interactions.