胞葬相关基因UCP2、EGLN3、IL1B对骨关节炎的诊断价值及中药治疗预测：基于生物信息学及机器学习

doi:10.12122/j.issn.1673-4254.2026.03.23

摘要/Abstract

摘要：

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。

关键词: 骨关节炎, 胞葬作用, 生物信息学, 机器学习, 中药预测

Abstract:

Objective 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). Methods 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. Results 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. Conclusion 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.

Key words: osteoarthritis, efferocytosis, bioinformatics, machine learning, prediction of traditional Chinese medicines

向科霖, 章晓云, 黎征鹏, 徐志为, 刘素杰, 柴源. 胞葬相关基因UCP2、EGLN3、IL1B对骨关节炎的诊断价值及中药治疗预测：基于生物信息学及机器学习[J]. 南方医科大学学报, 2026, 46(3): 693-706.

Kelin XIANG, Xiaoyu ZHANG, Zhengpeng LI, Zhiwei XU, Sujie LIU, Yuan CHAI. Identification of efferocytosis-related genes in osteoarthritis and prediction of traditional Chinese medicines based on bioinformatics and machine learning[J]. Journal of Southern Medical University, 2026, 46(3): 693-706.

图/表 12

表1 GEO数据集的基本信息和分组

Tab.1 Basic information and grouping of GEO datasets

GEO ID	Platform	Control (n)	Case (n)	Group
GSE55235	GPL96	10	10	Training set
GSE55457	GPL96	10	10	Validation set
GSE117999	GPL20844	12	12	Validation set

表2 PCR引物序列

Tab.2 PCR primer sequences

Gene	Primer sequence (5'-3')
UCP2-S	СТСССААТGТТGСССGАААТ
UCP2-A	GAAGTGGCAAGGGAGGTCGT
EGLN3-S	CGCCAAGTTACATGGAGGGG
EGLN3-A	CCAGACAGTCATGGCGTACC
IL1B-S	GAACAACAAAAATGCCTCGTGC
IL1B-A	GACAAACCGCTTTTCCATCTTCT
MMP13-S	TGCATACGAGCATCCATCCC
MMP13-A	CGTGTCCTCAAAGTGAACCGC
COL2A1-S	CAGACAGTACCTTGAGACAGCATGA
COL2A1-A	AGGTGCGAGCGGGATTCTT
GAPDH-S	CTGGAGAAACCTGCCAAGTATG
GAPDH-A	GGTGGAAGAATGGGAGTTGCT

图1 OA患者与健康人群样本间的差异表达基因分析

Fig.1 Differential gene expression analysis between osteoarthritis (OA) patients and healthy controls. A: Gene expression heatmap between OA patients and healthy controls. Red represents up-regulated expression, and blue represents down-regulated expression. B: Volcano plot of differentially expressed genes (DEGs). Black dots indicate genes with no significant differences, red dots indicate significantly up-regulated DEGs, and blue dots indicate significantly down-regulated DEGs. The figure displays efferocytosis-related DEGs. C: Venn diagram of the intersection between DEGs in OA and efferocytosis-related gene sets, obtaining 15 efferocytosis-related DEGs.

图2 骨关节炎差异基因的GO功能和KEGG通路富集分析

Fig.2 GO functional and KEGG pathway enrichment analysis of osteoarthritis DEGs. A: GO enrichment analysis results. B: KEGG pathway enrichment analysis results, displaying the signaling pathways significantly enriched by the DEGs.

图3 机器学习筛选特征基因

Fig.3 Machine learning screening of feature genes. A, B: RF screening process and results, showing trend of model error with increasing number of decision trees (A) and gene importance ranking (B). C, D: LASSO regression screening process and results showing model coefficient path diagram (C) and trend of cross-validation error with penalty coefficient λ (D). E, F: SVM screening process and results showing model error rate with varying number of features (E) and model accuracy with varying number of features (F). G: Venn diagram of the intersection of screening results from three machine learning algorithms, ultimately obtaining 3 consensus feature genes.

