目的·开发一种多算法协同的计算生物学策略，构建吗啡耐受外周神经调控网络的预测模型，并筛选高置信度候选靶标。方法·构建不同吗啡用药时长的小鼠模型，采集其背根神经节（dorsal root ganglion，DRG）组织开展批量RNA测序，以表达矩阵为基础构建加权基因共表达网络，用于识别共表达基因模块。随后，通过整合加权基因共表达网络与差异表达基因（differentially expressed genes，DEGs）筛选候选基因，并对候选基因开展基因本体论（Gene Ontology，GO）、京都基因与基因组百科全书（Kyoto Encyclopedia of Genes and Genomes，KEGG）功能富集分析。同时，构建候选基因蛋白质相互作用（protein-protein interaction，PPI）网络并应用cytoHubba算法识别获得枢纽基因。整合最小绝对收缩与选择算子（least absolute shrinkage and selection operator，LASSO）回归、支持向量机递归特征消除（support vector machine recursive feature elimination，SVM-RFE）模型及随机森林（random forest，RF）模型3种机器学习算法筛选获得特征基因。最终通过基因集富集分析（gene set enrichment analysis，GSEA）验证枢纽基因和特征基因的功能特征。结果·加权基因共表达网络分析（weighted gene co-expression network analysis，WGCNA）鉴定出8 297个关键模块基因，结合DEGs筛选获得177个候选基因，功能富集分析显示它们显著参与离子通道调控及血管平滑肌收缩通路的生物学过程。结合PPI网络与3种机器学习算法，最终识别出4个特征基因［肌动蛋白γ2（actin γ2，smooth muscle，Actg2）、中心粒卷曲螺旋蛋白110（centriolar coiled-coil protein 110，Ccp110）、神经细胞黏附分子2（neural cell adhesion molecule 2，Ncam2）、硒结合蛋白1（selenium binding protein 1，Selenbp1）］及6个枢纽基因［肌动蛋白α2（actin α2，smooth muscle，Acta2）、血管性血友病因子（von Willebrand factor，Vwf）、细胞通信网络因子2（cellular communication network factor 2，Ccn2）、整合素β4（integrin β4，Itgb4）、整合素α11（integrin α11，Itga11）、TEK受体酪氨酸激酶（TEK receptor tyrosine kinase，Tek）］。结论·成功构建了多算法协同的吗啡耐受外周神经调控网络预测模型，共筛选出10个高置信度核心基因。

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.