| 引用本文: | 盛孔胜,朱俊峰,孔思思,胡晓平,李清林,黄萍.基于网络药理学的清肺合剂抗肺癌作用机制研究[J].中国现代应用药学,2020,37(8):926-934. |
| SHENG Kongsheng,ZHU Junfeng,KONG Sisi,HU Xiaoping,LI Qinglin,HUANG Ping.Study on the Mechanisms of Qingfei Mixture in Lung Cancer Treatment Based on Network Pharmacology[J].Chin J Mod Appl Pharm(中国现代应用药学),2020,37(8):926-934. |
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| 基于网络药理学的清肺合剂抗肺癌作用机制研究 |
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盛孔胜1,2, 朱俊峰2, 孔思思2, 胡晓平2, 李清林2, 黄萍1,3
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1.浙江中医药大学药学院, 杭州 310053;2.浙江省肿瘤医院药剂科, 杭州 310022;3.浙江省人民医院药剂科, 杭州医学院附属人民医院, 杭州 310010
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| 摘要: |
| 目的 采用网络药理学方法阐明清肺合剂"多成分-多靶点-多途径"的作用理念，为进一步研究清肺合剂抗肺癌的药效物质基础和作用机制提供理论依据。方法 通过检索TCMSP数据库，结合口服生物利用度（OB ≥ 30%）、小肠上皮细胞渗透性（Caco-2 ≥-0.4）和类药性分析（DL ≥ 0.18）参数，筛选清肺合剂的活性成分；通过TCMSP、STITCH、Swiss数据库预测活性成分的靶点；通过TCMSP、TTD、PharmGKB数据库筛选出肺癌疾病相关基因；利用Cytoscape软件构建"活性成分-靶点-疾病"网络图；运用String数据库进行蛋白相互作用分析，建立PPI网络图；运用DAVID数据库进行GO功能注释和KEGG通路富集分析。结果 经筛选后得到清肺合剂的108个活性成分，对应357个靶点。挖掘得到肺癌靶点412个，其中成分-疾病交互靶点38个，参与细胞增殖和凋亡、血管生成、有丝分裂等生物过程，涉及多种肿瘤通路、焦点黏附通路、血管内皮生长因子信号通路、p53信号通路、ErbB信号通路。结论 清肺合剂通过多成分、多靶点、多通路调控细胞的分化、增殖、凋亡，调节血管生成，调控有丝分裂和减数分裂以及调节炎症反应从而发挥抗肺癌作用。 |
| 关键词: 清肺合剂 网络药理学 肺癌 血管生成 细胞凋亡 |
| DOI：10.13748/j.cnki.issn1007-7693.2020.08.006 |
| 分类号:R966 |
| 基金项目:浙江省中医药重点研究项目（2018ZZ006，2020ZZ003）；浙江自然科学基金/省药学会联合基金（LYY19H280001）；浙江省151人才第二层次培养项目（黄萍）；浙江省卫生创新人才培养项目（黄萍） |
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| Study on the Mechanisms of Qingfei Mixture in Lung Cancer Treatment Based on Network Pharmacology |
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SHENG Kongsheng1,2, ZHU Junfeng2, KONG Sisi2, HU Xiaoping2, LI Qinglin2, HUANG Ping1,3
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1.College of Pharmaceutical Science, Zhejiang Chinese Medical University, Hangzhou 310053, China;2.Pharmacy Department, Zhejiang Cancer Hospital, Hangzhou 310022, China;3.Department of Pharmacy, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou 310010, China
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| Abstract: |
| OBJECTIVE To elucidate the multicomponent-multitarget-multipathway nature of Qingfei mixture using network pharmacology strategy and to provide a theoretical basis for further study on the pharmacodynamic basis and action mechanisms of Qingfei mixture against lung cancer. METHODS The active components with oral bioavailability(≥ 30%), Caco-2 cell permeability(≥ -0.4) and drug-like analysis(≥ 0.18) in Qingfei mixture were screened by searching TCMSP database. TCMSP, STITCH and Swiss databases were used to predict the targets of active components; the lung cancer disease-related genes were screened by TCMSP, TTD, and PharmGKB databases; the "active component-target-disease" network was constructed using Cytoscape software; the protein interaction analysis was performed using String database and a PPI network was established; DAVID database was used for GO function annotation and KEGG pathway enrichment analysis. RESULTS After screening, 108 active components of Qingfei mixture were obtained and 357 corresponding targets were collected. The 412 lung cancer targets were obtained, including 38 component-disease interaction targets, which were involved in cell proliferation and apoptosis, angiogenesis, mitosis and other biological processes, and involved in multiple cancer pathways, focal adhesion pathway, vascular endothelial growth factor signaling pathway, p53 signaling pathway, ErbB signaling pathway. CONCLUSION Qingfei mixture can exert anti-lung cancer effects through the regulation of cell differentiation, proliferation, apoptosis, angiogenesis, mitosis and meiosis, and inflammatory response. |
| Key words: Qingfei mixture network pharmacology lung cancer angiogenesis apoptosis |
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