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

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.

Keywords: Gegen Qinlian Decoction; bioinformatics; network pharmacology; traditional Chinese medicine; type 2 diabetes; ulcerative colitis.

Figure 1

Workflow diagram illustrating the research strategy, encompassing five main components: database preparation, exploration of co-morbidity mechanisms in T2DM and UC, biomarker prediction for GQD treatment, and network pharmacology along with molecular docking analyses.

Figure 2

Weighted gene co-expression networks. (A) Scale independence and average connectivity in GSE20966. (B) Scale independence and average connectivity in GSE75214. (C) Different modules obtained from GSE20966 are displayed in various colors, aggregating genes of high relevance within each module. (D) Correlation analysis between modules and T2DM. (E) Different modules obtained from GSE75214 are displayed in various colors, aggregating genes of high relevance within each module. (F) Correlation analysis between modules and UC. (G) Biological process analysis of T2DM and UC module intercourse genes.

Figure 3

Acquisition of DEGs in T2DM and UC and KEGG enrichment analysis of genes intersecting both DEGs. (A) Volcano plot depicting the DEGs associated with T2DM (GSE25724). (B) Heatmap illustrating the DEGs associated with T2DM (GSE25724). (C) Volcano plot showing the DEGs associated with UC (GSE48958). (D) Heatmap displaying the DEGs associated with UC (GSE48958). (E) Chemokine signaling pathway. (F) Toll-like receptor signaling pathway. (G) IL-17 signaling pathway. (H) TNF signaling pathway. DEGs, Differentially Expressed Genes.

Figure 4

Immune infiltration analysis. (A) Boxplots for T2DM immune infiltration analysis. (B) Bar graph for T2DM immune infiltration analysis. (C) Boxplots for UC immune infiltration analysis. (D) Bar graph for UC immune infiltration analysis (*p < 0.05, **p < 0.01, ***p < 0.001).

Figure 5

Predictive Biomarkers for GQD Treatment. (A) SVM-REF analysis of T2DM. (B) LASSO analysis of T2DM. (C,D) Random Forest analysis of T2DM. (E) SVM-REF analysis of UC. (F) LASSO analysis of UC. (G,H) Random Forest analysis of UC. (I) Nomograms of UC marker genes. (J–L) ROC curves for UC marker genes. (M) Nomograms of T2DM marker genes. (N) ROC curves for T2DM marker genes.

Figure 5

Predictive Biomarkers for GQD Treatment. (A) SVM-REF analysis of T2DM. (B) LASSO analysis of T2DM. (C,D) Random Forest analysis of T2DM. (E) SVM-REF analysis of UC. (F) LASSO analysis of UC. (G,H) Random Forest analysis of UC. (I) Nomograms of UC marker genes. (J–L) ROC curves for UC marker genes. (M) Nomograms of T2DM marker genes. (N) ROC curves for T2DM marker genes.

Figure 6

Protein-Protein interaction (PPI) network. (A) Analysis results of PPI network. (B) Betweenness centrality. (C) Closeness centrality. (D) Degree centrality. (E) Neighborhood Component Analysis. (F) MCODE plugin cluster analysis.

Figure 7

GO, KEGG enrichment analysis. (A) Biological process. (B) Cellular composition. (C) Molecular function. (D) Bubble plots of the first 30 pathways analyzed by KEGG enrichment. (E) KEGG enrichment analysis of immune-related pathways.

Figure 8

Molecular docking results. (A) Drug-constituent-target network diagram (the larger the value of degree in the diagram, the larger the node). (B) Heat map of molecular docking (kcal/mol). Berlambine – TNF (C), Berlambine – BCL2 (D), Berlambine – PTGS2 (E), Palmatine – TNF (F), Berlambine – EGFR (G).

Figure 9

Single-cell sequencing analysis. (A) Cellular subtypes of T2DM. (B) Cellular subtypes of UC. (C) GQD expression in various cell clusters of T2DM. (D) GQD expression in various cell clusters of UC. (E,F) Distribution of the seven core targets in cell clusters of T2DM and UC.