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通过生物信息学和机器学习破译葛根芩连汤治疗2型糖尿病和溃疡性结肠炎的共同机制

Deciphering the shared mechanisms of Gegen Qinlian Decoction in treating type 2 diabetes and ulcerative colitis via bioinformatics and machine learning.

作者信息

Hu Faquan, Xiong Liyuan, Li Zhengpin, Li Lingxiu, Wang Li, Wang Xinheng, Zhou Xuemei, Zheng Yujiao

机构信息

College of Traditional Chinese Medicine, Anhui University of Chinese Medicine, Hefei, China.

出版信息

Front Med (Lausanne). 2024 Jun 19;11:1406149. doi: 10.3389/fmed.2024.1406149. eCollection 2024.

Abstract

BACKGROUND

Although previous clinical studies and animal experiments have demonstrated the efficacy of Gegen Qinlian Decoction (GQD) in treating Type 2 Diabetes Mellitus (T2DM) and Ulcerative Colitis (UC), the underlying mechanisms of its therapeutic effects remain elusive.

PURPOSE

This study aims to investigate the shared pathogenic mechanisms between T2DM and UC and elucidate the mechanisms through which GQD modulates these diseases using bioinformatics approaches.

METHODS

Data for this study were sourced from the Gene Expression Omnibus (GEO) database. Targets of GQD were identified using PharmMapper and SwissTargetPrediction, while targets associated with T2DM and UC were compiled from the DrugBank, GeneCards, Therapeutic Target Database (TTD), DisGeNET databases, and differentially expressed genes (DEGs). Our analysis encompassed six approaches: weighted gene co-expression network analysis (WGCNA), immune infiltration analysis, single-cell sequencing analysis, machine learning, DEG analysis, and network pharmacology.

RESULTS

Through GO and KEGG analysis of weighted gene co-expression network analysis (WGCNA) modular genes and DEGs intersection, we found that the co-morbidity between T2DM and UC is primarily associated with immune-inflammatory pathways, including IL-17, TNF, chemokine, and toll-like receptor signaling pathways. Immune infiltration analysis supported these findings. Three distinct machine learning studies identified IGFBP3 as a biomarker for GQD in treating T2DM, while BACE2, EPHB4, and EPHA2 emerged as biomarkers for GQD in UC treatment. Network pharmacology revealed that GQD treatment for T2DM and UC mainly targets immune-inflammatory pathways like Toll-like receptor, IL-17, TNF, MAPK, and PI3K-Akt signaling pathways.

CONCLUSION

This study provides insights into the shared pathogenesis of T2DM and UC and clarifies the regulatory mechanisms of GQD on these conditions. It also proposes novel targets and therapeutic strategies for individuals suffering from T2DM and UC.

摘要

背景

尽管先前的临床研究和动物实验已证明葛根芩连汤(GQD)在治疗2型糖尿病(T2DM)和溃疡性结肠炎(UC)方面的疗效,但其治疗作用的潜在机制仍不清楚。

目的

本研究旨在探讨T2DM和UC之间的共同致病机制,并使用生物信息学方法阐明GQD调节这些疾病的机制。

方法

本研究的数据来自基因表达综合数据库(GEO)。使用中药系统药理学数据库(PharmMapper)和瑞士药物靶点预测数据库(SwissTargetPrediction)确定GQD的靶点,而与T2DM和UC相关的靶点则从药物银行(DrugBank)、基因卡片(GeneCards)、治疗靶点数据库(TTD)、疾病基因数据库(DisGeNET)以及差异表达基因(DEG)中收集。我们的分析包括六种方法:加权基因共表达网络分析(WGCNA)、免疫浸润分析、单细胞测序分析、机器学习、DEG分析和网络药理学。

结果

通过对加权基因共表达网络分析(WGCNA)模块基因和DEG交集的基因本体(GO)和京都基因与基因组百科全书(KEGG)分析,我们发现T2DM和UC的共病主要与免疫炎症途径相关,包括白细胞介素-17(IL-17)、肿瘤坏死因子(TNF)、趋化因子和Toll样受体信号通路。免疫浸润分析支持了这些发现。三项不同的机器学习研究确定胰岛素样生长因子结合蛋白3(IGFBP3)是GQD治疗T2DM的生物标志物,而β-分泌酶2(BACE2)、 Ephrin B4受体(EPHB4)和Ephrin A2受体(EPHA2)是GQD治疗UC的生物标志物。网络药理学显示,GQD治疗T2DM和UC主要靶向Toll样受体、IL-17、TNF、丝裂原活化蛋白激酶(MAPK)和磷脂酰肌醇-3-激酶-蛋白激酶B(PI3K-Akt)信号通路等免疫炎症途径。

结论

本研究为T2DM和UC的共同发病机制提供了见解,并阐明了GQD对这些病症的调节机制。它还为患有T2DM和UC的个体提出了新的靶点和治疗策略。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c4b9/11220276/19956bd187ca/fmed-11-1406149-g001.jpg

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