Identification of efferocytosis-related genes in osteoarthritis and prediction of traditional Chinese medicines based on bioinformatics and machine learning

doi:10.12122/j.issn.1673-4254.2026.03.23

Abstract

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

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.

Figures/Tables 12

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

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

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.

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.

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.

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

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).

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.

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.

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

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

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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