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中国临床药理学与治疗学 ›› 2023, Vol. 28 ›› Issue (9): 988-999.doi: 10.12092/j.issn.1009-2501.2023.09.004

• 基础研究 • 上一篇    下一篇

基于血清代谢组学与网络药理学探究复方苦参注射液治疗肺癌的作用机制

游蓉丽1,2,黄玉荣3,毛 睿4,海丽娜2,王颖莉4,王 艳3   

  1. 1山西振东制药股份有限公司,长治  047100,山西;2北京振东光明药物研究院有限公司,北京  100085;3山西中医药大学,中药与食品工程学院,晋中  030619,山西;4山西中医药大学,实验管理中心,晋中  030619,山西

  • 收稿日期:2023-03-28 修回日期:2023-06-05 出版日期:2023-09-26 发布日期:2023-09-25
  • 通讯作者: 王艳,女,博士,副教授,研究方向:中药药理与毒理。 E-mail:wy180119@sxtcm.edu.cn
  • 基金资助:
    山西省科技攻关项目(2016ZD0401)

Mechanism of compound kushen injection in the treatment of lung cancer based on serum metabolomics and network pharmacology

YOU Rongli1,2, HUANG Yurong3, MAORui4, HAI Lina2, WANG Yingli4, WANG Yan3   

  1. 1Shanxi Zhendong Pharmaceutical Limited Company, Changzhi 047100, Shanxi, China; 2Beijing Zhendong Guangming Pharmaceutical Research Institute Limited Company, Beijing 100085, China; 3College of Chinese Medicine and Food Engineering, Shanxi University of Chinese Medicine, Jinzhong 030619, Shanxi, China; 4Experimental Management Center, Shanxi University of Chinese Medicine, Jinzhong 030619, Shanxi, China
  • Received:2023-03-28 Revised:2023-06-05 Online:2023-09-26 Published:2023-09-25
  • About author:游蓉丽,女,博士,研究方向:药物研发、药事管理。 E-mail:yourongli@zdjt.com

摘要:

目的:运用血清代谢组学、网络药理学及分子对接技术探讨复方苦参注射液(CKI)中生物碱类成分在治疗肺癌时的作用机制。方法:采用Lewis小鼠肺癌瘤株接种于C57小鼠建立肺癌模型,将雄性小鼠随机分为正常组、模型组和CKI组。尾静脉注射给药,每日1次,连续17 d。通过超高效液相色谱-串联质谱(LC-MS)代谢组学技术对小鼠血清进行检测,采用主成分分析(PCA)及正交偏最小二乘法判别分析(OPLS-DA)等多元统计分析,结合人类代谢数据库(HMDB)等数据库和相关文献指认、鉴定差异代谢物,通过MetaboAnalyst在线工具寻找相关的代谢途径。采用网络药理学,构建CKI治疗肺癌的“成分-靶点-疾病”网络。采用分子对接法验证潜在活性成分与核心靶点的相互作用。将血清代谢组学与网络药理学联合分析构建“代谢物-反应-酶-基因”网络。结果:通过代谢组学技术,从血清中筛选出与肺癌相关的16种差异代谢物,与模型组相比,CKI能回调这些差异代谢物水平。代谢通路主要涉及视黄醇代谢、色氨酸代谢、甘油磷脂代谢等代谢途径。网络药理学分析表明CKI治疗肺癌主要作用于STAT3、MAPK3、MAPK1等靶点蛋白,与肿瘤中的蛋白聚糖、细胞衰老、HIF-1信号通路密切相关。结论:本研究从代谢组学与网络药理学的角度阐释了CKI在治疗肺癌时的作用机制,为进一步研究CKI提供基础。

关键词: 复方苦参注射液, 肺癌, 代谢组学, 网络药理学, 分子对接

Abstract:

AIM: To explore the mechanism of action of alkaloid components of compound kushen Injection (CKI) in the treatment of lung cancer based on serum metabolomics, network pharmacology, and molecular docking techniques. METHODS: A lung cancer model was established in C57 mice by inoculation of Lewis mouse lung cancer tumor strain. Thirty male mice were randomly divided into normal group, model group and CKI group. The drug was administered by tail vein injection once daily for 17 consecutive days. Mouse serum was examined by ultrahigh performance liquid chromatography tandem mass spectrometry (LC-MS) metabolomics, and several multivariate statistical analyses including principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA), combined with databases such as the human metabolic database (HMDB) and related literature to identify and identify differential metabolites, the relevant metabolic pathways were searched for by the metaboanalyst online tool. Using network pharmacology, construct the “component-target-disease” network of CKI in the treatment of lung cancer. Molecular docking method was used to verify the interaction between potential active ingredients and core targets. Serum metabolomics was jointly analyzed with network pharmacology to construct a “metabolite-germinal-enzyme-gene” network. RESULTS: Through metabolomics technology, 16 differential metabolites associated with lung cancer were screened from serum, and CKI addback these differential metabolite levels compared with the model group. Metabolic pathways mainly involve retinol metabolism, tryptophan metabolism, glycerophospholipid metabolism and other metabolic pathways. Network pharmacology analysis indicated that CKI treatment of lung cancer mainly targets STAT3, MAPK3, and MAPK1, which are closely related to proteoglycans, cellular senescence, and HIF?1 signaling pathways in cancer. CONCLUSION: This article explains the mechanism of CKI in treating lung cancer from the perspective of metabonomics and network pharmacology, and provides basis for further study of CKI.

Key words: compound kushen injection, lung cancer, metabolomics, network pharmacology, molecular docking

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