大气细颗粒物PM2.5暴露显著增高肺癌发病风险。本研究旨在识别PM2.5及内载组分诱发肺癌发生的毒性通路和关键靶点，构建有害结局路径AOP框架，阐述剂量反应关系并预测暴露致癌风险。根据不同地区的污染模式，分别在夏/冬季采集5个典型区域城市北京、上海、广州、重庆、沈阳和1个对照城市珠海的PM2.5，解析表征主要内载组分并进行构效关系分析。应用转基因动物诱癌和细胞转化试验检测PM2.5及组分的致癌活性并探讨致癌机制。利用巨噬细胞和上皮细胞共培养模型，结合高通量组学技术筛查与PM2.5致癌相关的“毒性通路”或“关键事件”，重点探讨表观遗传网络调控机制。选择暴露区域的人群，进行内暴露和标记物检测；并用panel study评价不同生物学效应的窗口期及标志物的稳定性。应用系统生物学方法整合暴露、生物学效应和人群实际暴露的数据，建立AOP框架，为大气污染防控提供有效的生物监测指标和暴露风险评价手段。

Exposure to atmospheric PM2.5 significantly increased the risk of human lung cancer development. This study aims to identify the toxicity pathway and key targets associated with lung cancer development through exposure to the particulate matter and main components. Moreover, we will construct a framework for adverse outcome pathway (AOP), and elaborate the biological dose-response relationship and predict the cancer risk. According to different pollution pattern, five typical regions including Beijing, Shanghai, Guangzhou, Chongqing, and Shenyang, and control city Zhuhai will be recruited. We will collect the atmospheric PM2.5 in these regions in summer and winter season, respectively and followed by analysis of characterization and major toxic components and structure-activity by QSAR modules. Transgenic mice and human bronchial epithelial (HBE) cell transformation assay will be applied to assess whether PM2.5 and main components possessing carcinogenic potential and explore the related mechanisms of chemical carcinogenesis. With a model with co-cultured macrophage and human epithelial cells, together with high-throughput technology, we attempt to identify the “Toxicity Pathway” and “Initiative Molecular Event” underlying PM2.5-associated carcinogenesis. The epigenetic mechanism will be highlighted. Furthermore, internal exposure and effective biomarkers for PM2.5 will be measured in volunteers from recruited cities mentioned above. Panel study will be conducted to evaluate the window of biological effects and verify the stability of the biomarkers. Finally, the approach of systems biology will be applied to integrate the information and data from exposure, biological effects, and human actual exposure, and establish the predictions models for AOP. This effort will lead to identification of the effective biological monitoring markers and strategy of exposure risk assessment for air pollution prevention.