ArticleEnvironmental and molecular mutagenesis2018
Using a gene expression biomarker to identify DNA damage-inducing agents in microarray profiles.
Article in Environmental and molecular mutagenesis, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- Review of Transcriptomic Biomarkers That Predict In Vitro Genotoxicity in Human Cell Lines.Environmental and molecular mutagenesis · 2025Review
- Impacts of Inorganic Arsenic Exposure on Genetic Stability of Human Mesenchymal Stromal Cells.Journal of applied toxicology : JAT · 2025Article
- Searching for LINCS to Stress: Using Text Mining to Automate Reference Chemical Curation.Chemical research in toxicology · 2024Article
- Visualization strategies to aid interpretation of high-dimensional genotoxicity data.Environmental and molecular mutagenesis · 2024Article
- A new approach methodology to identify tumorigenic chemicals using short-term exposures and transcript profiling.Frontiers in toxicology · 2024Article
- Application of HepaRG cells for genotoxicity assessment: a review.Journal of environmental science and health. Part C, Toxicology and carcinogenesis · 2024Review
- Determinants of gene expression in the human liver: Impact of aging and sex on xenobiotic metabolism.Experimental gerontology · 2022Article
- A Collaborative Initiative to Establish Genomic Biomarkers for Assessing Tumorigenic Potential to Reduce Reliance on Conventional Rodent Carcinogenicity Studies.Toxicological sciences : an official journal of the Society of Toxicology · 2022Article
- Integrated Genotoxicity Testing of three anti-infective drugs using the TGx-DDI transcriptomic biomarker and high-throughput CometChipFrontiers in toxicology · 2022Article
- A gene expression biomarker for predictive toxicology to identify chemical modulators of NF-κB.PloS one · 2022Article
- Evaluating adaptive stress response gene signatures using transcriptomics.Computational toxicology (Amsterdam, Netherlands) · 2021Article
- Development and validation of the TGx-HDACi transcriptomic biomarker to detect histone deacetylase inhibitors in human TK6 cells.Archives of toxicology · 2021Article
- A Gene Expression Biomarker Identifies Chemical Modulators of Estrogen Receptor α in an MCF-7 Microarray Compendium.Chemical research in toxicology · 2021Article
- A Modern Genotoxicity Testing Paradigm: Integration of the High-Throughput CometChip® and the TGx-DDI Transcriptomic Biomarker in Human HepaRG™ Cell Cultures.Frontiers in public health · 2021Article
- A set of six Gene expression biomarkers and their thresholds identify rat liver tumorigens in short-term assays.Toxicology · 2020Article
- Identification of novel activators of the metal responsive transcription factor (MTF-1) using a gene expression biomarker in a microarray compendium.Metallomics : integrated biometal science · 2020Article
- Genetic toxicity assessment using liver cell models: past, present, and future.Journal of toxicology and environmental health. Part B, Critical reviews · 2020Review
- Article
- Flow cytometric micronucleus assay and TGx-DDI transcriptomic biomarker analysis of ten genotoxic and non-genotoxic chemicals in human HepaRG™ cells.Genes and environment : the official journal of the Japanese Environmental Mutagen Society · 2020Article
- Identification of p53 Activators in a Human Microarray Compendium.Chemical research in toxicology · 2019Article
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3 authors.
Funding
Abstract
High-throughput transcriptomic technologies are increasingly being used to screen environmental chemicals in vitro to provide mechanistic context for regulatory testing. The TGx-DDI biomarker is a 64-gene expression profile generated from testing 28 model chemicals or treatments (13 that cause DNA damage and 15 that do not) in human TK6 cells. While the biomarker is very accurate at predicting DNA damage inducing (DDI) potential using the nearest shrunken centroid method, the broad utility of the biomarker using other computational methods is not fully known. Here, we determined the accuracy of the biomarker used with the Running Fisher test, a nonparametric correlation test. In TK6 cells, the methods could readily differentiate DDI and non-DDI compounds with balanced accuracies of 87-97%, depending on the threshold for determining DDI positives. The methods identified DDI agents in the metabolically competent hepatocyte cell line HepaRG (accuracy = 90%) but not in HepG2 cells or hepatocytes derived from embryonic stem cells (60 and 80%, respectively). DDI was also accurately classified when the gene expression changes were derived using the nCounter technology (accuracy = 89%). In addition, we found: (1) not all genes contributed equally to the correlations; (2) the minimal overlap in genes between the biomarker and the individual comparisons required for significant positive correlation was 10 genes, but usually was much higher; and (3) different sets of genes in the biomarker can by themselves contribute to the significant correlations. Overall, these results demonstrate the utility of the biomarker to accurately classify DDI agents. Environ. Mol. Mutagen. 59:772-784, 2018. Published 2018. This article is a U.S. Government work and is in the public domain in the USA.
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