摘要
目的:应用古今医案云平台V2.3.5版本和Cytoscape 3.8.0版本分析中药外治治疗乳腺癌相关淋巴水肿的用药规律,并通过网络药理学和分子对接进一步分析核心药物的可能作用机制。方法:收集中国知网、万方、维普数据库自建库到2021年11月1日发表、证明有确切疗效或中药外治联合用药组优于非联合用药组的文献,将符合要求的中药外治方录入数据库中进行统计分析,筛选出核心药物,并进行后续机制探讨。首先进行靶点预测,通过TCMSP数据库筛选核心药物的有效成分和作用靶点,根据GeneCards数据库筛选乳腺癌相关淋巴水肿的靶基因;然后进行网络药理学筛选及分析,取中药和疾病的交集基因进行复方成分-靶点网络构建,并基于Bisogenet构建PPI网络及筛选,提取核心网络,应用Metascape数据库对交集靶点进行GO和KEGG富集分析,最后通过分子对接进行验证。结果:本研究最终纳入中药外治方总计48首,共143味中药。根据聚类分析及共现网络可视化分析,最终得出4味核心药物:黄芪、鸡血藤、桂枝和红花。对核心药物进行网络药理学分析,核心靶点包括NTRK1、TP53、ESR1、CUL3、MCM2、FN1、UBC、CDK2、COPS5、CUL7等,根据通路富集分析发现主要与癌症信号通路、糖尿病并发症相关AGE-RAGE信号通路、乙型肝炎、麻疹、丙型肝炎、PI3K-Akt、IL-17、Jak-STAT等通路相关。分子对接显示山奈酚、槲皮素、β-谷甾醇与ESR1和TP53具有较好的结合活性。结论:本研究通过数据挖掘筛选出中药外治方治疗乳腺癌相关淋巴水肿的核心药物,通过网络药理学分析其可能通过两个关键靶基因ESR1和TP53以及影响AGE-RAGE、IL-17等信号通路发挥治疗乳腺癌相关淋巴水肿的作用。
Objective:To analyze the medication law of external treatment of breast cancer related lymphedema with traditional Chinese medicine by using the ancient and modern medical case cloud platform v2.3.5 and Cytoscape 3.8.0,and further analyze the possible mechanism of action of core drugs through network pharmacology and molecular docking.Methods:Collect the literatures published by CNKI,Wanfang and VIP databases from the establishment of the database to November 1,2021,proving that the external treatment group of traditional Chinese medicine has definite curative effect or that the combined treatment group of traditional Chinese medicine is better than the non combined treatment group.Enter the qualified external treatment formulas of traditional Chinese medicine into the database for statistical analysis,screen out the core drugs,and discuss the follow-up mechanism.First,target prediction was carried out,the effective components and action targets of core drugs were screened through TCMSP database,and the target genes of breast cancer related lymphedema were screened according to genecards database.Then the network pharmacology screening and analysis were carried out,the intersection genes of traditional Chinese medicine and disease were taken to construct the compound component target network,and the PPI network and screening were constructed based on bisogenet,and the core network was extracted.The intersection targets were enriched and analyzed by GO and KEGG using metascape database,and finally verified by molecular docking.Results:A total of 48 external prescriptions of 143 traditional Chinese medicines were finally included in this study.According to cluster analysis and co-occurrence network visual analysis,four core drugs are finally obtained:Huangqi(Astragali radix),Jixueteng(Spatholobi caulis),Guizhi(Cinnamomi ramulus)and Honghua(Carthami flos).The core drugs were analyzed by network pharmacology.The core targets included ntrk1,TP53,ESR1,Cul3,MCM2,FN1,UBC,CDK2,cops5,cul7,etc.According to the pathway enrichment analysis,it was found that they were mainly related to cancer signaling pathway,AGE-RAGE signaling pathway related to diabetes complications,hepatitis B,measles,hepatitis C,PI3K Akt,IL-17,JAK stat and other pathways.Molecular docking showed kaempferol,quercetinβ-Sitosterol has good binding activity with ESR1 and TP53.Conclusion:In this study,the core drugs for the treatment of breast cancer-related lymphedema were screened out by TCM external therapy prescriptions through data mining.Through network pharmacology analysis,they may play their roles through two key target genes ESR1 and TP53,as well as affecting AGE-RAGE,IL-17 and other signaling pathways in the treatment of breast cancer-related lymphedema.
作者
陈筱筱
吴雪卿
邵士珺
瞿文超
陈玮黎
万华
CHEN Xiaoxiao;WU Xueqing;SHAO Shijun;QU Wenchao;CHEN Weili;WAN Hua(Shuguang Hospital,Shanghai University of Traditional Chinese Medicine,Shanghai 200120,China;Yueyang Hospital Integated Traditional Chinese and Western Medicine,Shanghai University of Traditional Chinese Medicine,Shanghai 200080,China)
出处
《中医药导报》
2022年第7期176-182,202,共8页
Guiding Journal of Traditional Chinese Medicine and Pharmacy
基金
上海市科学技术委员会科研计划项目(18ZR1440300),项目名称:乳癌术后方通过SDF-1/CXCR4及Wnt/β-catenin信号通路对乳腺癌肺转移过程中肿瘤转移屏障的影响。
关键词
淋巴水肿
乳腺癌
数据挖掘
古今医案云平台
网络药理学
分子对接
lymphedema
breast cancer
data mining
ancient and modern medical records cloud platform
network pharmacology
molecular docking
