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

Opioid-induced analgesic tolerance remains a major clinical challenge, with most existing research focusing on central nervous system mechanisms. However, the dorsal root ganglion (DRG), a key relay station in peripheral nociceptive transmission, has received limited attention. This study aimed to elucidate the molecular regulatory roles of DRG in morphine tolerance using RNA sequencing and integrative bioinformatics approaches. In order to conduct this research, A murine model of morphine tolerance was established across multiple treatment time points. Bulk RNA sequencing was performed on harvested DRG tissues.  Using the expression matrix as the foundation, a weighted gene co-expression network (WGCNA) was constructed to identify co-expressed gene modules. Candidate genes were subsequently screened through the integration of differentially expressed genes (DEGs) analysis with key WGCNA 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 genes and machine learning-derived signature genes.