A pan-cancer modular regulatory network analysis to identify common and cancer-specific network components.
Many human diseases including cancer are the result of perturbations to transcriptional regulatory networks that control context-specific expression of genes. A comparative approach across multiple cancer types is a powerful approach to illuminate the common and specific network features of this family of diseases. Recent efforts from The Cancer Genome Atlas (TCGA) have generated large collections of functional genomic data sets for multiple types of cancers. An emerging challenge is to devise computational approaches that systematically compare these genomic data sets across different cancer types that identify common and cancer-specific network components. We present a module- and network-based characterization of transcriptional patterns in six different cancers being studied in TCGA: breast, colon, rectal, kidney, ovarian, and endometrial. Our approach uses a recently developed regulatory network reconstruction algorithm, modular regulatory network learning with per gene information (MERLIN), within a stability selection framework to predict regulators for individual genes and gene modules. Our module-based analysis identifies a common theme of immune system processes in each cancer study, with modules statistically enriched for immune response processes as well as targets of key immune response regulators from the interferon regulatory factor (IRF) and signal transducer and activator of transcription (STAT) families. Comparison of the inferred regulatory networks from each cancer type identified a core regulatory network that included genes involved in chromatin remodeling, cell cycle, and immune response. Regulatory network hubs included genes with known roles in specific cancer types as well as genes with potentially novel roles in different cancer types. Overall, our integrated module and network analysis recapitulated known themes in cancer biology and additionally revealed novel regulatory hubs that suggest a complex interplay of immune response, cell cycle, and chromatin remodeling across multiple cancers.
Wisconsin Institute for Discovery, University of Wisconsin, Madison, WI, USA.
Sara A
SA
Knaack
Wisconsin Institute for Discovery, University of Wisconsin, Madison, WI, USA. ; Department of Computer Sciences, University of Wisconsin, Madison, WI, USA.
Alireza Fotuhi
AF
Siahpirani
Wisconsin Institute for Discovery, University of Wisconsin, Madison, WI, USA. ; Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, WI, USA.
Sushmita
S
Roy
Modular regulatory network inference; Pan-cancer analysis; Probabilistic graphical models; Regulatory modules; Stability selection; The Cancer Genome Atlas (TCGA); Transcriptional regulatory networks