To screen key genes related to efferocytosis in osteoarthritis (OA) based on bioinformatics and machine learning methods, and explore their diagnostic value, immune microenvironment characteristics, and potential therapeutic targets of traditional Chinese medicines (TCM). OA-related datasets GSE55235, GSE55457, and GSE117999 were obtained from the GEO database. An efferocytosis-related gene set was retrieved from GeneCards. Differential expression analysis was performed to identify OA-related differentially expressed genes (DEGs) and their intersection with efferocytosis-related genes, followed by GO and KEGG enrichment analyses. Three machine learning algorithms (Random Forest, LASSO regression, and SVM) were used to screen feature genes, and their diagnostic efficacy was evaluated using ROC curves. qRT-PCR was used to validate the feature gene expressions in a rat OA model. Immune cell infiltration was analyzed using CIBERSORT, GSEA was used to explore the related pathways, and the Coremine database was utilized to predict TCMs associated with the feature genes. A total of 959 OA-related DEGs were identified, including 15 efferocytosis-related genes, which were enriched in leukocyte migration, extracellular matrix, and inflammatory pathways. Machine learning identified 3 feature genes, namely UCP2, EGLN3, and IL1B, which showed good diagnostic performance in both the training (GSE55235) and validation sets (GSE55457 and GSE117999) and varying expression patterns in the mouse models. Immune infiltration analysis showed significant differences in resting mast cells, resting memory CD4⁺ T cells, and activated mast cells between OA patients and healthy controls. The feature genes were closely associated with the adipocytokine signaling pathway, sulfur metabolism, and spliceosome pathway. A total of 100 TCMs were predicted, which were primarily herbs for tonifying deficiency, clearing heat, and promoting blood circulation, such as Lycium barbarum, Epimedium brevicornu, Rehmannia glutinosa, Sophora flavescens, Ligusticum chuanxiong, and Achyranthes bidentata. Efferocytosis-related genes play important roles in OA pathogenesis. UCP2, EGLN3, and IL1B have diagnostic value for OA. The predicted TCMs may serve as potential agents for OA prevention and treatment. 目的: 基于生物信息学与机器学习法，筛选骨关节炎中与胞葬作用相关的关键基因，探讨其诊断价值、免疫微环境特征及潜在中药治疗靶点。方法: 从GEO数据库获取骨关节炎数据集GSE55235、GSE55457和GSE117999，其中GSE55235作为训练集，GSE55457和GSE117999作为验证集。从GeneCards数据库获取胞葬相关基因集。通过差异表达分析筛选骨关节炎差异基因，并与胞葬基因取交集获得胞葬相关差异基因。对差异基因进行GO和KEGG富集分析。采用随机森林、LASSO回归和SVM三种机器学习算法筛选特征基因，并通过ROC曲线评估其诊断效能。通过qRT-PCR实验在大鼠骨关节炎模型中验证特征基因表达，利用CIBERSORT解析免疫细胞浸润情况，采用GSEA分析特征基因相关通路，运用Coremine数据库预测与特征基因相关的中药。结果: 共筛选出959个OA差异基因，其中15个与胞葬作用相关。GO和KEGG分析显示这些基因主要富集于白细胞迁移、细胞外基质、炎症通路等。机器学习筛选出UCP2、EGLN3和IL1B三个特征基因，其在训练集和验证集中均表现出良好的诊断能力。qRT-PCR显示部分特征基因表达趋势存在差异（P<0.05）。免疫浸润分析显示静止肥大细胞、休眠记忆CD4+ T细胞和活化的肥大细胞在骨关节炎患者和健康人群的浸润差异显著（P<0.05）。GSEA提示特征基因与脂肪细胞因子信号通路、硫代谢和剪接体通路密切相关。中药预测获得100味中药，以补虚、清热、活血化瘀类为主，包括枸杞子、淫羊藿、生地黄、苦参、川芎、牛膝等。结论: 胞葬相关基因在骨关节炎发病中起重要作用，UCP2、EGLN3、IL1B基因对骨关节炎具有诊断价值，预测出的枸杞子、淫羊藿、生地黄、苦参、川芎、牛膝等中药可能是防治骨关节炎的潜在药物。.
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