表3 基于特征基因的分类模型性能评估

Tab.3 Performance evaluation of classification model based on the feature genes

Dataset	Accuracy	Precision	Recall	F1-Score	AUC
GSE55235	1.000	1.000	1.000	1.000	1.000
GSE55457	0.850	0.818	0.900	0.857	0.900
GSE117999	0.850	0.818	0.900	0.857	0.910

图4 模型性能评估的综合分析

Fig.4 Comprehensive analysis of model performance evaluation. A-C: Performance assessment of the diagnostic model in the training set (GSE55235), showing the accuracy, precision, recall, and F1-score diagram (A), confusion matrix (B), and ROC curve and AUC value (C). D-F: Performance assessment of the diagnostic model in the validation set (GSE55457) showing the accuracy, precision, recall, and F1-score diagram (D), confusion matrix (E), and ROC curve and AUC value (F). G-I: Performance of the diagnostic model in the validation set (GSE117999), showing the accuracy, precision, recall, and F1-score diagram (G), confusion matrix (H), and ROC curve and AUC value (I). J-L: Comparison of expression levels of the 3 feature genes between OA patients and healthy controls (J: UCP2; K: EGLN3; L: IL1B).

图5 关键基因qRT-PCR验证结果

图6 免疫浸润分析图

Fig.6 Immune infiltration analysis. A: Infiltration of immune cells in OA patient samples and healthy control samples, with different colors representing different immune cells. B: Comparison of immune cell infiltration between OA patient samples and healthy control samples. C: Correlation between feature genes and immune cells, with color depth representing the strength of correlation. *P<0.05, ***P<0.001.

图7 特征基因GSEA富集分析结果

Fig.7 GSEA enrichment analysis results of the feature genes. A: GSEA enrichment analysis results of UCP2. B: GSEA enrichment analysis results of EGLN3. C: GSEA enrichment analysis results of IL1B.

表4 中药预测分类表

Tab.4 Classification of predicted traditional Chinese medicines

Category	TCM herbs
Exterior-releasing	Saposhnikovia divaricata, Cinnamomum cassia, Ligusticum sinense, Arctium lappa, Centipeda minima, Peucedanum praeruptorum, Angelica dahurica, Ephedra sinica
Heat-clearing	Rehmannia glutinosa, Sophora flavescens, Scutellaria baicalensis, Coptis chinensis, Forsythia suspensa, Portulaca oleracea, Lonicera japonica, Houttuynia cordata, Lonicera japonica stem, Arnebia euchroma, Anemarrhena asphodeloides, Morus alba leaf, Phellodendron chinense, Prunella vulgaris, Chrysanthemum indicum, Scutellaria barbata, Terminalia chebula
Purgative	Rheum palmatum, Euphorbia lathyris
Wind-damp dispelling	Angelica pubescens, Centella asiatica, Dioscorea nipponica, Sinomenium acutum, Clematis chinensis, Gentiana macrophylla, Morus alba twig, Siegesbeckia orientalis, Stephania tetrandra, Chaenomeles speciosa
Dampness-resolving	Pogostemon cablin
Dampness-draining	Sargassum, Poria cocos, Smilax glabra, Alisma orientale, Akebia quinata
Internal-warming	Fresh Ginger Rhizome, Dried Ginger Rhizome, Aconitum carmichaelii, Evodia rutaecarpa, Cnidium monnieri, Cinnamomum cassia
Qi-regulating	Magnolia officinalis flower, Magnolia officinalis bark, Inula racemosa, Aucklandia lappa, Citrus aurantium
Hemostatic	Artemisia argyi
Blood-activating	Ligusticum chuanxiong, Achyranthes bidentata, Salvia miltiorrhiza, Paeonia lactiflora, Carthamus tinctorius, Panax notoginseng, Curcuma longa, Panax notoginseng flower, Crocus sativus, Sargentodoxa cuneata, Caesalpinia sappan, Cyathula officinalis, Anemone raddeana
Phlegm-resolving	Ginkgo biloba
Tranquilizing	Polygala tenuifolia
Wind-extinguishing	Gastrodia elata
Tonic	Lycium barbarum, Epimedium brevicornum, Panax ginseng leaf, Panax ginseng, Atractylodes macrocephala, Fish maw, Angelica sinensis, Ganoderma lucidum, Rehmannia glutinosa, Red ginseng, Glycyrrhiza uralensis, Cordyceps sinensis, Eucommia ulmoides, Cornus officinalis, Polygonatum sibiricum, Ophiopogon japonicus, Gynostemma pentaphyllum, Coriolus versicolor, Dioscorea opposita, Houttuynia cordata, Hippophae rhamnoides, Bletilla striata, Agrimonia pilosa, Ligustrum lucidum
Astringent	Schisandra chinensis, Rosa laevigata, Punica granatum peel, Ephedra sinica root, Terminalia chebula

