Novel insights into the molecular mechanisms underlying risk of colorectal cancer from smoking and red/processed meat carcinogens by modeling exposure in normal colon organoids
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Matthew Devall1,2, Christopher H. Dampier1,2, Stephen Eaton1,2, Mourad W. Ali1,2, Virginia Díez-Obrero3,4,5,6, Ferran Moratalla-Navarro3,4,5,6, Jennifer Bryant1,2, Lucas T. Jennelle1,2, Victor Moreno3,4,5,6, Steven M. Powell7, Ulrike Peters8 and Graham Casey1,2
1 Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
2 Department of Public Health Sciences, University of Virginia, Charlottesville, VA, USA
3 Oncology Data Analytics Program, Catalan Institute of Oncology (ICO), L’Hospitalet de Llobregat, Barcelona, Spain
4 Colorectal Cancer Group, ONCOBELL Program, Bellvitge Biomedical Research Institute (IDIBELL), L’Hospitalet de Llobregat, Barcelona, Spain
5 Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), Madrid, Spain
6 Department of Clinical Sciences, Faculty of Medicine, University of Barcelona, Barcelona, Spain
7 Digestive Health Center, University of Virginia, Charlottesville, VA, USA
8 Public Health Sciences Division, Fred Hutchinson Cancer Center Research Institute, Seattle, WA, USA
Keywords: colon organoids; microsatellite instability; smoking; single-cell deconvolution; weighted gene co-expression network analysis
Received: July 15, 2021 Accepted: August 13, 2021 Published: September 14, 2021
Tobacco smoke and red/processed meats are well-known risk factors for colorectal cancer (CRC). Most research has focused on studies of normal colon biopsies in epidemiologic studies or treatment of CRC cell lines in vitro. These studies are often constrained by challenges with accuracy of self-report data or, in the case of CRC cell lines, small sample sizes and lack of relationship to normal tissue at risk. In an attempt to address some of these limitations, we performed a 24-hour treatment of a representative carcinogens cocktail in 37 independent organoid lines derived from normal colon biopsies. Machine learning algorithms were applied to bulk RNA-sequencing and revealed cellular composition changes in colon organoids. We identified 738 differentially expressed genes in response to carcinogens exposure. Network analysis identified significantly different modules of co-expression, that included genes related to MSI-H tumor biology, and genes previously implicated in CRC through genome-wide association studies. Our study helps to better define the molecular effects of representative carcinogens from smoking and red/processed meat in normal colon epithelial cells and in the etiology of the MSI-H subtype of CRC, and suggests an overlap between molecular mechanisms involved in inherited and environmental CRC risk.
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