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Toxicogenomics discrimination of potential hepatocarcinogenicity of non‐genotoxic compounds in rat liver
Author(s) -
Yamada Fumihiro,
Sumida Kayo,
Uehara Takeki,
Morikawa Yuji,
Yamada Hiroshi,
Urushidani Tetsuro,
Ohno Yasuo
Publication year - 2013
Publication title -
journal of applied toxicology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.784
H-Index - 87
eISSN - 1099-1263
pISSN - 0260-437X
DOI - 10.1002/jat.2790
Subject(s) - toxicogenomics , ames test , carcinogen , computational biology , in vivo , toxicology , genotoxicity , concordance , gene expression , pharmacology , bioinformatics , biology , gene , medicine , toxicity , genetics , salmonella , bacteria
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.