ObjectiveWhile oesophageal squamous cell carcinoma remains infrequent in Western populations, the incidence of oesophageal adenocarcinoma (EAC) has increased sixfold to eightfold over the past four decades. We aimed to characterise oesophageal cancer-specific and subtypes-specific gene regulation patterns and their upstream transcription factors (TFs). DesignTo identify regulatory elements, we profiled fresh-frozen oesophageal normal samples, tumours and cell lines with chromatin immunoprecipitation sequencing (ChIP-Seq). Mathematical modelling was performed to establish (super)-enhancers landscapes and interconnected transcriptional circuitry formed by master TFs. Coregulation and cooperation between master TFs were investigated by ChIP-Seq, circularised chromosome conformation capture sequencing and luciferase assay. Biological functions of candidate factors were evaluated both in vitro and in vivo.ResultsWe found widespread and pervasive alterations of the (super)-enhancer reservoir in both subtypes of oesophageal cancer, leading to transcriptional activation of a myriad of novel oncogenes and signalling pathways, some of which may be exploited pharmacologically (eg, leukemia inhibitory factor (LIF) pathway). Focusing on EAC, we bioinformatically reconstructed and functionally validated an interconnected circuitry formed by four master TFs—ELF3, KLF5, GATA6 and EHF—which promoted each other’s expression by interacting with each super-enhancer. Downstream, these master TFs occupied almost all EAC super-enhancers and cooperatively orchestrated EAC transcriptome. Each TF within the transcriptional circuitry was highly and specifically expressed in EAC and functionally promoted EAC cell proliferation and survival.ConclusionsBy establishing cancer-specific and subtype-specific features of the EAC epigenome, our findings promise to transform understanding of the transcriptional dysregulation and addiction of EAC, while providing molecular clues to develop novel therapeutic modalities against this malignancy.
Motivation DNA methylation has been used to identify functional changes at transcriptional enhancers and other cis-regulatory modules (CRMs) in tumors and other disease tissues. Our R/Bioconductor package ELMER (Enhancer Linking by Methylation/Expression Relationships) provides a systematic approach that reconstructs altered gene regulatory networks (GRNs) by combining enhancer methylation and gene expression data derived from the same sample set. Results We present a completely revised version 2 of ELMER that provides numerous new features including an optional web-based interface and a new Supervised Analysis mode to use pre-defined sample groupings. We show that Supervised mode significantly increases statistical power and identifies additional GRNs and associated Master Regulators, such as SOX11 and KLF5 in Basal-like breast cancer. Availability and implementation ELMER v.2 is available as an R/Bioconductor package at http://bioconductor.org/packages/ELMER/. Supplementary information Supplementary data are available at Bioinformatics online.
Gastrointestinal adenocarcinomas (GIAC) of the tubular GI tract including esophagus, stomach, colon, and rectum comprise most GI cancers and share a spectrum of genomic features. However, the unified epigenomic changes specific to GIAC are poorly characterized. Using 907 GIAC samples from The Cancer Genome Atlas, we applied mathematical algorithms to large-scale DNA methylome and transcriptome profiles to reconstruct transcription factor (TF) networks and identify a list of functionally hyperactive master regulator (MR) TF shared across different GIAC. The top candidate HNF4A exhibited prominent genomic and epigenomic activation in a GIAC-specific manner. A complex interplay between the HNF4A promoter and three distal enhancer elements was coordinated by GIAC-specific MRTF including ELF3, GATA4, GATA6 and KLF5; HNF4A also self-regulated its own promoter and enhancers. Functionally, HNF4A promoted cancer proliferation and survival by transcriptional activation of many downstream targets, including HNF1A and factors of Interleukin signaling, in a lineage-specific manner. Overall, our study provides new insights into the GIAC-specific gene regulatory networks and identifies potential therapeutic strategies against these common cancers. Statement of significance: Findings show that GIAC-specific master regulatory transcription factors control HNF4A via three distal enhancers to promote GIAC cell proliferation and survival. Research.
Recent studies indicate that DNA methylation can be used to identify changes at transcriptional enhancers and other cis-regulatory elements in primary human samples. A systematic approach to inferring gene regulatory networks has been provided by the R/Bioconductor package ELMER (Enhancer Linking by Methylation/Expression Relationships), which first identifies DNA methylation changes in distal regulatory elements and correlates these with the expression of nearby genes to identify direct transcriptional targets. Next, ELMER performs a transcription factor binding motif analysis and integrates with expression profiling of all human transcription factors, to identify master regulatory TFs and place each differentially methylated regulatory element into the context of an altered gene regulatory network (GRN).Here we present a completely updated version of the package (ELMER v. 2.0), which uses the latest Bioconductor data structures including the popular MultiAsssayExperiment, supports multiple reference genome assemblies as well as the DNA methylation platforms Infinium MethylationEPIC and Infinium HumanMethylation450, and provides a "Supervised" analysis mode for paired sample study designs (such as treated vs. untreated replicate samples). It also supports data import from the new NCI Genomic Data Commons (GDC) database. The new version is substantially re-written, improving stability, performance, and extensibility. It also uses improved databases for transcription factor binding domain families and binding motif specificities, and has newly designed output plots for publication-quality figures.Below, we describe the methods and new features of ELMER v. 2.0 and present two use case demonstrating how the tool can be used to analyze TCGA data in either Unsupervised or Supervised mode. ELMER (v2.0.0) is available as an R/Bioconductor package at
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.