ArticlePloS one2014
Genomic models of short-term exposure accurately predict long-term chemical carcinogenicity and identify putative mechanisms of action.
Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.
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Who cites it
25 citing papers in PubMed, 1 synthesis or guideline pooled it, 68 citations in OpenAlex.
- Systematic transcriptome-wide meta-analysis across endocrine disrupting chemicals reveals shared and unique liver pathways, gene networks, and disease associations.Environment international · 2024Pooled it
- Consensus findings of an International Workshops on Genotoxicity Testing workshop on using transcriptomic biomarkers to predict genotoxicity.Environmental and molecular mutagenesis · 2025Review
- Article
- A mechanism of global gene expression regulation is disrupted by multiple disease states and drug treatments.PloS one · 2025Article
- Artificial intelligence uncovers carcinogenic human metabolites.Nature chemical biology · 2022Article
- Integrated multi-omics analysis of adverse cardiac remodeling and metabolic inflexibility upon ErbB2 and ERRα deficiency.Communications biology · 2022Article
- Drug repositioning based on network-specific core genes identifies potential drugs for the treatment of autism spectrum disorder in children.Computational and structural biotechnology journal · 2021Article
- Effect of common medications on the expression of SARS-CoV-2 entry receptors in liver tissue.Archives of toxicology · 2020Article
- Classifying polycyclic aromatic hydrocarbons by carcinogenic potency using in vitro biosignatures.Toxicology in vitro : an international journal published in association with BIBRA · 2020Article
- Identification of putative calorie restriction mimetics using mammalian gene expression profiles.Open biology · 2020Article
- A Set of Six Gene Expression Biomarkers Identify Rat Liver Tumorigens in Short-term Assays.Toxicological sciences : an official journal of the Society of Toxicology · 2020Article
- Drug vector representation: a tool for drug similarity analysis.Molecular genetics and genomics : MGG · 2020Article
- A cross-sector call to improve carcinogenicity risk assessment through use of genomic methodologies.Regulatory toxicology and pharmacology : RTP · 2020Article
- The Carcinogenome Project: In Vitro Gene Expression Profiling of Chemical Perturbations to Predict Long-Term Carcinogenicity.Environmental health perspectives · 2019Article
- A novel, integrated in vitro carcinogenicity test to identify genotoxic and non-genotoxic carcinogens using human lymphoblastoid cells.Archives of toxicology · 2018Article
- Transcriptional Responses Reveal Similarities Between Preclinical Rat Liver Testing Systems.Frontiers in genetics · 2018Article
- Kynurenic Acid Protects against Thioacetamide-Induced Liver Injury in Rats.Analytical cellular pathology (Amsterdam) · 2018Article
- Network and Pathway Analysis of Toxicogenomics Data.Frontiers in genetics · 2018Article
- A transcriptomics data-driven gene space accurately predicts liver cytopathology and drug-induced liver injury.Nature communications · 2017Article
- Network-based analysis of transcriptional profiles from chemical perturbations experiments.BMC bioinformatics · 2017Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundDespite an overall decrease in incidence of and mortality from cancer, about 40% of Americans will be diagnosed with the disease in their lifetime, and around 20% will die of it. Current approaches to test carcinogenic chemicals adopt the 2-year rodent bioassay, which is costly and time-consuming. As a result, fewer than 2% of the chemicals on the market have actually been tested. However, evidence accumulated to date suggests that gene expression profiles from model organisms exposed to chemical compounds reflect underlying mechanisms of action, and that these toxicogenomic models could be used in the prediction of chemical carcinogenicity.
resultsIn this study, we used a rat-based microarray dataset from the NTP DrugMatrix Database to test the ability of toxicogenomics to model carcinogenicity. We analyzed 1,221 gene-expression profiles obtained from rats treated with 127 well-characterized compounds, including genotoxic and non-genotoxic carcinogens. We built a classifier that predicts a chemical's carcinogenic potential with an AUC of 0.78, and validated it on an independent dataset from the Japanese Toxicogenomics Project consisting of 2,065 profiles from 72 compounds. Finally, we identified differentially expressed genes associated with chemical carcinogenesis, and developed novel data-driven approaches for the molecular characterization of the response to chemical stressors.
conclusionHere, we validate a toxicogenomic approach to predict carcinogenicity and provide strong evidence that, with a larger set of compounds, we should be able to improve the sensitivity and specificity of the predictions. We found that the prediction of carcinogenicity is tissue-dependent and that the results also confirm and expand upon previous studies implicating DNA damage, the peroxisome proliferator-activated receptor, the aryl hydrocarbon receptor, and regenerative pathology in the response to carcinogen exposure.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.