Identify the potential target of efferocytosis in knee osteoarthritis synovial tissue: a bioinformatics and machine learning-based study

尚博 牛 Mengmeng Li Jinling Wang Peirui Zhong Xing Wen Fujin Huang Linwei Yin Yang Liao Jun Zhou ^*

• Rehabilitation Medicine Center Hengyang Medical School, The First Affiliated Hospital, University of South China, Hengyang, China

Knee 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. We 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. The intersection of DEGs and EFRGs yielded 12 KOA-related efferocytosis DEGs. 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, and UCP2, CX3CR1, and CEBPB were finally identified as hub genes.These findings were validated through public datasets and quantitative real-time PCR experiments.Additionally, we conducted immune infiltration analysis with CIBERSORT by using the combined datasets and found that immune cell components were highly correlated with changes in the expression levels of the hub genes. Thus, this study successfully identified specific molecular targets involved in the efferocytosis process in KOA synovial tissue, thereby providing potential therapeutic targets for treating KOA.