Phthalates- Thyroid cancer

First, the potential toxicity of phthalates was evaluated using online toxicity prediction platforms. Toxicological targets were retrieved from SwissTargetPrediction, ChEMBL, and STITCH databases and subsequently consolidated. Potential targets were then screened using a network toxicology approach, while thyroid cancer-related genes were comprehensively collected from the GeneCards database. Using R language and associated packages, the intersection of these two gene sets was identified as candidate key genes, and their biological functions were elucidated through KEGG pathway and Gene Ontology (GO) functional enrichment analyses. The candidate genes were then imported into the STRING database to construct a protein-protein interaction (PPI) network, which was further visualized using Cytoscape. Through calculations with CytoHubba and MCODE plugins, the top 20 hub genes and highly interconnected core subnetwork modules were identified. To verify the causal relationship between the intersecting genes and thyroid cancer, Mendelian Randomization (MR) analysis was performed using data from the thyroid cancer-related datasets in FinnGen database. Finally, molecular docking of phthalate esters against thyroid cancer-related key proteins was conducted, followed by molecular dynamics simulations.

首先，本研究通过在线毒性预测平台评估邻苯二甲酸酯（phthalates）的潜在毒性。从SwissTargetPrediction、ChEMBL及STITCH数据库中检索毒理学靶点并进行整合。随后采用网络毒理学方法筛选潜在靶点，同时从GeneCards数据库全面收集甲状腺癌相关基因。借助R语言及其配套分析包，将上述两类基因集的交集鉴定为候选关键基因，并通过京都基因与基因组百科全书（Kyoto Encyclopedia of Genes and Genomes, KEGG）通路分析与基因本体（Gene Ontology, GO）功能富集分析，阐明其生物学功能。随后将候选基因导入STRING数据库以构建蛋白质相互作用（Protein-Protein Interaction, PPI）网络，并通过Cytoscape完成可视化。利用CytoHubba与MCODE插件进行计算，筛选得到排名前20的核心枢纽基因以及高度互联的核心子网模块。为验证交集基因与甲状腺癌的因果关联，本研究采用FinnGen数据库中甲状腺癌相关数据集开展孟德尔随机化（Mendelian Randomization, MR）分析。最后，对邻苯二甲酸酯与甲状腺癌相关关键蛋白进行分子对接，并实施分子动力学模拟。