Explore potential immune-related targets of leeches in the treatment of type 2 diabetes based on network pharmacology and machine learning

Abstract

Introduction: Type 2 diabetes mellitus (T2DM) is a chronic metabolic disorder that poses a significant global health burden due to its profound effects on systemic physiological homeostasis. Without timely intervention, the disease can progress insidiously, leading to multisystem complications such as cardiovascular, renal, and neuropathic pathologies. Consequently, pharmacological intervention becomes crucial in managing the condition. Leeches have been traditionally used in Chinese medicine for their potential to inhibit the progression of T2DM and its associated complications; however, the specific mechanisms underlying their action and target pathways remain poorly understood. The objective of this study was to predict potential therapeutic targets of leeches in the treatment of T2DM.

Methods: We collected active components and targets associated with leeches from four online databases, while disease-related targets were sourced from the GeneCards and OMIM databases. Following this, we performed Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Gene expression data were obtained from the GSE184050 dataset. Important immune cell types were identified through immunoinfiltration analysis in conjunction with single sample enrichment analysis (ssGSEA). Additionally, weighted co-expression network analysis (WGCNA) was utilized to identify significantly associated genes. Finally, we employed LASSO regression, SVM-RFE, XGBoost, and random forest algorithms to further predict potential targets, followed by validation through molecular docking.

Results: Leeches may influence cellular immunity by modulating immune receptor activity, particularly through the activation of RGS10, CAPS2, and OPA1, thereby impacting the pathology of Type 2 Diabetes Mellitus (T2DM).

Discussion: However, it is important to note that our results lack experimental validation; therefore, further research is warranted to substantiate these findings.

Keywords: immune infiltrate; leech; machine learning; network pharmacology; type 2 diabetes.

FIGURE 1

(A) Venn diagram of leeches and T2DM targets; (B) PPI maps of intersection targets. In the PPI picture of the intersection target, the size of the circle represents the correlation, and the color represents the P-value. The smaller the P-value, the darker the color. (C) The network diagram for “Leech - active ingredient - target” illustrates the relationships between different components. In this diagram, the size of each circle indicates the importance of the composition, with larger circles signifying a stronger correlation. The primary active components of leeches include hirudin, geniposide, ursolic acid, and croomionidine, which play key roles in their medicinal properties. (D) Table of degree values of main active ingredients of leech. (E) The GO function analysis histogram visually presents the different categories of gene functions. In this representation, Biological Processes (BP) are marked in dark cyan, Cellular Components (CC) in sienna, and Molecular Functions (MF) in steel blue. The analysis indicates that leeches are significantly involved in the positive regulation of immune processes, humoral immune response, and inflammatory response under Biological Processes. Furthermore, they play a role in Molecular Functions related to immunoreceptor activity and protein serine/threonine kinase activity. (F) Sankey plot by KEGG enrichment analysis. Shows the link between signaling pathways and genes.

FIGURE 2

(A) Heat map of differential analysis. The top 20 genes in the differential analysis were shown. (B) Correlations among the top 20 differentially expressed genes were established. (C) GSEA Enrichment Analysis Diagram. The results indicated that bioengineering primarily influences organic morphogenesis, organ development, and the skeletal system. The KEGG pathway analysis highlighted that the main focus is on signaling pathways, including neural active ligand-receptor interactions, calcium signaling, and DNA replication. The enrichment results were categorized into different gene clusters based on gene classification. (D) GSEA enriched cell cluster. GSEA enriched cell clusters were categorized into different gene clusters based on gene classification.

FIGURE 3

(A–F) Single Sample enrichment analysis (ssGSEA) of important immune cells. They were activated CD4 cells, activated dendritic cells, central memory CD4 T cells, type 1 T helper cells, and type 2 T helper cells.

FIGURE 4

(A) Expression differences in sample data; (B) represents connectivity graph; (C) sample represents cluster graph; (D) module correlation heat map. The darker the color, the stronger the correlation. MEdarkturquoise module (5e-04) and MElightgreen module (1e-04) had the strongest correlation, both belonging to activated dendritic cells; (E) MM and GS correlation coefficient maps. MEdarkturquoise module are positively correlated (cor = 0.075, p = 0.32); (F) MM and GS coefficients of MElightgreen module were positively correlated (cor = 0.23, p = 5.2e-05).

FIGURE 5

(A) Wynn diagram of drug targets and WGCNA core module targets; Correlation heat map of the top 20 intersection targets in (B, C).

FIGURE 6

(A) LASSO regression model; (B) Random forest algorithm model; (C) Xgboost algorithm model; (D) SVM-REF algorithm model; (E) Wayne diagram of intersection targets obtained by four algorithms.

FIGURE 7

(A) Table of docking scores; (B) CAPS2-ursolic acid molecular docking diagram; (C) RGS10-ursolic acid molecular docking diagram; (D) OPA1-croomionidine molecular docking diagram.