预测遗传毒性与非遗传毒性致癌物分类器的建立及验证  

Establishment and Validation of Classifiers to Predict Genotoxic and Non-Genotoxic Carcinogens

作　　者：吕建军[1] 李耀庭[1,2] 周舒雅[3] 范昌发[3] 曾雪贞 李保文[3] 汪巨峰[1] 黄芝瑛[2] 李波[4] 

机构地区：[1]中国食品药品检定研究院国家药物安全评价监测中心药物非临床安全评价研究北京市重点实验室,北京100176 [2]中山大学药学院,广州510006 [3]中国食品药品检定研究院实验动物资源研究所,北京100079 [4]中国食品药品检定研究院,北京100050

出　　处：《中国药学杂志》2016年第20期1753-1764,共12页Chinese Pharmaceutical Journal

基　　金：科技部"十二五"国家重大新药创制专项资助项目(2012ZX09302001)

摘　　要：目的以毒理基因组学方法建立预测遗传毒性致癌物与非遗传毒性致癌物的分类器,探索暴露时间对其预测能力的影响并验证其性能。方法原代小鼠肝细胞模型经2个遗传毒性致癌物黄曲霉素B1和苯并芘,2个非遗传毒性致癌物硫代乙酰胺和匹立尼酸处理24和48 h后,对差异表达基因运用基因芯片预测分析筛选出分类器。通过基因集富集分析研究分类器中基因的功能,并运用STRING数据库预测分类器中基因编码蛋白之间的相互关系。进一步运用2个额外的致癌物验证分类器的预测性能。最后还通过Quanti Gene Multiplex实验验证了基因芯片数据。结果经基因芯片预测分析筛选的48 h分类器优于24 h分类器,分类器中的基因涉及p53通路、肿瘤坏死因子-α信号通路、脂肪酸代谢相关基因集、过氧化物酶体增殖物激活受体通路等。分类器中的基因形成致癌蛋白-蛋白相互作用关系网络图和代谢相关蛋白-蛋白相互作用网络图。经验证48h分类器对2个额外的致癌物预测可能率接近100%,Quanti Gene Multiplex实验结果与芯片数据有较高的一致性。结论成功建立了预测分类器并验证其性能。该分类器可用于分辨潜在的遗传毒性致癌物和非遗传毒性致癌物,并对未知化合物可能的作用机制进行预测,有望成为药物非临床安全性评价致癌性试验体外替代方法之一。OBJECTIVE To establish classifiers to predict genotoxic and non-genotoxic carcinogens using toxicogenomics methods, explore the effect of exposure time and validated the prediction performance of the classifiers. METHODS The primary mouse hepatocyte model was treated for 24 and 48 h with two genotoxic carcinogens, aflatoxin B1（AFB1）, benzo（a）pyrene （BAP） and two non-genotoxic carcinogens, thioacetamide （TAA）, wyeth-14643 （WY）. The differentially expressed genes were input to prediction analysis for microarray （PAM） software to screen out classifiers. The functions and interrelations of genes in classifiers were studied by gene set enrichment analysis （GSEA） and the protein-protein interactions were predicted using STRING database. Two additional carcinogens to validate the prediction performance of the classifiers were used. Finally, the experiment of QuantiGene Multiplex assay （Q-GP） to validate the microarray data was used. RESULTS Forty-eight h classifiers had a better predicted capability than that of 24 h classifiers. p53 pathway, TNF-α signaling pathway, fatty acid metabolism, PPAR signaling pathway involved in the classifires were enriched by GSEA. Carcinogenic protein-protein interaction network and metabolism-related protein-protein interaction network are obtained using STRING database. The predicted probability of the two additional carcinogens using 48 h classifiers was nearly 100% and data between QuantiGene Multiplex assay and microarray assay had a high conformity. CONCLUSION The classifiers which could be used to discriminate the potential genotoxic carcinogens and non-genotoxic carcinogens and to predict modes of action for unknown compounds, are successfully established and validated. This might be a promising candidate in vitro method for carcinogenicity study in the field of nonclinical safety evaluation of drugs.

关 键 词：毒理基因组学 分类器 暴露时间 基因芯片预测分析 致癌性试验 药物非临床安全性评价 

分 类 号：R965[医药卫生—药理学]