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ORIGINAL RESEARCH article
Front. Genet.
Sec. Computational Genomics
Volume 16 - 2025 | doi: 10.3389/fgene.2025.1554622
This article is part of the Research TopicAdvancements in AI for the Analysis and Interpretation of Large-scale Data by Omics TechniquesView all 3 articles
Explore potential immune-related targets of leeches in the treatment of type 2 diabetes based on network pharmacology and machine learning
Provisionally accepted
Tairan Hu1*
Zhaohui Fang2- 1Anhui University of Chinese Medicine, Hefei, China
- 2First Affiliated Hospital of Anhui University of Traditional Chinese Medicine, Hefei, Anhui Province, China
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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. 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.Our findings suggest that 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). However, it is important to note that our results lack experimental validation; therefore, further research is warranted to substantiate these findings.
Keywords: leech, type 2 diabetes, Network Pharmacology, immune infiltrate, machine learning
Received: 13 Jan 2025; Accepted: 25 Mar 2025.
Copyright: © 2025 Hu and Fang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Tairan Hu, Anhui University of Chinese Medicine, Hefei, China
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