ArticleFrontiers in medicine2024
Deciphering the shared mechanisms of Gegen Qinlian Decoction in treating type 2 diabetes and ulcerative colitis via bioinformatics and machine learning.
Article in Frontiers in medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- TENTACLES: a consensus machine learning tool for robust biomarker discovery in heterogeneous data.BioData mining · 2026Article
- Synergistic Mechanistic Insights into Anti-T2DM Benefits ofFoods (Basel, Switzerland) · 2026Article
- Sleep habits, brain functional networks, and type 2 diabetes risk: cohort studies from North American and Asian populations and mediating Mendelian randomization analyses.Diabetology & metabolic syndrome · 2025Article
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8 authors.
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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.
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