“大数据”预测分析与三丁基锡暴露有关疾病

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Abstract

The potential influence information of tributyltin (TBT) was excavated to predict the relationship between TBT exposure and human-related diseases by using the "big data" of toxicology database. In comparative toxicogenomics database (CTD), 488 genes interacting with TBT were collected. Among the 488 genes, TP53 was the most associated genes, followed by ESR1 and FN1. Using CTD analysis, it was also found that cancer, nervous system diseases, cardiovascular diseases, urogenital disease (female), digestive system diseases, metabolic disease, urogenital disease (male), endocrine system diseases, immune system diseases and respiratory tract disease were the top 10diseases related to TBT exposure. Based KEGG pathway and DAVID gene function annotation analysis, metabolic diseases were more susceptible to be induced by TBT exposure; moreover, intensive genes interacting with TBT were annotated in the pathways of glucose metabolism. PASS prediction found that biological activities of many sugar-related enzymes could be affected by TBT exposure, which suggested that the impact of TBT on glucose metabolism should be of concern.

Key words： tributyltin comparative toxicogenomics database (CTD) toxicology informatics disease

Received: 10 December 2017

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	Articles by authors
	LIU Min
	ZHANG Ji-liang
	ZHANG Chun-nuan
	QI Qian
	CUI Wei
	FENG Hai-yang

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LIU Min,ZHANG Ji-liang,ZHANG Chun-nuan等. Relationship analysis between tributyltin exposure and human diseases based on “big data”[J]. CHINA ENVIRONMENTAL SCIENCECE, 2018, 38(7): 2746-2750.

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http://www.zghjkx.com.cn/EN/ OR http://www.zghjkx.com.cn/EN/Y2018/V38/I7/2746

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