表4 中药预测分类表

Tab.4 Classification of predicted traditional Chinese medicines

Category	TCM herbs
Exterior-releasing	Saposhnikovia divaricata, Cinnamomum cassia, Ligusticum sinense, Arctium lappa, Centipeda minima, Peucedanum praeruptorum, Angelica dahurica, Ephedra sinica
Heat-clearing	Rehmannia glutinosa, Sophora flavescens, Scutellaria baicalensis, Coptis chinensis, Forsythia suspensa, Portulaca oleracea, Lonicera japonica, Houttuynia cordata, Lonicera japonica stem, Arnebia euchroma, Anemarrhena asphodeloides, Morus alba leaf, Phellodendron chinense, Prunella vulgaris, Chrysanthemum indicum, Scutellaria barbata, Terminalia chebula
Purgative	Rheum palmatum, Euphorbia lathyris
Wind-damp dispelling	Angelica pubescens, Centella asiatica, Dioscorea nipponica, Sinomenium acutum, Clematis chinensis, Gentiana macrophylla, Morus alba twig, Siegesbeckia orientalis, Stephania tetrandra, Chaenomeles speciosa
Dampness-resolving	Pogostemon cablin
Dampness-draining	Sargassum, Poria cocos, Smilax glabra, Alisma orientale, Akebia quinata
Internal-warming	Fresh Ginger Rhizome, Dried Ginger Rhizome, Aconitum carmichaelii, Evodia rutaecarpa, Cnidium monnieri, Cinnamomum cassia
Qi-regulating	Magnolia officinalis flower, Magnolia officinalis bark, Inula racemosa, Aucklandia lappa, Citrus aurantium
Hemostatic	Artemisia argyi
Blood-activating	Ligusticum chuanxiong, Achyranthes bidentata, Salvia miltiorrhiza, Paeonia lactiflora, Carthamus tinctorius, Panax notoginseng, Curcuma longa, Panax notoginseng flower, Crocus sativus, Sargentodoxa cuneata, Caesalpinia sappan, Cyathula officinalis, Anemone raddeana
Phlegm-resolving	Ginkgo biloba
Tranquilizing	Polygala tenuifolia
Wind-extinguishing	Gastrodia elata
Tonic	Lycium barbarum, Epimedium brevicornum, Panax ginseng leaf, Panax ginseng, Atractylodes macrocephala, Fish maw, Angelica sinensis, Ganoderma lucidum, Rehmannia glutinosa, Red ginseng, Glycyrrhiza uralensis, Cordyceps sinensis, Eucommia ulmoides, Cornus officinalis, Polygonatum sibiricum, Ophiopogon japonicus, Gynostemma pentaphyllum, Coriolus versicolor, Dioscorea opposita, Houttuynia cordata, Hippophae rhamnoides, Bletilla striata, Agrimonia pilosa, Ligustrum lucidum
Astringent	Schisandra chinensis, Rosa laevigata, Punica granatum peel, Ephedra sinica root, Terminalia chebula

图8 中药预测图

Fig.8 Prediction of traditional Chinese medicines. A: Radar chart of four properties frequency. B: Radar chart of five flavors frequency. C: Radar chart of meridian tropism frequency. D: Frequency diagram of efficacy classification.

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