文献类型：期刊文献

中文题名：基于网络药理学探讨治疗甲状腺癌的靶向药物

英文题名：Study on the Targeted Drugs for the Treatment of Thyroid Cancer Based on Network Pharmacology

作者：王斯炯[1];庞月苓[2];宋浩[1];刘润泽[1];陈丽苹[2];张宇[2];马欢欢[2];何振宇[2];丁小山[2];丁楠[1];杨庚宁[1];李权洲[1];权欣怡[1];李玲[2,3];李佳蔚[2,3,4]

第一作者：王斯炯

机构：[1]甘肃中医药大学第一临床学院,甘肃 兰州;[2]甘肃中医药大学基础医学院,甘肃 兰州;[3]甘肃中医药大学,甘肃省高校重大疾病分子医学与中医药防治研究重点实验室,甘肃 兰州;[4]甘肃中医药大学,甘肃省中医药防治慢性疾病重点实验室,甘肃 兰州

第一机构：甘肃中医药大学中医临床学院

年份：2024

卷号：14

期号：9

起止页码：453

中文期刊名：临床医学进展

外文期刊名：Advances in Clinical Medicine

语种：中文

中文关键词：网络药理学;甲状腺癌;整合治疗;靶向机制

摘要：目的:甲状腺癌是一种常见的内分泌系统恶性肿瘤,在全球新诊断的肿瘤中占比约2.1%。近年来,甲状腺癌的发病率呈现上升趋势,对人类健康构成了严重威胁。本研究综述了当前用于甲状腺癌治疗的靶向药物,并运用网络药理学方法进行了分析,以揭示其作用机制和可能的治疗靶点为目的,为进一步研究和临床实践甲状腺癌靶向药物应用提供新的思路。方法:首先,通过文献的检索,对目前治疗甲状腺癌的靶向药物进行了检索,包括索拉非尼、仑伐替尼、凡德他尼、卡博替尼等。再从网络药理学的角度进行探究,借助DrugBank、Uniprot数据库和文献检索分子靶点与药物作用靶点。GeneCards和DrugBank和数据库,利用VENNY 2.1网站将所得到的有效成分靶点与Thyroid Carcinoma靶点构建韦恩图。采用STRING数据库和Cytoscape软件构建蛋白–蛋白相互作用网络及“药物–潜在活性成分–作用靶点”相互作用网络(PPI)。通过DAVID数据库对共同靶点进一步进行基因本体论(GO)功能分析和京都基因与基因组百科全书(KEEG)通路富集分析,筛选关键通路,并通过微生信绘制GO网络图、KEGG图和关键靶点–通路图,揭示药物可能发挥作用的机制。结果:通过针对甲状腺癌的靶向治疗药物主要有索拉非尼、仑伐替尼、凡德他尼、卡博替尼等多种分子靶向药物。基于数据库和软件筛选,确定有效成分及其相关靶点。结合药物–组分–靶点网络图揭示在胚胎发育、细胞增殖等方面的重要性。分析GO功能富集揭示药物调节多细胞生物发育、细胞增殖等生物学过程发挥作用。KEGG功能富集分析显示MAPK讯号路径、PI3K-AKT讯号路径等多条机制路径与甲状腺癌治疗高度相关。结论:分析目前治疗甲状腺癌的靶向药物及其作用机制,揭示临床药物通过多途径介入治疗甲状腺癌的发生和发展。同时构建的药物–成分–靶点网络及功能富集分析为进一步临床整合治疗提供重要参考。Objective: Thyroid cancer is a common malignant tumor of the endocrine system, accounting for approximately 2.1% of newly diagnosed tumors on the globe. In recent years, the incidence rate of thyroid cancer is on the rise, posing a serious threat to human health. Therefore, this study reviews the targeted drugs currently used for the treatment of thyroid cancer and analyzes them using network pharmacology methods to reveal their mechanisms of action and potential therapeutic targets, providing new ideas for further research and clinical practice of the applications of targeted drug in thyroid cancer. Methods: First, a search was done on the current targeted drugs for the treatment of thyroid cancer by searching relevant literatures, including sorafenib, lenvatinib, vandetanib, cabozantinib and so on. Then, exploring from the perspective of network pharmacology, DrugBank, Uniprot databases, literature searches were used to identify molecular targets and drug targets. GeneCards, DrugBank and databases were searched and the VENNY 2.1 website was used to construct a Venn diagram by combining the obtained effective ingredient targets with thyroid cancer targets. Protein-Protein Interaction Networks and Drug-Potential Active Ingredient-Target Interaction Networks (PPI) were constructed using STRING database and Cytoscape software. Gene Ontology (GO) functional analysis and Kyoto Encyclopedia of Genes and Genomes (KEEG) pathway enrichment analysis were further conducted on common targets through the DAVID database to screen for key pathways. GO network diagrams, KEGG diagrams and key target pathways diagrams were drawn using Microbiology Data to reveal the possible mechanisms of drug functions. Results: Currently, the targeted therapeutic drugs for thyroid cancer mainly include molecular targeted drugs such as sorafenib, lenvatinib, vandetanib, cabozantinib and so on. Based on databases and software screening, the effective ingredients and related targets were determined. Combining with the drug-component-target network diagram, the importance of some targets in embryonic development, cell proliferation, and other aspects were revealed. Analyzing GO functional enrichment revealed that drugs exert their effects by regulating biological processes such as multicellular biological development and cell proliferation. KEGG functional enrichment analysis showed that multiple pathways such as MAPK signaling pathway and PI3K-AKT signaling pathway and so on were highly correlated with the treatment of thyroid cancer. Conclusion: We analyzed the current targeted drugs for the treatment of thyroid cancer and their mechanisms of function, revealing that clinical drugs intervene in the treatment of the occurrence and development of thyroid cancer through multiple pathways. At the same time, the constructed drug-component-target network and functional enrichment analysis provide important references for further clinical integrated therapy.