Identification and mechanistic analysis of core genes associated with morphine tolerance in dorsal root ganglion:an integrative transcriptomics approach using WGCNA and machine learning algorithms
10.3969/j.issn.1674-8115.2025.10.006
- VernacularTitle:背根神经节吗啡耐受核心基因筛选与机制研究:加权基因共表达网络分析和机器学习的转录组学整合策略
- Author:
Zhiyuan YU
1
;
Haiping DONG
;
Nan GAO
;
Ke MA
Author Information
1. 上海交通大学医学院附属新华医院疼痛科,上海 210092
- Publication Type:Journal Article
- Keywords:
morphine tolerance;
dorsal root ganglia(DRG);
RNA sequencing;
weighted gene co-expression network analysis(WGCNA);
differentially expressed gene(DEG);
machine learning
- From:
Journal of Shanghai Jiaotong University(Medical Science)
2025;45(10):1308-1319
- CountryChina
- Language:Chinese
-
Abstract:
Objective·To develop a multi-algorithm collaborative computational biology strategy for constructing a predictive model of the peripheral morphine tolerance network and for screening high-confidence candidate targets.Methods·A murine model of morphine tolerance was established across multiple treatment time points.Bulk RNA sequencing was performed on harvested dorsal root ganglion(DRG)tissues.Using the expression matrix as a basis,a weighted gene co-expression network was constructed to identify co-expressed gene modules.Candidate genes were subsequently screened through the integration of differentially expressed genes(DEGs)with key weighted gene co-expression network modules.These candidates underwent functional annotation via Gene Ontology(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG)enrichment analyses.A protein-protein interaction(PPI)network was established,and hub genes were systematically identified using the cytoHubba algorithm.Three distinct machine learning approaches,least absolute shrinkage and selection operator(LASSO)regression,support vector machine recursive feature elimination(SVM-RFE)model,and random forest(RF)model,were strategically integrated to screen characteristic signature genes.Finally,gene set enrichment analysis(GSEA)was implemented to functionally validate both the hub and signature genes.Results·Weighted gene co-expression network analysis(WGCNA)identified 8 297 key module genes,of which 177 candidate genes overlapped with DEGs.These genes were significantly enriched in biological processes including ion channel regulation and vascular smooth muscle contraction.A combination of PPI network analysis and machine learning revealed four signature genes[actin γ2,smooth muscle(Actg2),centriolar coiled-coil protein 110(Ccp110),neural cell adhesion molecule 2(Ncam2),and selenium binding protein 1(Selenbp1)]and six hub genes[actin α2,smooth muscle(Acta2),von Willebrand factor(Vwf),cellular communication network factor 2(Ccn2),integrin β4(Itgb4),integrin α11(Itga11),and TEK receptor tyrosine kinase(Tek)]closely associated with morphine tolerance.Conclusion·In this study,we successfully constructed a multi-algorithm collaborative peripheral nerve regulation network prediction model for morphine tolerance,and screened out 10 core genes with high confidence.
