Du Zhiyong, Sun Cuiping, Wu Jiawei, Gao Hongwei, Wu Jialong, Zhou You, Wu Xuechao, Shen Liping, Wang Qing
Department of Neurosurgery, Wuxi No. 2 People's Hospital (Jiangnan University Medical Center), Wuxi, China.
Wuxi School of Medicine, Jiangnan University, Wuxi, China.
Front Pharmacol. 2025 May 15;16:1546285. doi: 10.3389/fphar.2025.1546285. eCollection 2025.
This investigation sought to explore the inhibitory impact of wogonin on prolactinoma and elucidate its underlying mechanisms through network pharmacology, molecular docking (MD), and molecular biology experiments.
Target identification for wogonin and prolactinoma was conducted using relevant databases, followed by protein-protein interaction (PPI) analysis of intersecting targets via the STRING database. Functional and pathway enrichment analyses were executed utilizing Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) methodologies. Hub genes were identified from the PPI network, and MD was utilized to assess the binding patterns and interaction strength between wogonin and hub targets. Network pharmacological findings were further validated through and experiments.
A sum of 137 drug targets for wogonin and 3,942 disease targets for prolactinoma were identified, with 37 overlapping targets. Nine hub genes were screened, including KDR, EGFR, BCL2, IL6, ESR1, MYC, CCL2, PTGS2, and ESR2. GO and KEGG analyses revealed that wogonin was closely associated with several critical signaling cascades. MD analysis confirmed robust binding interactions between wogonin and the identified hub targets. Cellular experiments suggested that wogonin suppressed cell proliferation and triggered apoptosis in prolactinoma cells in a time- and concentration-dependent manner, primarily via inhibition of the PI3K/AKT signaling cascades. Animal studies further revealed that wogonin markedly suppressed tumor growth and enhanced prolactinoma sensitivity to bromocriptine.
These findings suggest that wogonin exerts its anti-prolactinoma effects via multiple targets and signaling cascades, establishing a robust scientific basis for the development and screening of novel anti-prolactinoma therapeutics.
本研究旨在探讨汉黄芩素对催乳素瘤的抑制作用,并通过网络药理学、分子对接(MD)和分子生物学实验阐明其潜在机制。
利用相关数据库对汉黄芩素和催乳素瘤进行靶点识别,然后通过STRING数据库对交叉靶点进行蛋白质-蛋白质相互作用(PPI)分析。利用基因本体论(GO)和京都基因与基因组百科全书(KEGG)方法进行功能和通路富集分析。从PPI网络中识别出枢纽基因,并利用分子对接评估汉黄芩素与枢纽靶点之间的结合模式和相互作用强度。通过细胞和动物实验进一步验证网络药理学研究结果。
共鉴定出137个汉黄芩素的药物靶点和3942个催乳素瘤的疾病靶点,其中有37个重叠靶点。筛选出9个枢纽基因,包括KDR、EGFR、BCL2、IL6、ESR1、MYC、CCL2、PTGS2和ESR2。GO和KEGG分析表明,汉黄芩素与几个关键信号级联密切相关。分子对接分析证实了汉黄芩素与所识别的枢纽靶点之间有强大的结合相互作用。细胞实验表明,汉黄芩素以时间和浓度依赖性方式抑制催乳素瘤细胞的增殖并诱导其凋亡,主要是通过抑制PI3K/AKT信号级联。动物研究进一步表明,汉黄芩素显著抑制肿瘤生长,并增强催乳素瘤对溴隐亭的敏感性。
这些发现表明,汉黄芩素通过多个靶点和信号级联发挥其抗催乳素瘤作用,为开发和筛选新型抗催乳素瘤治疗药物奠定了坚实的科学基础。