A lung epithelial cell-based test system for discrimating genotoxic and non-genotoxic carcinogens
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ABSTRACT:
ORGANISM(S): Homo sapiens
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PROVIDER: S-DIXA-004 | biostudies-other |
REPOSITORIES: biostudies-other
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Project description:Based on the process of carcinogenesis, carcinogens are classified as either genotoxic or non-genotoxic. In contrast to non-genotoxic carcinogens, many genotoxic carcinogens have been reported to cause tumor in carcinogenic bioassays in animals. Thus evaluating the genotoxicity potential of chemicals is important to discriminate genotoxic from non-genotoxic carcinogens for health care and pharmaceutical industry safety. Additionally, investigating the difference between the mechanisms of genotoxic and non-genotoxic carcinogens could provide the foundation for a mechanism-based classification for unknown compounds. In this study, we investigated the gene expression of HepG2 cells treated with genotoxic or non-genotoxic carcinogens and compared their mechanisms of action. To enhance our understanding of the differences in the mechanisms of genotoxic and non-genotoxic carcinogens, we implemented a gene set analysis using 12 compounds for the training set (12, 24, 48 h) and validated significant gene sets using 22 compounds for the test set (24, 48 h). For a direct biological translation, we conducted a gene set analysis using Globaltest and selected significant gene sets. To validate the results, training and test compounds were predicted by the significant gene sets using a prediction analysis for microarrays (PAM). Finally, we obtained 6 gene sets, including sets enriched for genes involved in the adherens junction, bladder cancer, p53 signaling pathway, pathways in cancer, peroxisome and RNA degradation. Among the 6 gene sets, the bladder cancer and p53 signaling pathway sets were significant at 12, 24 and 48 h. We also found that the DDB2, RRM2B and GADD45A, genes related to the repair and damage prevention of DNA, were consistently up-regulated for genotoxic carcinogens. Our results suggest that a gene set analysis could provide a robust tool in the investigation of the different mechanisms of genotoxic and non-genotoxic carcinogens and construct a more detailed understanding of the perturbation of significant pathways.
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Project description:Current in vitro genotoxicity tests can produce misleading positive results, indicating an inability to effectively predict a compound's subsequent carcinogenic potential in vivo. Such oversensitivity can incur unnecessary in vivo tests to further investigate positive in vitro results, supporting the need to improve in vitro tests to better inform risk assessment. It is increasingly acknowledged that more informative in vitro tests using multiple endpoints may support the correct identification of carcinogenic potential. The present study, therefore, employed a holistic, multiple-endpoint approach using low doses of selected carcinogens and non-carcinogens (0.001-770 µM) to assess whether these chemicals caused perturbations in molecular and cellular endpoints relating to the Hallmarks of Cancer. Endpoints included micronucleus induction, alterations in gene expression, cell cycle dynamics, cell morphology and bioenergetics in the human lymphoblastoid cell line TK6. Carcinogens ochratoxin A and oestradiol produced greater Integrated Signature of Carcinogenicity scores for the combined endpoints than the "misleading" in vitro positive compounds, quercetin, 2,4-dichlorophenol and quinacrine dihydrochloride and toxic non-carcinogens, caffeine, cycloheximide and phenformin HCl. This study provides compelling evidence that carcinogens can successfully be distinguished from non-carcinogens using a holistic in vitro test system. Avoidance of misleading in vitro outcomes could lead to the reduction and replacement of animals in carcinogenicity testing.
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Project description:The need for an efficient carcinogenicity test prompted this study, in which we used a microarray-based genomics approach, with a short-term in vivo model, in combination with insights from statistical and mechanistic analyses. We performed additional experiments to support the significance of the microarray results. Carcinogens were evaluated based on differences in the mechanisms involved in the response to genotoxic (GTX) carcinogens and non-genotoxic (NGTX) carcinogens. Microarray data were analyzed for 2 time points after treatment with the following 6 carcinogens. The analysis was performed using t-tests to compare the fold changes, and we selected differentially expressed genes (DEGs) and evaluated the reasons for differential expression in terms of cellular pathways and processes. We mapped the DEG-related pathways to analyze cellular processes, and we were able to uncover significant mechanisms that involve critical cellular components, such as CDKN1A (p21) and BAX. In addition, a comparison of the data from two time points showed that the repeated administration model was more effective than a single administration for carcinogen research. The classification analysis of selected DEGs was performed by setting microarray data of 4 carcinogens as test sets; these test sets were evaluated as classifiers. Microarray results were further supported using the Comet and micronucleus assays. It was found that gene expression profiling using microarrays, followed by pathway analysis, was effective in increasing the understanding of the characteristics of different carcinogens, and the efficiency of these methods was exemplified by the short-term (3 day) nature of the animal experiments. The following 10 hepatocarcinogens were used: 2-acetylaminofluorene (53-96-3), diethylnitrosamine (55-18-5), 4-dimethylamino-3'-methyl azobenzene (55-80-1), clofibrate (637-07-0), 1,4-dioxane (123-91-1), DL-ethionine (67-21-0), 1,3-dichloro-2'-propanol (96-23-1), urethane (51-79-6), sodium nitrite (7632-00-0), and methyleugenol (93-15-2). Forty-one data were obtained for the training compounds in this study. We treated rats with one of three compounds or a control for each GTX and NGTX experimental group and measured 2 time points for each group. All experimental procedures were repeated three times. We excluded 1 file that was found to be a failure. Each ‘.CEL’ file obtained as a raw data file was standardized using the RMA (Robust Multi-array Average) algorithm.
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