机器学习驱动下发现STAT3作为温胃散胃方治疗慢性萎缩性胃炎的关键靶点

Tingting Huang , Lu Wang , Mengru Dou , Jia Guo , Kaihua Long , Yuan Wang , Yang Liu , Bo Wang , Weijian Zhao , Shanrong Han , Jingyi Bai , Xinli Wen , Ye Li , Yuxi Liu , Hong Zhang
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引用次数: 0

摘要

温胃散积方(WWSJ)是治疗慢性萎缩性胃炎(CAG)的中药方剂,临床疗效显著。然而,其具体作用机制尚不清楚。本研究采用网络药理学、机器学习、分子对接等方法,结合实验验证,探讨了WWSJ对CAG的分子机制和活性成分。方法利用TCMSP和Uniprot数据库筛选WWSJ的活性化合物和靶点,从GeneCards和OMIM数据库筛选CAG的靶点。利用STRING和Cytoscape构建蛋白-蛋白相互作用网络和中草药-化合物-靶点网络。用DAVID进行功能富集。机器学习(PCA/LASSO/RF)从GEO数据中识别核心目标。在分子对接研究中,AutoDock Vina用于评价结合活性。通过体外和体内实验对结果进行验证。结果共筛选出53种有效成分,其中(OB)≥30%,(DL)≥0.1。对公式靶点(1657)和CAG病靶点(923)进行筛选,共鉴定出207个共同靶点。通过GO和KEGG对WWSJ进行功能分析,发现742个生物过程(BP)、85个细胞成分(CC)、116个分子功能(MF)和149个信号通路与CAG相关。此外,通过机器学习和分子对接鉴定出5个核心基因。这些化合物与靶蛋白具有较强的结合亲和力,尤其是与STAT3。因此,我们假设WWSJ可以通过抑制STAT3信号传导来治疗CAG。细胞和动物实验结果表明,WWSJ通过抑制STAT3对CAG有治疗作用。结论综合网络药理学预测和机器学习优化确定STAT3为WWSJ的主要治疗靶点。实验研究证实,WWSJ通过抑制STAT3磷酸化来缓解CAG。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Machine learning-driven discovery of STAT3 as a pivotal target for Wen-Wei-San-Ji formula in chronic atrophic gastritis therapy

Background

Wen-Wei-San-Ji Formula (WWSJ) is a Traditional Chinese medicine (TCM) formula used in treatment of chronic atrophic gastritis (CAG), demonstrating significant clinical efficacy. However, its specific mechanism of action remains unclear. This study investigates the molecular mechanisms and active components of WWSJ for CAG by employing network pharmacology, machine learning, and molecular docking, complemented by experimental validation.

Methods

Active compounds and targets of WWSJ were screened using TCMSP and Uniprot databases, while targets for CAG were collected from GeneCards and OMIM databases. Protein-protein-interaction and herb-compound-target networks were constructed using by STRING and Cytoscape. Functional enrichment was performed with DAVID. Machine learning (PCA/LASSO/RF) identified core targets from GEO data. In molecular docking studies, AutoDock Vina was used to evaluate binding activity. In vitro and in vivo experiments were conducted to validate the results.

Results

A total of 53 active ingredients with (OB)≥30% and (DL)≥0.1 from WWSJ were selected. Targets of the formula (1657) and CAG disease (923) were screened, resulting in the identification of 207 common targets. Functional analysis of WWSJ by GO and KEGG revealed 742 Biological Processes (BP), 85 Cellular Components (CC), 116 Molecular Functions (MF), and 149 signaling pathways associated with CAG. Additionally, 5 core genes were identified through machine learning and molecular docking. The compounds demonstrated strong binding affinity with the target proteins, especially STAT3. Therefore, we hypothesized that WWSJ can treat CAG through inhibiting STAT3 signaling. Results of cell and animal experiments indicated that WWSJ is effective in the treatment of CAG through the inhibition of STAT3.

Conclusion

Integrated network pharmacology predictions and machine learning optimization identified STAT3 as the primary therapeutic target of WWSJ. Experimental studies confirmed that WWSJ alleviates CAG by suppressing STAT3 phosphorylation.
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