Discriminant for genotoxic and non-genotoxic carcinogens by differentially expressed gene profiling

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.本研究出于对高效致癌性试验（carcinogenicity test）的迫切需求，采用基于微阵列（microarray）的基因组学方法，结合短期体内模型与统计、机制分析的相关研究思路开展实验。我们额外开展了多组验证实验，以佐证微阵列检测结果的显著性与可靠性。本研究依据遗传毒性（genotoxic，GTX）致癌物与非遗传毒性（non-genotoxic，NGTX）致癌物的应答机制差异，对各类致癌物进行分类评估。针对以下6种致癌物处理后两个时间点的样本，我们对微阵列数据展开分析：采用t检验比较基因表达倍数变化，筛选得到差异表达基因（differentially expressed genes, DEGs），并从细胞通路与细胞过程层面解析差异表达的成因。我们对差异表达基因相关的通路进行映射分析以探究细胞进程，最终揭示了涉及CDKN1A（p21）与BAX等关键细胞组分的重要致癌机制。此外，对两个时间点样本数据的对比分析表明，相较于单次给药模型，重复给药模型在致癌物研究中更具应用价值。我们以4种致癌物的微阵列数据作为测试集，对筛选得到的差异表达基因开展分类分析，并将其作为分类器进行性能评估。本研究通过彗星试验（Comet assay）与微核试验（micronucleus assay）进一步验证了微阵列检测结果的可靠性。研究结果证实，采用微阵列技术进行基因表达谱分析并结合通路分析，可有效提升对不同致癌物特性的认知水平；而本研究动物实验为期仅3天的短期周期，也印证了上述方法的高效性。