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基于网络药理学和生物信息学探究黄芪抗非小细胞肺癌的关键靶点和分子机制 被引量:10

Exploration on the hub targets and molecular mechanism of Astragali radix against non-small cell lung cancer based on network pharmacology and bioinformatics
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摘要 目的应用网络药理学和生物信息学方法预测和探讨黄芪抗非小细胞肺癌(Non-small cell lung cancer,NSCLC)的关键靶点和作用机制。方法通过TCMSP数据库检索黄芪活性成分和靶点,利用GeneCards、DisGeNET和DigSee数据库获得NSCLC相关靶点并与药物作用靶点映射,使用Cytoscape软件构建“黄芪—活性成分—靶点—NSCLC”网络;采用String数据库和Cytoscape构建靶标蛋白互作网络,采用MCODE插件筛选功能聚类模块和关键基因,运用DAVID数据库和R软件将核心模块中基因进行基因本体(GO)和京都基因与基因组百科全书(KEGG)富集分析,通过UALCAN和KM plotter数据库分析关键基因在NSCLC中的表达和对患者总生存率的影响。结果从“黄芪—活性成分—靶点—NSCLC”网络中得到9个活性成分,44个靶标,3个功能聚类模块和关键基因。GO富集到涉及转录和凋亡调控、细胞对刺激的反应、一氧化氮生物合成、细胞增殖和蛋白质修饰等生物过程104个,11个细胞组分主要定位在细胞核、脂筏、胞浆、细胞外间隙、线粒体、染色体端粒区等,19个分子功能主要参与蛋白和酶结合、转录因子结合、增殖和凋亡相关激酶活性等;KEGG富集到癌症通路、P53信号通路、细胞周期、FoxO信号通路、黏着斑、PI3K-Akt信号通路、癌症中心碳代谢等48个通路。关键基因PARP1、GSTP1和ESR2在NSCLC中表达量均高于正常组织(P<0.05),相比低表达者,高表达者总体生存率较低,差异有统计学意义(P<0.05)。结论本研究初步揭示了黄芪抗NSCLC的分子机制,发现PARP1、GSTP1和ESR2有望作为关键靶点,体现了中药多成分、多靶点、多途径协同作用的特点,为黄芪抗肿瘤研究及临床应用提供理论依据。 Objective To explore the hub targets and mechanism of Astragali radix against non-small cell lung cancer(NSCLC)based on network pharmacology and bioinformatics.Methods The traditional Chinese medicine system pharmacology platform(TCMSP)was used to screen out the active ingredients and targets of Astragali radix for anti-NSCLC.The GeneCards,DisGeNET and DigSee databases were employed to acquire the targets of NSCLC and were mapped with drug targets,then the network of"Astragali radix-active ingredients-targets-NSCLC"was constructed by using the software of Cytoscape.The protein interactions network was constructed by using String database and Cytoscape software,and the MCODE plug-in was used to screen functional clustering modules and hub genes.The function analysis of Gene Ontology and Kyoto Encyclopedia of Genes and Genomes of genes in the key module were enriched and analyzed through DAVID database and R software.The UALCAN and KM plotter databases were employed to analyze the expression of hub genes in NSCLC and its effect on the overall survival rate of patients.Results Totally 9 active ingredients,44 targets and 3 functional clustering modules and hub genes were obtained from the network of“Astragali radix-active ingredients-targets-NSCLC”.GO function enrichment showed that the 104 biological processes were concentrated in transcription and apoptosis-regulation,cell response to stimulation,NO biosynthesis,cell proliferation and protein modification.Eleven cellular components were mainly located in the nucleus,membrane raft,cytoplasm,extracellular space,mitochondria and telomeric region of chromosomes.Nineteen molecular functions were mainly involved in protein and enzyme binding,transcription factor binding,proliferation and apoptosis-related kinase activity.KEGG pathway enrichment showed that the 48 pathways were mainly concentrated in cancer pathways,p53 signal pathway,cell cycle,FoxO signal pathway,focal adhesion,PI3K-Akt signal pathway and central carbon metabolism in cancer.The expression of hub genes such as PARP1,GSTP1 and ESR2 in NSCLC was higher than that in normal tissues(P<0.05).Compared with the low-expression group,the overall survival rate of the high-expression group was lower,and the difference was statistically significant(P<0.05).Conclusion The study preliminarily reveals the molecular mechanism of Astragali radix against NSCLC and reflects the multi-components,multi-targets and multi-pathway synergistic characteristics of TCMs.PARP1,GSTP1 and ESR2 are expected to be the hub genes.It can provide theoretical basis for anti-tumor research and clinical application of Astragali radix.
作者 叶松山 刘云鹤 李文涛 夏颖 赵芷洁若 于建春 YE Song-shan;LIU Yun-he;LI Wen-tao;XIA Ying;ZHAO Zhi-jie-ruo;YU Jian-chun(Department of Oncology,First Teaching Hospital of Tianjin University of Traditional Chinese Medicine,Tianjin 300193,China;Graduate School of Tianjin University of Traditional Chinese Medicine,Tianjin 301617,China)
出处 《实用药物与临床》 CAS 2020年第10期876-885,共10页 Practical Pharmacy and Clinical Remedies
基金 天津市教委科研计划项目(2018KJ034)。
关键词 黄芪 非小细胞肺癌 网络药理学 生物信息学 分子机制 Astragali radix NSCLC Network pharmacology Bioinformatics Molecular mechanism
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