Background Metastatic progress is the primary cause of death in most cancers, yet the regulatory dynamics driving the cellular changes necessary for metastasis remain poorly understood. Multi-omics approaches hold great promise for addressing this challenge; however, current analysis tools have limited capabilities to systematically integrate transcriptomic, epigenomic, and cistromic information to accurately define the regulatory networks critical for metastasis. Results To address this limitation, we use a purposefully generated cellular model of colon cancer invasiveness to generate multi-omics data, including expression, accessibility, and selected histone modification profiles, for increasing levels of invasiveness. We then adopt a rigorous probabilistic framework for joint inference from the resulting heterogeneous data, along with transcription factor binding profiles. Our approach uses probabilistic graphical models to leverage the functional information provided by specific epigenomic changes, models the influence of multiple transcription factors simultaneously, and automatically learns the activating or repressive roles of cis-regulatory events. Global analysis of these relationships reveals key transcription factors driving invasiveness, as well as their likely target genes. Disrupting the expression of one of the highly ranked transcription factors JunD, an AP-1 complex protein, confirms functional relevance to colon cancer cell migration and invasion. Transcriptomic profiling confirms key regulatory targets of JunD, and a gene signature derived from the model demonstrates strong prognostic potential in TCGA colorectal cancer data. Conclusions Our work sheds new light into the complex molecular processes driving colon cancer metastasis and presents a statistically sound integrative approach to analyze multi-omics profiles of a dynamic biological process.
Differential gene expression in bulk transcriptomics data can reflect regulated change of transcript abundance within a cell type and/or change in the proportion of cell types within the sample. To differentiate these scenarios, bulk expression deconvolution methods have been developed, which reveal cell type proportions and transcriptomes at the larger scales afforded by bulk RNA-seq compared to single-cell RNA-seq. However, the accuracy of these methods is highly sensitive to technical and biological differences between bulk profiles and the cell type-signatures required as references during deconvolution. We present BEDwARS, a Bayesian deconvolution method specifically designed to address potential differences between reference signatures and true but unknown signatures underlying the bulk transcriptomic profiles. Through extensive benchmarking utilizing eight different datasets derived from pancreas and brain, and by generating additional noisy reference signatures, we demonstrate that BEDwARS outperforms leading in-class methods for estimating cell type proportions and is more robust to noise in reference signatures. Furthermore, it more accurately estimates true cell type signatures compared to the state-of-the-art method. Application of BEDwARS to newly generated RNA-seq and scRNA-seq data on a rare pediatric condition (Dihydropyridine Dehydrogenase deficiency) revealed the possible involvement of ciliopathy and impaired translational control in the etiology of the disorder.
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.