Chen Ping, Ye Baibai, Lin Cheng, Zhang Chenning, Chen Jia, Li Linfu
Pharmacy College, Gannan Medical University, Ganzhou, Jiangxi, China.
Department of Pharmacy, Xiangyang No. 1 People's Hospital, Hubei University of Medicine, Xiangyang, China.
Tzu Chi Med J. 2024 Jul 8;37(1):99-108. doi: 10.4103/tcmj.tcmj_77_24. eCollection 2025 Jan-Mar.
This study aimed to explore the potential mechanisms of TMF (5,7,3',4'-tetramethoxyflavone) in treating osteoarthritis (OA) using network pharmacology and molecular docking.
Databases including SwissTargetPrediction, BATMAN-TCM, PharmMapper, TargetNet, SuperPred, and SEA were utilized to screen the targets of TMF. "OA" was used as the disease keyword to predict OA-related genes through GeneCards, Therapeutic Target Database, PharmGKB, Online Mendelian Inheritance in Man, and Comparative Toxicogenomics Database. The Venn diagram was employed to identify the intersection of predicted targets between TMF and OA as potential targets for TMF in treating OA. The intersection targets were input into the STRING 12.0 online database to construct a protein-protein interaction (PPI) network and identify core targets. Subsequently, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed using the Metascape V3.5 online database platform. Finally, molecular docking between TMF and core targets was conducted using AutoDockTools 1.5.6.
A total of 228 intersection targets for TMF treating OA were obtained, and PPI network analysis identified 5 core targets: STAT3, SRC, CTNNB1, EGFR, and AKT1. GO enrichment analysis yielded 2736 results, while KEGG analysis identified 203 pathways. Most elated GO and KEGG items of TMF in treating OA may include hormonal responses, antiviral and anticancer effects, anti-inflammation, phosphorus metabolism, phosphate metabolism, nitrogen compound responses, cancer-related pathways, PI3K-Akt signaling pathway, and MAPK signaling pathway. Molecular docking revealed good binding affinities between TMF and all core targets except STAT3.
TMF might act on multiple targets and activate diverse pathways to intervene in OA, revealing the molecular processes involved in TMF treatment of OA.
本研究旨在利用网络药理学和分子对接技术探索5,7,3',4'-四甲氧基黄酮（TMF）治疗骨关节炎（OA）的潜在机制。
利用包括SwissTargetPrediction、BATMAN-TCM、PharmMapper、TargetNet、SuperPred和SEA在内的数据库筛选TMF的靶点。以“OA”作为疾病关键词，通过GeneCards、治疗靶点数据库、药物基因组学知识库、人类孟德尔遗传在线数据库和比较毒理基因组学数据库预测OA相关基因。采用维恩图确定TMF与OA预测靶点的交集，作为TMF治疗OA的潜在靶点。将交集靶点输入STRING 12.0在线数据库构建蛋白质-蛋白质相互作用（PPI）网络并确定核心靶点。随后，使用Metascape V3.5在线数据库平台进行基因本体（GO）和京都基因与基因组百科全书（KEGG）通路富集分析。最后，使用AutoDockTools 1.5.6进行TMF与核心靶点之间的分子对接。
共获得228个TMF治疗OA的交集靶点，PPI网络分析确定了5个核心靶点：信号转导和转录激活因子3（STAT3）、原癌基因酪氨酸蛋白激酶（SRC）、β-连环蛋白（CTNNB1）、表皮生长因子受体（EGFR）和蛋白激酶B（AKT1）。GO富集分析产生2736个结果，KEGG分析确定203条通路。TMF治疗OA最相关的GO和KEGG条目可能包括激素反应、抗病毒和抗癌作用、抗炎、磷代谢、磷酸盐代谢、氮化合物反应、癌症相关通路、磷脂酰肌醇-3激酶-蛋白激酶B（PI3K-Akt）信号通路和丝裂原活化蛋白激酶（MAPK）信号通路。分子对接显示TMF与除STAT3外的所有核心靶点之间具有良好的结合亲和力。
TMF可能作用于多个靶点并激活多种通路来干预OA，揭示了TMF治疗OA所涉及的分子过程。
