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Introduction: We attempted to perform a comprehensive bioinformatics analyses on osteoarthritis (OA) based on the NKT-related genes and explore the clinical related critical genes.
Methods: Differentially expressed genes (DEGs) and NKT-related genes from WGCNA were obtained using the dataset GSE114007, followed by intersection analysis to obtain NKT-related DEGs. Lasso regression, support vector machine and random forest were performed to screen feature genes, followed by verification with ROC curve, and nomogram model. Protein-protein interaction network, gene set enrichment analysis were performed based on the four marker genes. Finally, immune infiltration of 64 types of immune cells was analyzed between OA samples and normal samples. The significance of biomarkers was validated in clinical samples and OA mice models.
Results: A total of four NKT-related biomarker genes (CCNJ, CFI, PREX2 and SMIM13) were identified. These genes were all upregulated in OA samples. CFI exerted promising diagnostic value for OA with AUC of 0.994 in GSE114007 training dataset and 0.98 in validation dataset. A significantly negative correlation between CFI and NKT cells and a significantly positive correlation between CFI and cDC cells were found. All the biomarkers were determined to be upregulated in OA patients by clinical samples. CFI knockdown significantly reduced DC infiltration and inflammation in the knee joints of OA mice models.
Conclusions: CFI has potential value in the pathogenesis of OA and can be used as a candidate biomarker for OA diagnosis and treatment.
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http://dx.doi.org/10.1093/cei/uxaf054 | DOI Listing |
Clin Exp Immunol
September 2025
Orthopedic Center, Sunshine Union Hospital, High-tech Zone, Weifang City, Shandong Province, China.
Introduction: We attempted to perform a comprehensive bioinformatics analyses on osteoarthritis (OA) based on the NKT-related genes and explore the clinical related critical genes.
Methods: Differentially expressed genes (DEGs) and NKT-related genes from WGCNA were obtained using the dataset GSE114007, followed by intersection analysis to obtain NKT-related DEGs. Lasso regression, support vector machine and random forest were performed to screen feature genes, followed by verification with ROC curve, and nomogram model.