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Medical Diagnosis 2021
基于网络药理学方法预测扶正抑瘤方作用于PD-1和CTLA-4免疫检查点潜在信号转导途径
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Abstract:
目的：运用网络药理学方法预测扶正抑瘤方作用于PD-1和CTLA-4免疫检查点潜在信号转导途径。方法：ETCM、TCMSP数据库筛选扶正抑瘤方中各味药物活性成分，利用STITCH、Swiss Target Prediction等数据库对各活性成分进行作用靶点预测，构建成分–靶点集，与MalaCards、DisGeNET等数据库中PD-1和CTLA-4免疫检查点相关核心靶点取交集。利用STRING数据库进行蛋白互作网络构建，DAVID数据库进行富集分析，利用Cytoscape构建“药物–成分–靶点–通路”网络图，对数据进行可视化。结果：从扶正抑瘤方种筛选得到195个活性化合物，涉及相关作用靶标676个；筛选核心靶标44个，映射交集16个。蛋白互作网络筛选得到5个关键靶标HIF1A、STAT1、STAT3、CD80、CD86，主要通过PD-L1表达于PD-1检查点通路及CTLA4介导的T细胞受体信号转导。结论：扶正抑瘤方可通过多成分多靶点发挥肿瘤免疫杀伤作用，与T细胞受体信号通路CTLA-4和PD-1检查点通路等高度相关。
Objective: Using network pharmacology methods to predict the potential signal transduction pathways of Fuzheng Yiliu Prescription on PD-1 and CTLA-4 immune checkpoints. Methods: ETCM, TCMSP databases screened the active ingredients of various medicines in Fuzheng Yiliu Prescription, and used STITCH, Swiss Target Prediction and other databases to predict the action target of each active ingredient, and constructed the ingredient-target set, which was combined with MalaCards, DisGeNET and other databases Intersection of relevant targets for PD-1 and CTLA-4 immune checkpoints. Use STRING database for protein-protein interaction network construction, DAVID database for enrichment analysis, use Cytoscape to construct a “drug-com- ponent-target-pathway” network diagram to visualize the data. Result: 195 active compounds were screened from Fuzheng Yiliu Prescription, involving 676 related targets; 44 liver cancer-related key targets were screened, and 16 were mapped. The protein interaction network screened 5 key targets HIF1A, STAT1, STAT3, CD80, and CD86, mainly through PD-L1 expression PD-1 checkpoint pathway and CTLA4 mediated T cell receptor signal transduction. Conclusion: Fuzheng Yiliu Prescription can play a role in tumor immune killing through multiple components and multiple targets, which is highly related to T cell receptor signaling pathway CTLA-4 and PD-1 checkpoint pathway.
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https://doi.org/10.1056/NEJMoa1910836 |
