Affiliated Hospital of Jiangxi University of Chinese Medicine, Nanchang 330006, China.
Guangzhou Municipal and Guangdong Provincial Key Laboratory of Molecular Target & Clinical Pharmacology, The NMPA and State Key Laboratory of Respiratory Disease, School of Pharmaceutical Sciences and the Fifth Affiliated Hospital, Guangzhou Medical University, Guangzhou 511436, China.
Biomed Res Int. 2023 Jan 23;2023:6407588. doi: 10.1155/2023/6407588. eCollection 2023.
To screen the main active components of through a network pharmacology approach, construct a component-disease target network, explore its molecular mechanism for the treatment of non-small-cell lung cancer (NSCLC), and validate it experimentally.
The active ingredients in and the targets of and NSCLC were collected through the Traditional Chinese Medicine Systematic Pharmacology Database and Analysis Platform (TCMSP), GeneCards, and OMIM databases. The protein interaction network was constructed using the STRING database, and the component-disease relationship network graph was analyzed using Cytoscape 3.9.1. The Metascape database can be used for GO and KEGG enrichment analyses. The Kaplan-Meier plotter was applied for overall survival analysis of key targets of in the treatment of NSCLC. Real-time PCR (RT-PCR) and Western blotting were used to determine the mRNA and protein levels of key targets of for the treatment of NSCLC.
Five active ingredients of were screened, and 54 potential targets for the treatment of NSCLC were found, of which the key ingredient was nobiletin and the key targets are TP53, CXCL8, ESR1, PPAR-, and MMP9. GO and KEGG enrichment analyses indicated that the mechanism of nobiletin in treating NSCLC may be related to the regulation of cancer signaling pathway, phosphatidylinositol-3 kinase (PI3K)/protein kinase B (Akt) signaling pathway, lipid and atherosclerosis signaling pathway, and neurodegenerative signaling pathway. The experimental results showed that nobiletin could inhibit the proliferation of NSCLC cells and upregulate the levels of P53 and PPAR- and suppress the expression of MMP9 ( < 0.05).
can participate in the treatment of NSCLC through multiple targets and pathways.
通过网络药理学方法筛选 的主要活性成分，构建成分-疾病靶标网络，探讨其治疗非小细胞肺癌（NSCLC）的分子机制，并通过实验进行验证。
通过中药系统药理学数据库和分析平台（TCMSP）、GeneCards 和 OMIM 数据库收集 的活性成分和 及 NSCLC 的靶点。使用 STRING 数据库构建蛋白质相互作用网络，使用 Cytoscape 3.9.1 分析成分-疾病关系网络图。使用 Metascape 数据库进行 GO 和 KEGG 富集分析。Kaplan-Meier plotter 用于分析 治疗 NSCLC 的关键靶标总体生存率。实时 PCR（RT-PCR）和 Western blot 用于测定 治疗 NSCLC 的关键靶标基因的 mRNA 和蛋白水平。
筛选出 中的 5 种活性成分，发现 54 个治疗 NSCLC 的潜在靶标，其中关键成分是诺必亭，关键靶标是 TP53、CXCL8、ESR1、PPAR- 和 MMP9。GO 和 KEGG 富集分析表明，诺必亭治疗 NSCLC 的机制可能与癌症信号通路、磷脂酰肌醇-3 激酶（PI3K）/蛋白激酶 B（Akt）信号通路、脂质和动脉粥样硬化信号通路以及神经退行性信号通路的调节有关。实验结果表明，诺必亭可抑制 NSCLC 细胞的增殖，上调 P53 和 PPAR-的水平，并抑制 MMP9 的表达（<0.05）。
可通过多个靶标和途径参与治疗 NSCLC。
