A transcriptomic biomarker predictive of cell proliferation for use in adverse outcome pathway-informed testing and assessment.

Cell proliferation DNA damage Estrogen receptor α Gene expression biomarkers Gene expression compendium Hepatocarcinogens

Journal

Toxicological sciences : an official journal of the Society of Toxicology
ISSN: 1096-0929
Titre abrégé: Toxicol Sci
Pays: United States
ID NLM: 9805461

Informations de publication

Date de publication:
13 Aug 2024
Historique:
medline: 13 8 2024
pubmed: 13 8 2024
entrez: 13 8 2024
Statut: aheadofprint

Résumé

High-throughput transcriptomics (HTTr) is increasingly being used to identify molecular targets of chemicals that can be linked to adverse outcomes. Cell proliferation is an important key event in chemical carcinogenesis. Here, we describe the construction and characterization of a gene expression biomarker that is predictive of the cell proliferation (CP) status in human and rodent tissues. The biomarker was constructed from 30 genes known to be increased in expression in prostate cancers relative to surrounding tissues and in cycling human MCF-7 cells after estrogen receptor (ER) agonist exposure. Using a large compendium of gene expression profiles to test utility, the biomarker could identify increases in cell proliferation in 1) 367 tumor vs. normal surrounding tissue comparisons from 6 human organs, 2) MCF-7 cells after activation of ER, 3) after partial hepatectomy in mice and rats, and 4) the livers of mice after exposure to nongenotoxic hepatocarcinogens. The biomarker identified suppression of CP 1) under conditions of p53 activation by DNA damaging agents in human cells, 2) in human A549 lung cells exposed to therapeutic anticancer kinase inhibitors (dasatinib, nilotnib) and 3) in the mouse liver when comparing high levels of CP at birth to the low background levels in the adult. The responses using the biomarker were similar to those observed using conventional markers of CP including PCNA, Ki67, and BrdU labeling. The CP biomarker will be a useful tool for interpretation of HTTr data streams to identify cell proliferation status after exposure to chemicals in human cells or in rodent tissues.

Identifiants

pubmed: 39137154
pii: 7732924
doi: 10.1093/toxsci/kfae102
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

Published by Oxford University Press 2024.

Auteurs

J Christopher Corton (JC)

Center for Computational Toxicology and Exposure, US Environmental Protection Agency, Research Triangle Park, NC 27711.

Victoria Ledbetter (V)

Center for Computational Toxicology and Exposure, US Environmental Protection Agency, Research Triangle Park, NC 27711.

Samuel M Cohen (SM)

Department of Pathology and Microbiology and Buffett Cancer Center, University of Nebraska Medical Center, Omaha, NE, 69198-3135.

Ella Atlas (E)

Environmental Health Science and Research Bureau, Healthy Environments and Consumer Safety Branch (HECSB) Health Canada, Ottawa, Ontario, K2K 0K9, Canada.

Carole L Yauk (CL)

Department of Biology, University of Ottawa, Ottawa, Ontario, Canada.

Jie Liu (J)

Center for Computational Toxicology and Exposure, US Environmental Protection Agency, Research Triangle Park, NC 27711.

Classifications MeSH