Abstract
<title>Abstract</title> <p> <bold>Background</bold> Studies have shown that ubiquitination played an important role in the occurrence and development of Osteoarthritis (OA), but the precise underlying mechanism remains elusive. Therefore, this study mainly explored the internal mechanism of action between these two entities, offering novel therapeutic targets and strategies for managing OA. <bold>Methods</bold> GSE152805, GSE55235 and GSE55457 were included in this study. URGs were obtained from previously published literature. DEGs were identified through differential expression analysis. The DE-URGs were obtained by intersecting DEGs and URGs. UR-DEGs were gained through the correlation analysis of DE-URGs and DEGs. Subsequently, these UR-DEGs were further screened by PPI and machine learning to obtain candidate key genes. Candidate key genes with an area under the ROC curve (AUC) greater than 0.7 and consistent expression trends in GSE55235 and GSE55457 were defined as biomarkers. Additionally, enrichment analysis, immune analysis were conducted. Finally, we performed single-cell sequencing analysis to explore the expression of biomarkers at the cellular level. Cells with high expression of biomarkers were defined as key cells, and pseudo-timing analysis were performed for key cells. The expression of biomarkers was clinically validated using RT-qPCR. <bold>Results</bold> A total of 7 DE-URGs were obtained by overlapping 2,822 DEGs and 79 URGs. Through correlation analysis, we identified 321 UR-DEGs, among which 120 exhibited significant interactions with each other. Furthermore, MYC, TLR7, CX3CR1, PER1 and GADD45B were recognized as biomarkers. Enrichment analysis demonstrated that these biomarkers were co-enriched in the TNF-alpha signaling pathway via NF-kappaB. Immune analysis showed a significant up-regulation of plasma cells, regulatory T (Tregs) cells, M0 macrophages and resting mast cells in OA group. Single-cell RNA sequencing analysis revealed higher expression levels of biomarkers in SSF compared to other cell subsets. Pseudo-timing analysis identified 9 states within SSF with C1 exhibiting a higher degree of differentiation. The expression of GADD45B and MYC showed an upward trend across different stages of SSF. Lastly, the expression validation results obtained through RT-qPCR demonstrated that the expression of most biomarkers was consistent with findings from public database. <bold>Conclusion</bold> MYC, TLR7, CX3CR1, PER1 and GADD45B were confirmed to be related to ubiquitination on OA, offering novel therapeutic targets for managing OA. </p>