基于网络药理学和生物信息学探究雷公藤多苷联合甲胺蝶呤治疗类风湿性关节炎机制

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摘要 目的: 本研究旨在通过网络药理学和机器学习方法,探讨雷公藤多苷联合甲氨蝶呤治疗类风湿性关节炎的潜在机制,旨在揭示其协同作用及分子基础。方法: 本研究利用CTD数据库（http：//ctdbase.org/）检索甲氨蝶呤和雷公藤多苷的主要成分,识别相关靶基因。随后,通过分析GEO数据库（https：//www.ncbi.nlm.nih.gov/geo/）中的类风湿性关节炎数据集,确定差异表达基因,并通过韦恩图识别药物与疾病的共享基因。利用机器学习算法筛选核心共享基因,并通过临床样本验证其作用。该方法结合网络药理学与机器学习,提供了系统的分析框架。结果: 通过系统性分析和机器学习方法,我们确定了IGFBP4和CASP7是雷公藤多苷联合甲氨蝶呤治疗类风湿性关节炎的潜在靶点。临床实验显示,治疗24周后,患者IGFBP4上调和CASP7下调的失调状态得到改善,且DS28指数显著下降。这表明,雷公藤多苷联合甲氨蝶呤可能通过调控IGFBP4和CASP7的表达,降低类风湿性关节炎的严重程度。结论: 本研究通过系统性分析和机器学习揭示了雷公藤多苷和甲氨蝶呤在类风湿性关节炎治疗中的分子机制,并确定了新的潜在治疗靶点,为个体化治疗策略提供了科学依据,具有重要的临床应用价值。

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	王燕
	李会龙
	焦爱军

关键词 ：类风湿性关节炎, 雷公藤多苷, 甲胺蝶呤, 网络药理学, 机器学习

Abstract：Objective This study aims to explore how Tripterygium glycosides and methotrexate work together to treat rheumatoid arthritis using network pharmacology and machine learning, focusing on their synergistic effects and underlying molecular mechanisms. Methods This study used the CTD database to identify target genes of methotrexate and triptolide, and analyzed rheumatoid arthritis datasets from the GEO database to find differentially expressed genes. Shared genes between the drugs and the disease were identified using Venn diagrams. Machine learning algorithms screened core shared genes, which were then validated with clinical samples. This approach integrates network pharmacology with machine learning. Results Using systematic analysis and machine learning, we've identified IGFBP4 and CASP7 as potential targets for rheumatoid arthritis treatment with tripterygium glycosides and methotrexate. Clinical trials show that after 24 weeks, the dysregulated IGFBP4 and CASP7 levels improve, and the DAS28 score significantly decreases, suggesting that this combination therapy may alleviate rheumatoid arthritis by regulating these proteins. Conclusion This study uses systematic analysis and machine learning to uncover the molecular mechanisms of tripterygium glycosides and methotrexate in treating rheumatoid arthritis, identifying new therapeutic targets. It offers a scientific basis for personalized treatments with significant clinical value.

Key words： rheumatoid arthritis triptolide methotrexate network pharmacology machine learning

收稿日期: 2024-03-21

通讯作者: 王燕,E-mail：hlong1123@163.com

引用本文:

王燕, 李会龙, 焦爱军. 基于网络药理学和生物信息学探究雷公藤多苷联合甲胺蝶呤治疗类风湿性关节炎机制[J]. 湖南师范大学学报(医学版), 2024, 21(4): 161-168. WANG Yan, LI Huilong, JIAO Aijun. Investigating the synergistic effects of Tripterygium glycosides and methotrexate in the management of rheumatoid arthritis through the utilization of network pharmacology and bioinformatics.. HuNan ShiFan DaXue XueBao(YiXueBan), 2024, 21(4): 161-168.

链接本文:

http://yxb.hunnu.edu.cn/CN/ 或 http://yxb.hunnu.edu.cn/CN/Y2024/V21/I4/161

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