Discrimination of Carcinogens by Hepatic Transcript Profiling in Rats following 28-day Administration

Abstract

This study aimed at discriminating carcinogens on the basis of hepatic transcript profiling in the rats administrated with a variety of carcinogens and non-carcinogens. We conducted 28-day toxicity tests in male F344 rats with 47 carcinogens and 26 non-carcinogens, and then investigated periodically the hepatic gene expression profiles using custom microarrays. By hierarchical cluster analysis based on significantly altered genes, carcinogens were clustered into three major groups (Group 1 to 3). The formation of these groups was not affected by the gene sets used as well as the administration period, indicating that the grouping of carcinogens was universal independent of the conditions of both statistical analysis and toxicity testing. Seventeen carcinogens belonging to Group 1 were composed of mainly rat hepatocarcinogens, most of them being mutagenic ones. Group 2 was formed by three subgroups, which were composed of 23 carcinogens exhibiting distinct properties in terms of genotoxicity and target tissues, namely nonmutagenic hepatocarcinogens, and mutagenic and nonmutagenic carcinogens both of which are targeted to other tissues. Group 3 contained 6 carcinogens including 4 estrogenic substances, implying the group of estrogenic carcinogens. Gene network analyses revealed that the significantly altered genes in Group 1 included Bax, Tnfrsf6, Btg2, Mgmt and Abcb1b, suggesting that p53-mediated signaling pathway involved in early pathologic alterations associated with preceding mutagenic carcinogenesis. Thus, the common transcriptional signatures for each group might reflect the early molecular events of carcinogenesis and hence would enable us to identify the biomarker genes, and then to develop a new assay for carcinogenesis prediction.

Differences in gene expression profiles in liver caused by different types of anesthesia: cases of CO₂-O₂ and isoflurane

“…Anesthesia is used for pain control and is necessary in toxicological studies, and several anesthesia methods are employed in the fields of toxicogenomics, for example CO 2 -O 2 anesthesia (Matsumoto et al, 2009(Matsumoto et al, , 2014. Currently, isoflurane anesthesia is becoming widely used for this purpose (Nagate et al, 2007;Edmands et al, 2013;Nakazato et al, 2009).…”

CARCINOscreen®: New short-term prediction method for hepatocarcinogenicity of chemicals based on hepatic transcript profiling in rats

“…We performed a cluster analysis of the gene expression profile for rat liver in a 28-day repeated dose toxicity study using 73 chemicals consisting of 47 carcinogens and 26 non-carcinogens and identified three major carcinogen groups, each with a characteristic gene expression profile (Matsumoto et al, 2009). These results indicate that there are three kinds of characteristic gene sets that show gene expression changes specifically for each of three groups of carcinogens; therefore, the prediction of carcinogens based on characteristic gene sets should be performed individually for each group.…”

Simpler alternative to CARCINOscreen^® based on quantitative PCR (qPCR)

“…A total of 51 chemicals, including 29 for trainingphase trials and 22 for validation-phase trials, were selected from among the chemicals used in previous studies (Matsumoto et al, 2009(Matsumoto et al, , 2014. The names of the chemicals and their toxicological profiles are shown in Table 1 and Table 2.…”

“…After applying the log2 ratio data of 15 predictive genes to CARCINOscreen ® , the largest value among the predictive scores obtained using the three prediction formulae was determined as the final predictive score. The details of the microarray experiments for CARCINOscreen ® are described in Matsumoto et al (2009Matsumoto et al ( , 2011Matsumoto et al ( and 2014.…”

“…This system consists of three prediction formulae generated from 15 characteristically predictive genes. The accuracy of this microarray-based CARCINOscreen ® for carcinogenicity prediction is reported as 94.1% for training chemical sets, therefore, this prediction system may be a promising tool for predicting hepatocarcinogenicity of chemicals in rats (Matsumoto et al, 2009(Matsumoto et al, , 2011(Matsumoto et al, and 2014. However, microarray analysis requires specialized equipment and skilled bioinformatics approaches for data analysis, limiting its versatility for the toxicological applications.…”