Dependency of the Cancer-Specific Transcriptional Regulation Circuitry on the Promoter DNA Methylome

• Main Authors: Yu Liu, Yang Liu, Rongyao Huang, Wanlu Song, Jiawei Wang, Zhengtao Xiao, Shengcheng Dong, Yang Yang, Xuerui Yang
• Format: Article
• Language: English
• Published: Elsevier 2019-03-01
• Series: Cell Reports
• Online Access: http://www.sciencedirect.com/science/article/pii/S2211124719302700

Summary: Dynamic dysregulation of the promoter DNA methylome is a signature of cancer. However, comprehensive understandings about how the DNA methylome is incorporated in the transcriptional regulation circuitry and involved in regulating the gene expression abnormality in cancers are still missing. We introduce an integrative analysis pipeline based on mutual information theory and tailored for the multi-omics profiling data in The Cancer Genome Atlas (TCGA) to systematically find dependencies of transcriptional regulation circuits on promoter CpG methylation profiles for each of 21 cancer types. By coupling transcription factors with CpG sites, this cancer type-specific transcriptional regulation circuitry recovers a significant layer of expression regulation for many cancer-related genes. The coupled CpG sites and transcription factors also serve as markers for classifications of cancer subtypes with different prognoses, suggesting physiological relevance of such regulation machinery recapitulated here. Our results therefore generate a resource for further studies of the epigenetic scheme in gene expression dysregulations in cancers. : Using an analysis pipeline based on information theory and tailored for cancer multi-omics data in TCGA, Yu et al. conducted genome-wide surveys of DNA promoter methylome in modulating transcriptional regulation circuits in cancers. Results serve as a resource for dissecting gene expression dysregulation in cancer. Keywords: DNA methylation, CpG dinucleotides, cancer type-specificity, transcriptional regulation, gene expression regulation, genome-wide analysis, transcriptional regulation network, conditional mutual information, pan-cancer analysis, MeTRN