首页|期刊导航|浙江医学|通过网络药理学、机器学习及细胞实验探索泮托拉唑治疗特发性肺纤维化的分子机制

通过网络药理学、机器学习及细胞实验探索泮托拉唑治疗特发性肺纤维化的分子机制OA

Exploration of the molecular mechanism of pantoprazole in the treatment of idiopathic pulmonary fibrosis based on network phar-macology,machine learning,and cellular experiments

中文摘要英文摘要

目的 运用网络药理学、机器学习、分子对接及细胞实验探索泮托拉唑治疗特发性肺纤维化(IPF)的分子机制.方法 首先,采用网络药理学方法获取IPF相关的疾病靶点,构建交集并进行蛋白质-蛋白质相互作用网络分析.随后,采用机器学习筛选与IPF预后相关的核心基因,并基于这些基因构建预后模型.通过分子对接分析泮托拉唑与关键靶点的结合情况.将A549细胞分为空白对照组、转化生长因子-β1(TGF-β1)组、泮托拉唑钠(20、40、60 mg/L)组.TGF-β1组加入5 ng/mL TGF-β1刺激培养48 h,泮托拉唑钠组分别加入20、40、60 mg/L泮托拉唑钠和5 ng/mL TGF-β1共同刺激培养48 h.采用蛋白质印迹法检测上皮间质转化(EMT)相关标志物E-钙黏蛋白(E-cadherin)、波形蛋白(Vimentin)、基质金属蛋白酶(MMP)1、MMP8蛋白相对表达水平.结果 筛选出41个泮托拉唑治疗IPF的关键靶点,基因本体论、京都基因和基因组百科全书分析显示泮托拉唑主要涉及胶原蛋白分解、磷脂酰肌醇3-激酶/蛋白激酶B信号通路等生物学过程.机器学习方法筛选出7个核心靶点:MMP1、MMP7、MMP8、激酶插入结构域受体、丝裂原活化蛋白激酶8、丝氨酸蛋白酶抑制剂A1家族成员1、肉瘤病毒癌基因,并建立了有效的IPF预后模型.分子对接结果显示,泮托拉唑与这些靶点具有较强结合力.细胞实验结果表明,泮托拉唑能逆转TGF-β1诱导的A549细胞EMT,表现为上调E-cadherin,下调Vimentin、MMP1、MMP8等蛋白表达.结论 泮托拉唑可能通过多靶点、多信号途径发挥治疗IPF的作用.

Objective To explore the molecular mechanism of pantoprazole in treating idiopathic pulmonary fibrosis(IPF)using network pharmacology,machine learning,molecular docking,and cellular experiments.Methods First,network pharmacology was employed to identify targets associated with IPF;common targets were obtained and used to construct a protein-protein interaction(PPI)network.Subsequently,machine learning was applied to screen core genes related to IPF prognosis,and a prognostic model was built based on these genes.The binding interactions between pantoprazole and key targets were analyzed via molecular docking.A549 cells were divided into a blank control group,a transforming growth factor-β1(TGF-β1)group,and pantoprazole sodium groups(20,40,and 60 mg/L).The TGF-β1 group was stimulated with 5 ng/mL TGF-β1 for 48 h;the pantoprazole sodium groups were co-stimulated with 5 ng/mL TGF-β1 and respective concentrations of pantoprazole sodium(20,40,and 60 mg/L)for 48 h.Western blot was used to detect the relative protein expression levels of epithelial-mesenchymal transition(EMT)-related markers,including E-cadherin,Vimentin,matrix metalloproteinase(MMP)1,and MMP8.Results Forty-one key targets of pantoprazole in the treatment of IPF were screened.Gene ontology and Kyoto encyclopedia of genes and genomes analyses indicated that pantoprazole was primarily involved in biological processes such as collagen catabolism and the phosphatidylinositol 3-kinase/protein kinase B signaling pathway.Machine learning identified seven core targets:MMP1,MMP7,MMP8,kinase insert domain receptor,mitogen-activated protein kinase 8,serpin family A member 1,and sarcoma virus oncogene;an effective prognostic model for IPF was established based on these targets.Molecular docking demonstrated strong binding affinity between pantoprazole and these targets.Cellular experiments revealed that pantoprazole reversed TGF-β1 induced EMT in A549 cells,as evidenced by up regulated E-cadherin expression and down regulated expression of Vimentin,MMP1,MMP8,and other proteins.Conclusion Pantoprazole may exert therapeutic effects against IPF through multiple targets and signaling pathways.

王剑;傅晓芳;李星星;沈林峰;陈哲

311100 杭州市临平区第一人民医院呼吸与危重症医学科311100 杭州市临平区第一人民医院呼吸与危重症医学科311100 杭州市临平区第一人民医院肿瘤内科311100 杭州市临平区第一人民医院呼吸与危重症医学科温岭市第一人民医院呼吸与危重症医学科

泮托拉唑特发性肺纤维化机器学习网络药理学分子对接

PantoprazoleIdiopathic pulmonary fibrosisMachine learningNetwork pharmacologyMolecular docking

《浙江医学》 2026 (2)

131-138,后插1-后插2,10

浙江省医药卫生科技计划项目(2024KY273)浙江省中医药科技计划项目(2026ZL0706)

10.12056/j.issn.1006-2785.2026.48.2.2025-760

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