ArticleToxicological sciences : an official journal of the Society of Toxicology2016
Moving Toward Integrating Gene Expression Profiling Into High-Throughput Testing: A Gene Expression Biomarker Accurately Predicts Estrogen Receptor α Modulation in a Microarray Compendium.
Article in Toxicological sciences : an official journal of the Society of Toxicology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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Who cites it
31 citing papers in PubMed, 48 citations in OpenAlex.
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- High-throughput transcriptomics analysis of equipotent and human relevant mixtures of BPA alternatives reveal additive effects in vitro.Archives of toxicology · 2025Article
- Signature analysis of high-throughput transcriptomics screening data for mechanistic inference and chemical grouping.Toxicological sciences : an official journal of the Society of Toxicology · 2024Article
- High-Throughput Transcriptomics Screen of ToxCast Chemicals in U-2 OS Cells.Toxicology and applied pharmacology · 2024Article
- A new approach methodology to identify tumorigenic chemicals using short-term exposures and transcript profiling.Frontiers in toxicology · 2024Article
- Exploring the effects of experimental parameters and data modeling approaches on in vitro transcriptomic point-of-departure estimates.Toxicology · 2024Article
- In vitro transcriptomic analyses reveal pathway perturbations, estrogenic activities, and potencies of data-poor BPA alternative chemicals.Toxicological sciences : an official journal of the Society of Toxicology · 2023Article
- 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
- A gene expression biomarker for predictive toxicology to identify chemical modulators of NF-κB.PloS one · 2022Article
- Epigenetic remodelling of enhancers in response to estrogen deprivation and re-stimulation.Nucleic acids research · 2021Article
- Commentary: Novel strategies and new tools to curtail the health effects of pesticides.Environmental health : a global access science source · 2021Article
- High-Throughput Transcriptomics Platform for Screening Environmental Chemicals.Toxicological sciences : an official journal of the Society 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 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
- 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
- Early microRNA indicators of PPARα pathway activation in the liver.Toxicology reports · 2020Article
- Article
- The use of evidence from high-throughput screening and transcriptomic data in human health risk assessments.Toxicology and applied pharmacology · 2019Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
Abstract
Microarray profiling of chemical-induced effects is being increasingly used in medium- and high-throughput formats. Computational methods are described here to identify molecular targets from whole-genome microarray data using as an example the estrogen receptor α (ERα), often modulated by potential endocrine disrupting chemicals. ERα biomarker genes were identified by their consistent expression after exposure to 7 structurally diverse ERα agonists and 3 ERα antagonists in ERα-positive MCF-7 cells. Most of the biomarker genes were shown to be directly regulated by ERα as determined by ESR1 gene knockdown using siRNA as well as through chromatin immunoprecipitation coupled with DNA sequencing analysis of ERα-DNA interactions. The biomarker was evaluated as a predictive tool using the fold-change rank-based Running Fisher algorithm by comparison to annotated gene expression datasets from experiments using MCF-7 cells, including those evaluating the transcriptional effects of hormones and chemicals. Using 141 comparisons from chemical- and hormone-treated cells, the biomarker gave a balanced accuracy for prediction of ERα activation or suppression of 94% and 93%, respectively. The biomarker was able to correctly classify 18 out of 21 (86%) ER reference chemicals including "very weak" agonists. Importantly, the biomarker predictions accurately replicated predictions based on 18 in vitro high-throughput screening assays that queried different steps in ERα signaling. For 114 chemicals, the balanced accuracies were 95% and 98% for activation or suppression, respectively. These results demonstrate that the ERα gene expression biomarker can accurately identify ERα modulators in large collections of microarray data derived from MCF-7 cells.
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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.