Zhu Yifang, Li Min, Wang Qin, Yu Wenjing, Zhao Dan
Clinical Laboratory, Sichuan Province Orthopedic Hospital, Chengdu, Sichuan, China.
Department of Rheumatoid Osteoarthropathy, Sichuan Province Orthopedic Hospital, Chengdu, Sichuan, China.
Medicine (Baltimore). 2025 Aug 29;104(35):e42281. doi: 10.1097/MD.0000000000042281.
This study aims to explore the mechanism of artemisinin in treating osteoarthritis (OA) through bioinformatics and network pharmacology. The targets of artemisinin were obtained from databases such as TCMSP, and the disease targets of OA were screened from OMIM, TTD, DisGeNET, and GEO databases. The predicted targets of artemisinin were intersected with OA disease targets to obtain drug-disease common targets, which were visualized using a Venn diagram. Gene ontology (GO) analysis and KEGG functional analysis was performed on the 68 common target genes, and protein interaction network analysis was conducted to analyze their interaction relationships. The key genes were identified using the Cytohubba algorithm, followed by molecular docking with AutoDockTools 1.5.7 software and PyMOL software. Through database screening, 464 targets of artemisinin were identified, and 1654 OA target genes were screened from databases and GEO chip databases. The intersection of drug targets and disease targets yielded 68 drug-disease common targets. GO and KEGG analysis showed that these common target genes are mainly involved in oxidative stress response, bone formation, response to bacterial molecules, response to lipopolysaccharide, response to hypoxia, response to xenobiotic stimuli. Their molecular functions include regulation of transcription factor binding, ubiquitin-protein ligase activity, cytokine receptor binding. These common targets are enriched in 36 signaling pathways, including MAPK signaling pathway, PI3K-Akt signaling pathway, TNF signaling pathway, IL-17 signaling pathway, NF-Kappa B signaling pathway, which are key regulatory pathways in the development of OA. Through protein interaction analysis and Cytohubba algorithm, 10 key genes were obtained. Furthermore, the top 5 key genes (BCL-2, IL-6, CASP3, HIF1A, TNF) were molecular-docked with artemisinin, and the results showed that these molecules could form stable binding through hydrogen bonding and hydrophobic interaction. Artemisinin may exert drug efficacy through multi-target and multi-pathway synergism in the treatment of OA. This study provides an effective theoretical basis for the treatment of OA with artemisinin.
本研究旨在通过生物信息学和网络药理学探索青蒿素治疗骨关节炎(OA)的机制。青蒿素的靶点从诸如中药系统药理学数据库(TCMSP)等数据库中获取,OA的疾病靶点从在线人类孟德尔遗传数据库(OMIM)、治疗靶点数据库(TTD)、疾病基因数据库(DisGeNET)和基因表达综合数据库(GEO)中筛选。将青蒿素的预测靶点与OA疾病靶点进行交集分析,以获得药物-疾病共同靶点,并用维恩图进行可视化展示。对68个共同靶基因进行基因本体(GO)分析和京都基因与基因组百科全书(KEGG)功能分析,并进行蛋白质相互作用网络分析以分析它们的相互作用关系。使用Cytohubba算法鉴定关键基因,随后用AutoDockTools 1.5.7软件和PyMOL软件进行分子对接。通过数据库筛选,鉴定出464个青蒿素靶点,并从数据库和GEO芯片数据库中筛选出1654个OA靶基因。药物靶点与疾病靶点的交集产生了68个药物-疾病共同靶点。GO和KEGG分析表明,这些共同靶基因主要参与氧化应激反应、骨形成、对细菌分子的反应、对脂多糖的反应、对缺氧的反应、对外源生物刺激的反应。它们的分子功能包括转录因子结合调控、泛素-蛋白连接酶活性、细胞因子受体结合。这些共同靶点富集于36条信号通路,包括丝裂原活化蛋白激酶(MAPK)信号通路、磷脂酰肌醇-3激酶-蛋白激酶B(PI3K-Akt)信号通路、肿瘤坏死因子(TNF)信号通路、白细胞介素-17(IL-17)信号通路、核因子κB(NF-Kappa B)信号通路,这些都是OA发展中的关键调控通路。通过蛋白质相互作用分析和Cytohubba算法,获得了10个关键基因。此外,对排名前5的关键基因(BCL-2、IL-6、CASP3、HIF1A、TNF)与青蒿素进行分子对接,结果表明这些分子可通过氢键和疏水相互作用形成稳定结合。青蒿素在治疗OA时可能通过多靶点、多途径协同发挥药效。本研究为青蒿素治疗OA提供了有效的理论依据。