Toxicogenomics discrimination of potential hepatocarcinogenicity of non-genotoxic compounds in rat liver
Article first published online: 13 JUL 2012
Copyright © 2012 John Wiley & Sons, Ltd.
Journal of Applied Toxicology
Volume 33, Issue 11, pages 1284–1293, November 2013
How to Cite
Yamada, F., Sumida, K., Uehara, T., Morikawa, Y., Yamada, H., Urushidani, T. and Ohno, Y. (2013), Toxicogenomics discrimination of potential hepatocarcinogenicity of non-genotoxic compounds in rat liver. J. Appl. Toxicol., 33: 1284–1293. doi: 10.1002/jat.2790
- Issue published online: 20 SEP 2013
- Article first published online: 13 JUL 2012
- Manuscript Accepted: 28 MAY 2012
- Manuscript Received: 27 APR 2012
- gene expression profiles;
Long-term carcinogenicity testing of a compound is exceedingly time-consuming and costly, and requires many test animals, whereas the Ames test, which is based on the assumption that any substance that is mutagenic may also exert carcinogenic potential, is useful as a short-term screening assay but has major drawbacks. Although, in fact, 90% of compounds that give a positive Ames test cause cancer in laboratory animals, a good proportion of compounds that give a negative Ames test are also carcinogens; that is, there is no good correlation between carcinogenicity and negative Ames test results. As an alternative to these two approaches, we have tried applying toxicogenomics to predict the carcinogenicity of a compound from the gene expression profile induced in vivo. To establish our model, male Sprague–Dawley rats were orally administered test compounds (12 hepatocarcinogens and 26 non-hepatocarcinogens) for 28 days. Analysis of liver gene expression data by Support Vector Machines (SVM) dividing compounds into ‘for training’ and ‘for test’ (20 cases assigned randomly) allowed a set of marker genes to be tested for prediction of hepatocarcinogenicity. The developed prediction model was then validated with reference to the concordance rate with training data and test data, and a good performance was obtained. We will have new gene expression data and continue the validation of our model. Copyright © 2012 John Wiley & Sons, Ltd.