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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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

Received: 21 March 2024

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	WANG Yan
	LI Huilong
	JIAO Aijun

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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.[J]. HuNan ShiFan DaXue XueBao(YiXueBan), 2024, 21(4): 161-168.

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http://yxb.hunnu.edu.cn/EN/ OR http://yxb.hunnu.edu.cn/EN/Y2024/V21/I4/161

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