Dataset Information

Trancriptional profiling of rat liver after short-term (up tp 14 days) administration of carcinogenic and non-carcinogenic chemicals

ABSTRACT: The carcinogenic potential of chemicals is currently evaluated with rodent life-time bioassays, which are time consuming, and expensive with respect to cost, number of animals and amount of compound required. Since the results of these 2-year bioassays are not known until quite late during development of new chemical entities, and since the short-term test battery to test for genotoxicity, a characteristic of genotoxic carcinogens, is hampered by low specificity, the identification of early biomarkers for carcinogenicity would be a big step forward. Using gene expression profiles from the livers of rats treated up to 14 days with genotoxic and non-genotoxic carcinogens we previously identified characteristic gene expression profiles for these two groups of carcinogens. We have now added expression profiles from further hepatocarcinogens and from non-carcinogens the latter serving as control profiles. We used these profiles to extract biomarkers discriminating genotoxic from non-genotoxic carcinogens and to calculate classifiers based on the support vector machine (SVM) algorithm. These classifiers then predicted a set of independent validation compound profiles with up to 88% accuracy, depending on the marker gene set. We would like to present this study as proof of the concept that a classification of carcinogens based on short-term studies may be feasible.

ORGANISM(S): Rattus norvegicus

PROVIDER: GSE68110 | GEO | 2016/04/27

SECONDARY ACCESSION(S): PRJNA281878

REPOSITORIES: GEO

ACCESS DATA

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Project description:Liver tumors in rodents are frequently induced by non-genotoxic carcinogens. These hepatocarcinogens generally activate hepatic nuclear receptors (e.g., CAR and PXR), resulting in a cascade of signals causing modifications in the expression of genes responsible for several processes involved in carcinogenesis. Evaluation of the carcinogenic potential of chemicals is a regulatory requirement but is time-consuming and expensive. Consequently, several short-term in vivo and in vitro approaches, using molecular tools, have been proposed as predictive models for non-genotoxic hepatocarcinogens. The objective of our study was to discriminate between chemicals that are either non-genotoxic hepatocarcinogens or merely hepatotoxicants and also between CAR and PXR modulators on the basis of their gene expression profiles. Thus, we treated rats for seven days with the hepatoxicants, diclofenac and diazepam, or with several CAR and PXR modulators, which were mainly hepatocarcinogens. Different hepatic gene expression profiles were obtained not only between the hepatotoxicants and the non-genotoxic hepatocarcinogens but also between the CAR activators phenobarbital, phenytoin and 1,1-bis-(4-chlorophenyl)-2,2-dichloroethene which were grouped together, and the two PXR activators pregnenolone 16Î±-carbonitrile and clotrimazole. Diethylstilbestrol had an expression profile that was quite distinct from the other PXR activators, suggesting that this compound is certainly not a classic PXR modulator. Moreover, some differences were observed between phenytoin (not considered as a hepatocarcinogen), and the other two CAR activators. Our data therefore indicate that discrimination is possible between hepatocarcinogens and hepatotoxicants, between CAR and PXR modulators and also between compounds within the same class of modulators using a short-term transcriptomic approach. CAR or PXR inducers were administered in suspension to rats (7 weeks old at start of treatment) by oral gavage at a daily dose for 7 consecutive days. Treatment-related changes in gene expression were determined in the liver using whole genome oligonucleotide microarrays.

Dataset Information

Trancriptional profiling of rat liver after short-term (up tp 14 days) administration of carcinogenic and non-carcinogenic chemicals

Dataset's files

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets