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

doi:10.3969/j.issn.1674-8115.2025.10.006

摘要/Abstract

摘要：

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。

关键词: 吗啡耐受, 背根神经节, 转录组测序, 加权基因共表达网络分析, 差异表达基因, 机器学习

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.

Key words: morphine tolerance, dorsal root ganglia (DRG), RNA sequencing, weighted gene co-expression network analysis (WGCNA), differentially expressed gene (DEG), machine learning

中图分类号:

R614.1

禹志远, 董海平, 高楠, 马柯. 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[J]. 上海交通大学学报（医学版）, 2025, 45(10): 1308-1319.

YU Zhiyuan, DONG Haiping, GAO Nan, MA Ke. 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[J]. Journal of Shanghai Jiao Tong University (Medical Science), 2025, 45(10): 1308-1319.

图/表 8

图1 生物信息学分析流程图

Fig 1 Bioinformatics analysis workflow

图2 不同吗啡用药时长小鼠模型的建立与行为学评估Note： A. Schematic diagram showing the overall experimental workflow. B. Morphine administration protocols for different treatment durations. C. Comparison of MPE% in the tail-flick test among groups. Control (n=6), 6 h (n=5), 4 d (n=6), 10 d (n=5). ①P=0.004, 6 h vs Control group; ②P=0.008, 6 h vs 10 d group (Kolmogorov-Smirnov test). i.p.—intraperitoneal injection.

Fig 2 Establishment and behavioral evaluation of mouse models with different morphine administration durations

图3 WGCNA筛选吗啡耐受关键基因模块Note： A. Scale-free fit index vs mean connectivity. B. Co-expression module identification using dynamic tree-cutting algorithm. C. Module-treatment group correlation heatmap (Red: positive; Blue: negative). D. Intergroup differences in gene expression of the blue module (P=0.026) and black module (P=0.041) (Kruskal-Wallis test).

Fig 3 Identification of key morphine tolerance-associated gene modules by WGCNA

图4 DEGs与WGCNA模块基因的交集分析Note： A. Heatmap of DEGs between morphine treatment groups. Red indicates upregulated genes, and blue indicates downregulated genes. B. Venn diagram intersecting WGCNA module genes with two DEG sets (control vs 6 h, 6 h vs 10 d).

Fig 4 Intersection analysis of morphine tolerance-associated DEGs and WGCNA module genes

图5 候选基因富集分析结果Note： A. GO enrichment analysis of candidate intersection genes. B. KEGG pathway enrichment analysis of candidate intersection genes. BP—biological process; MF—molecular function; CC—cellular component; ECM—extracellular matrix.

Fig 5 GO and KEGG enrichment analysis of candidate genes

图6 PPI网络和枢纽基因筛选Note： A. PPI network of intersection genes. B. UpSet plot for genes screening using eight cytoHubba algorithms, with red boxes indicating qualified hub genes.

Fig 6 PPI network and screening of hub genes

图7 特征基因筛选与整合Note： A. LASSO coefficient profile: the red vertical line indicates the optimal number of genes (n=14) corresponding to the minimum mean squared error (MSE)=0.089, with a model coefficient of determination R²=0.889. B. SVM-RFE model parameter optimization: the lowest root mean squared error (RMSE=0.194) identifies 26 key genes, with a model coefficient of determination R²=0.787. C. RF model parameter optimization: a minimal MSE=0.076 selects 78 candidate genes, with a model coefficient of determination R²=0.931. D. Venn diagram intersecting signature genes from the three algorithms.

Fig 7 Screening and integration of signature genes

图8 枢纽基因与特征基因的表达差异及GSEA富集关联分析Note： A. Box plots showing expression differences of hub and signature genes across treatment groups (comparisons: 6 h vs control or 10 d groups). Significant P values are indicated directly on the plots. B. Heatmap of GSEA enrichment correlations between hallmark gene sets and hub/signature genes: color intensity represents normalized enrichment score (NES). ①P<0.001, ②P<0.01, ③P<0.05.

Fig 8 Intergroup expression differences of hub and signature genes and their GSEA enrichment associations

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