Identify the potential target of efferocytosis in knee osteoarthritis synovial tissue: a bioinformatics and machine learning-based study
IntroductionKnee osteoarthritis (KOA) is a degenerative joint disease characterized by the progressive deterioration of cartilage and synovial inflammation. A critical mechanism in the pathogenesis of KOA is impaired efferocytosis in synovial tissue. The present study aimed to identify and validate...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-02-01
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| Series: | Frontiers in Immunology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fimmu.2025.1550794/full |
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| Summary: | IntroductionKnee osteoarthritis (KOA) is a degenerative joint disease characterized by the progressive deterioration of cartilage and synovial inflammation. A critical mechanism in the pathogenesis of KOA is impaired efferocytosis in synovial tissue. The present study aimed to identify and validate key efferocytosis-related genes (EFRGs) in KOA synovial tissue by using comprehensive bioinformatics and machine learning approaches.MethodsWe integrated three datasets (GSE55235, GSE55457, and GSE12021) from the Gene Expression Omnibus database to screen differentially expressed genes (DEGs) associated with efferocytosis and performed weighted gene co-expression network analysis. Subsequently, we utilized univariate logistic regression analysis, least absolute shrinkage and selection operator regression, support vector machine, and random forest algorithms to further refine these genes. The results were then inputted into multivariate logistic regression analysis to construct a diagnostic nomogram. Public datasets and quantitative real-time PCR experiments were employed for validation. Additionally, immune infiltration analysis was conducted with CIBERSORT using the combined datasets.ResultsAnalysis of the intersection between DEGs and EFRGs identified 12 KOA-related efferocytosis DEGs. Further refinement through machine learning algorithms and multivariate logistic regression revealed UCP2, CX3CR1, and CEBPB as hub genes. Immune infiltration analysis demonstrated significant correlations between immune cell components and the expression levels of these hub genes. Validation using independent datasets and experimental approaches confirmed the robustness of these findings.ConclusionsThis study successfully identified three hub genes (UCP2, CX3CR1, and CEBPB) with significant expression alterations in KOA, demonstrating high diagnostic potential and close associations with impaired efferocytosis. These targets may modulate synovial efferocytosis-related immune processes, offering novel therapeutic avenues for KOA intervention. |
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| ISSN: | 1664-3224 |