Potential targets of endocrine-disrupting chemicals related to breast cancer identified by ToxCast and deep learning models

Miaomiao Guan, Guanpeng Qi, Zuojing Li, X. Hou
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Abstract

Abstract Of 47 endocrine-disrupting chemicals (EDCs) collected from literature and related to breast cancer, not all were tested in a toxicity forecaster (ToxCast) program of the US-Environmental Protection Agency (EPA). Therefore, deep learning models based on the toxicity data in that database have been used to predict the molecular toxicity of the untested EDCs. Combined with the values of median lethal doses (LDs), six potential targets of EDCs related to breast cancer have been identified, viz. MYC proto-oncogene, urokinase plasminogen activator receptor (PLAUR), cytochrome P450 4 A 11, nuclear receptor 1 H 2 (NR1H2), peroxisome proliferator-activated receptor alpha (PPARA), and hypoxia-inducible factor 1 alpha (HIF1A).
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通过ToxCast和深度学习模型确定与乳腺癌相关的内分泌干扰化学物质的潜在目标
从文献中收集到的47种与乳腺癌相关的内分泌干扰化学物质(EDCs),并不是所有的都在美国环境保护署(EPA)的毒性预测(ToxCast)项目中进行了测试。因此,基于该数据库中毒性数据的深度学习模型已被用于预测未经测试的EDCs的分子毒性。结合中位致死剂量(ld),确定了6个与乳腺癌相关的EDCs的潜在靶点,即MYC原癌基因、尿激酶纤溶酶原激活物受体(PLAUR)、细胞色素P450 4a11、核受体1h2 (NR1H2)、过氧化物酶体增殖物激活受体α (PPARA)和缺氧诱导因子1 α (HIF1A)。
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