1. Omics for prediction of environmental health effects: Blood leukocyte-based cross-omic profiling reliably predicts diseases associated with tobacco smoking
- Author
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Antonis Myridakis, Mark Steven Gilthorpe, Domenico PALLI, Po-Hsiu Kuo, Chuhsing kate Hsiao, PANAGIOTIS GEORGIADIS, Fatemeh Saberi Hosnijeh, Päivi Ruokojärvi, Dennie Hebels, HUNG HUNG, Wen-Chung Lee, KUO-LIONG CHIEN, Benedetta Bendinelli, Rosario Tumino, Wei Jen Chen, Julian Krauskopf, Soterios Kyrtopoulos, Irene Liampa, Sarah Fleming, Alexandros Siskos, Marc Chadeau-Hyam, Yu-Kang Tu, CBITE, Toxicogenomics, RS: MERLN - Cell Biology - Inspired Tissue Engineering (CBITE), RS: GROW - R1 - Prevention, and Promovendi ODB
- Subjects
0301 basic medicine ,Male ,Lung Neoplasms ,Molecular biology ,EnviroGenomarkers consortium ,Genome-wide association study ,Disease ,Coronary Artery Disease ,Bioinformatics ,EPIDEMIOLOGY ,Medicine ,Whole blood ,RISK ,education.field_of_study ,Multidisciplinary ,GENE-EXPRESSION SIGNATURES ,Smoking ,Public Health, Global Health, Social Medicine and Epidemiology ,Environmental exposure ,Middle Aged ,Multidisciplinary Sciences ,DNA methylation ,Science & Technology - Other Topics ,Female ,Environmental Health ,Population ,Predictive markers ,Article ,03 medical and health sciences ,HODGKIN-LYMPHOMA ,WHOLE-BLOOD ,Humans ,Genetic Predisposition to Disease ,Lung cancer ,education ,METAANALYSIS ,Science & Technology ,business.industry ,Gene Expression Profiling ,Computational Biology ,Environmental Exposure ,DEGRADATION ,DNA Methylation ,medicine.disease ,Gene expression profiling ,Folkhälsovetenskap, global hälsa, socialmedicin och epidemiologi ,030104 developmental biology ,Risk factors ,CIGARETTE-SMOKING ,INHIBITORS ,business ,LUNG ,Genome-Wide Association Study - Abstract
The utility of blood-based omic profiles for linking environmental exposures to their potential health effects was evaluated in 649 individuals, drawn from the general population, in relation to tobacco smoking, an exposure with well-characterised health effects. Using disease connectivity analysis, we found that the combination of smoking-modified, genome-wide gene (including miRNA) expression and DNA methylation profiles predicts with remarkable reliability most diseases and conditions independently known to be causally associated with smoking (indicative estimates of sensitivity and positive predictive value 94% and 84%, respectively). Bioinformatics analysis reveals the importance of a small number of smoking-modified, master-regulatory genes and suggest a central role for altered ubiquitination. The smoking-induced gene expression profiles overlap significantly with profiles present in blood cells of patients with lung cancer or coronary heart disease, diseases strongly associated with tobacco smoking. These results provide proof-of-principle support to the suggestion that omic profiling in peripheral blood has the potential of identifying early, disease-related perturbations caused by toxic exposures and may be a useful tool in hazard and risk assessment.
- Published
- 2016