Osteoarthritis (OA) is a severe disease and has brought a massive burden to people's daily life. This study was performed to explore the hub genes associated with OA and predict potential biomarkers for OA. The cartilage (GSE114007) and synovial (GSE55235 and GSE55457) datasets downloaded from the Gene Expression Omnibus (GEO) database were used to screen the differentially expressed genes in OA compared with normal tissues. Then, based on weighted gene co-expression network analysis, the intersection genes between cartilage and synovium data were screened. The protein-protein interaction network and receiver operating characteristic (ROC) curve analysis were utilized to identify the OA-related hub genes. The gene ontology (GO), kyoto encyclopedia of genes and genomes (KEGG), and gene set variation analysis (GSVA) databases were used to explore the potential molecular mechanism of genes. Single-sample gene set enrichment analysis was performed to analyze the immune infiltration levels in OA synovium and cartilage tissues, respectively. A total of 131 intersection genes were screened. These genes were mainly enriched in the osteoclast differentiation and PI3K-Akt signaling pathways. Then, eight OA-related hub genes were further identified, including JUN, MYC, VEGFA, ATF3, NR4A1, BTG2, DUSP1, and JUNB. ROC curve analysis showed the area under the curve of these eight genes was > 0.6 in another OA dataset, suggesting their feasible capacity for predicting OA. Finally, we found that NR4A1 and BTG2 might be involved in multiple inflammatory responses of OA tissues. Our study identified some OA-related hub genes and revealed novel insights into the biological mechanism of OA, which provided a theoretical foundation for further experimental study.