环境污染物被认为是肿瘤发生的原因,但是评估它们引起肿瘤的风险的方法仍然不足。作为一种替代方法,不良结局途径(AOP)框架用于评估环境污染物引起肿瘤的风险.砷是与肺癌有关的污染物,但是缺乏对肺癌风险的早期评估。因此,我们将AOP框架应用于砷诱导的肺癌。一项系统评价显示,暴露于饮用水中一系列砷浓度后,患肺癌的风险增加(OR=1.83,95%CI=1.46-2.30)。我们获得了,来自公共数据库,与砷诱导肺癌风险相关的基因。然后,Cox和LASSO回归用于从风险基因中筛选靶基因。随后,靶基因,表型,和路径被用来构建计算AOP网络,由Cytoscape确定有156条边和45个节点。Further,靶基因,表型,和通路被用作分子启动事件和关键事件以根据上游和下游关系构建AOP框架。在AOP框架中,根据证据的重量,砷暴露会增加EGFR的水平,激活PI3K/AKT通路,通过促进G1/S期转换调节细胞增殖,并导致肺癌的产生。外部验证是通过亚砷酸盐诱导的,恶性转化的人支气管上皮(HBE)细胞。总的来说,这些结果,通过集成到现有数据来构建AOP框架,提供对砷暴露的肺癌风险评估的见解。需要特别关注低剂量砷暴露人群。
Environmental pollutants are considered as a cause of tumorigenesis, but approaches to assess their risk of causing tumors remain insufficient. As an alternative approach, the adverse outcome pathway (AOP) framework is used to assess the risk of tumors caused by environmental pollutants. Arsenic is a pollutant associated with lung cancer, but early assessment of lung cancer risk is lacking. Therefore, we applied the AOP framework to arsenic-induced lung cancer. A systematic review revealed increased risks of lung cancer following exposure to a range of arsenic concentrations in drinking water (OR = 1.83, 95 % CI = 1.46-2.30). We obtained, from public databases, genes related to risk of arsenic-induced lung cancer. Then, Cox and LASSO regressions were used to screen target genes from the risk genes. Subsequently, target genes, phenotypes, and pathways were used to construct the computational AOP network, which was determined by Cytoscape to have 156 edges and 45 nodes. Further, target genes, phenotypes, and pathways were used as molecular initiating events and key events to construct the AOP framework depending on upstream and downstream relationships. In the AOP framework, by Weight of Evidence, arsenic exposure increased levels of EGFR, activated the PI3K/AKT pathway, regulated cell proliferation by promoting the G1/S phase transition, and caused generation of lung cancers. External validation was achieved through arsenite-induced, malignant transformed human bronchial epithelial (HBE) cells. Overall, these results, by integration into existing data to construct an AOP framework, provide insights into the assessment of lung cancer risk for arsenic exposure. Special attention needs to be focused on populations with low-dose arsenic exposure.