Welcome to MR4Cancer

Cellular phenotype transitions in cancer are determined by the shift of regulatory networks in response to external or internal signals. A Master Regulator (MR) is a gene that locates at the hub of a regulatory network and controls a large number of targets (collectively termed as its regulon). MRs are very highly connected and have been demonstrated to be involved in the response of cells to disease. Identification of aberrant MRs in a specific context can provide new insights for cancer diagnosis, prognosis, and therapeutics.

MR4Cancer can prioritize MRs driving tumor phenotype divergence of interest. Based on the input gene list or expression profiles, it outputs ranked MRs by enrichment testing against the pre-defined regulons (i.e., cancer-specific and noncancer-sepecific regulons). At the same time, Gene Ontology (GO) term and canonical pathway analyses are also conducted to further elucidate the function of MR candidates. In addition, we provide a cutting-edge network visualization tool for interactions between MRs and their targets, which can be interactively interrogated to produce high-quality figures for publications.

The input is either a gene list (Scenario 1) or a mRNA expression matrix in two conditions (Scenario 2). Over-Representation Analysis (ORA) is used to evaluate the statistical significance of overlap between a gene list and the predefined regulons based on hypergeometric test. For the expression matrix, differential expression analysis is conducted firstly, and then users can choose either the top differentially expressed genes to analyze or genome-wide sorted gene list for Gene Set Enrichment Analysis (GSEA). The output are seven groups of ranked MRs with function annotations based on GO terms and pathways. Click here for more details about the workflow of MR4Cancer.