全暴露组关联研究（EWAS）新方法及其在肝癌风险因子发现中的应用

Environmental exposure is closely related to human health. Exposome-wide association study (EWAS) is a promising tool to elucidate the relationship between environmental exposure and health by measuring the significant changes of exogenous and endogenous compounds in biological matrix. However, the challenge in EWAS is to realize full coverage and maximized structural annotation of exogenous and endogenous compounds in a large concentration range. Therefore, in this project, novel analytical methods with high coverage, high sensitivity are to be developed based on multi-dimensional liquid chromatography system coupled with high resolution mass spectrometry, which aims to improve peak capacity and maximize the information acquisition of exogenous and endogenous compounds. Furthermore, computational exposomics is applied to extract signals with low abundance, and a new compound identification strategy based on molecular networking, quatitative structure-(chromatographic) retention relationships (QSRR), modulated modularity clustering analysis (MMC) and home-made database is established to improve efficiency and reliability of compounds identification. Finally, the developed method is applied to screen and validate the risk factors and prospective early-warning marker(s) of hepatocellular carcinoma (HCC) in the nested cohorts, which provides not only new insight to the etiological study of HCC, but also evidence for the early diagnosis and prevention of HCC.

环境暴露与人类健康密切相关。全暴露组关联研究（exposome-wide association study-EWAS）可通过测定生物基质中外源及内源性化合物的变化阐明环境暴露与健康效应的关系。如何实现不同丰度的外源及内源性化合物的同时分析和信息提取是当前全暴露组关联研究所面临的巨大挑战。本项目围绕环境暴露与健康危害的重大科学问题，利用课题组多年来在代谢组学研究方面的学术积累，构建基于多维色谱-高分辨质谱的高覆盖度、高灵敏度的暴露组学分析平台，以提高色谱峰容量，实现外源及内源性化合物的同时分析和信息最大化，结合“计算暴露组学”数据挖掘手段和未知物鉴定策略，实现不同丰度内暴露组和代谢组的信息提取及规模化定性，尤其是低丰度信号的提取和注释。在此基础上将所建方法用于肝癌巢式队列人群血清的暴露组学研究，筛选肝癌风险因子及预警标志物，为肝癌的早期诊断及预防提供依据。

环境暴露与人类健康密切相关。全暴露组关联研究可通过测定生物样本中外源及内源性化合物的变化阐明环境暴露与健康效应的关系。如何实现不同丰度的外源及内源性化合物的同时分析和信息提取是当前全暴露组关联研究所面临的巨大挑战。围绕环境暴露与健康危害的重大科学问题，本项目针对暴露组高覆盖分析和高分辨质谱数据解析能力不足的重大技术难题，开展了系统的方法学和示范应用研究。首先构建了基于二维液相色谱高分辨质谱系统的高覆盖分析平台，可同时高覆盖检测内、外源小分子；在此基础上开发的高覆盖、高灵敏的拟靶向分析新方法，可实现小分子（包括低丰度化合物）的稳定检测，助力大规模人群样本分析。其次，构建了基于质谱特征融合的计算暴露组学方法，可在原始数据中有效挖掘真实化学信号，尤其适用于暴露组分析时的低丰度质谱特征挖掘。在此基础上进一步构建了非靶向数据智能结构解析系统，为自动化数据预处理和规模化结构注释提供了有力工具。最后，将上述方法用于巢氏队列的肝癌病因探究，揭示了沙美特罗、己烯雌酚和磷酸二丁酯具有潜在的肝癌促进作用，并从代谢水平上揭示相关机制和预警标志物，验证了我们所开发方法和策略的应用潜力。相关工作结果在国际、国内会议上交流了8次，发表SCI收录论文28篇，包括Nat Commun, Sci Total Environ, Adv Sci, Anal Chem, Anal Chim Acta, TrAC等，申请发明专利4项，申请软件著作权1项。培养4名博士研究生。