Objective: Osteoarthritis (OA) is a chronic degenerative disease of the joints, adversely affecting the quality of life for the patients. To better understand the mechanisms underlying the pathological changes in osteoarthritis and identify the key genes associated with osteoarthritis pathogenesis, we utilized a comprehensive bioinformatics approach to analyze the transcriptome between osteoarthritis synovial and control samples with public microarray datasets.
Materials and methods: First, the GSE82107 microarray dataset containing ten osteoarthritis synovial and seven control samples were selected from the Gene Expression Omnibus (GEO) database.
Results: A total of 52 overlapped differentially expressed genes (DEGs) in GSE82107 and OA-associated genes in the Comparative Toxicogenomics Database were identified. These OA-associated DEGs were further incorporated into a protein-protein interaction (PPI) network. Gene proopiomelanocortin (POMC) was identified in the largest cluster of PPI network with Cytoscape. GO and KEGG analyses suggested that these genes were associated with multiple functions. Other GEO datasets of osteoarthritis synovial tissues, including GSE55235 and GSE55457, were used to validate the expression level of POMC. Quantitative polymerase chain reaction and western blot analyses were also used to test the expression levels of POMC in our osteoarthritis samples. We found POMC was positively associated with transporter complex, ion channel activity, and G protein-coupled receptor signaling pathway.
Conclusions: This study highlighted the OA-associated gene POMC, and its related biological pathways, suggesting it served as a potential treatment target in osteoarthritis.