PDF(Pubmed)
Abstract：
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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：
■虽然之前的临床研究和动物实验已经证明葛根芩连汤(GQD)治疗2型糖尿病(T2DM)和溃疡性结肠炎(UC)的疗效,其治疗效果的潜在机制仍然难以捉摸。
■本研究旨在研究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。
本研究的数据来源于基因表达综合(GEO)数据库。使用PharmMapper和SwissTargetPrediction确定GQD的目标，虽然与T2DM和UC相关的目标是从DrugBank汇编的，GeneCards,治疗目标数据库（TTD），DisGeNET数据库,和差异表达基因(DEGs)。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。
■通过GO和KEGG分析加权基因共表达网络分析（WGCNA）模块化基因和DEG交集，我们发现T2DM和UC的合并症主要与免疫炎症通路相关,包括IL-17，TNF，趋化因子,和toll样受体信号通路。免疫浸润分析支持这些发现。三项不同的机器学习研究将IGFBP3确定为治疗T2DM的GQD生物标志物。而BACE2，EPHB4和EPHA2在UC治疗中作为GQD的生物标志物出现。网络药理学显示，T2DM和UC的GQD治疗主要靶向免疫炎症通路，如Toll样受体，IL-17,TNF,MAPK,和PI3K-Akt信号通路。
■这项研究提供了对T2DM和UC共同发病机制的见解，并阐明了GQD对这些疾病的调节机制。它还为患有T2DM和UC的个体提出了新的靶标和治疗策略。
■本研究旨在研究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。
本研究的数据来源于基因表达综合(GEO)数据库。使用PharmMapper和SwissTargetPrediction确定GQD的目标，虽然与T2DM和UC相关的目标是从DrugBank汇编的，GeneCards,治疗目标数据库（TTD），DisGeNET数据库,和差异表达基因(DEGs)。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。
■通过GO和KEGG分析加权基因共表达网络分析（WGCNA）模块化基因和DEG交集，我们发现T2DM和UC的合并症主要与免疫炎症通路相关,包括IL-17，TNF，趋化因子,和toll样受体信号通路。免疫浸润分析支持这些发现。三项不同的机器学习研究将IGFBP3确定为治疗T2DM的GQD生物标志物。而BACE2，EPHB4和EPHA2在UC治疗中作为GQD的生物标志物出现。网络药理学显示，T2DM和UC的GQD治疗主要靶向免疫炎症通路，如Toll样受体，IL-17,TNF,MAPK,和PI3K-Akt信号通路。
■这项研究提供了对T2DM和UC共同发病机制的见解，并阐明了GQD对这些疾病的调节机制。它还为患有T2DM和UC的个体提出了新的靶标和治疗策略。
