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BACKGROUND: Osteoarthritis is inseparable from synovitis, so it has important clinical significance to explore osteoarthritis pathogenesis based on synovial tissue. OBJECTIVE: To explore the diagnosis and therapeutic targets of osteoarthritis from the perspective of synovium through analyzing the transcriptome data of synovial tissues between patients with osteoarthritis and healthy controls based on bioinformatics methods, as well as to provide follow-up research ideas for osteoarthritis. METHODS: Datasets containing normal and osteoarthritis synovial tissues were screened from Gene Expression Omnibus (GEO) database. GSE55457 and GSE55235 were selected, both of which contained 10 synovial tissue samples of osteoarthritis and 10 synovial tissue samples of healthy controls. GEO2R was utilized to perform differential gene expression analysis of GSE55457 and GSE55235 datasets, and genes with adjusted P values (adj.P) < 0.05 were collected. The online tool Xiantao Academy was used to obtained common up-regulated and down-regulated differentially expressed genes in the two datasets. GO functional annotation and KEGG pathway enrichment analysis for differentially expressed genes were performed. The STRING database was used to perform protein-protein interaction analysis. The results were visualized in Cytoscape software through CytoHubba App by 7 algorithms (BottleNeck, Clossness, Degree, DNNC, EPC, NNC and MCC). The top 10 genes with the highest scores were picked out for each algorithm, and the intersected genes of 7 algorithms were selected as the hub genes of osteoarthritis. RESULTS AND CONCLUSION: 200 common up-regulated differential genes and 124 common down-regulated differential genes were gained from the above two datasets. The results of GO analysis for the 324 differential genes showed that these genes were mainly associated with the DNA-binding transcription factor binding, RNA polymerase II specific DNA-binding transcription factor binding, poly(A) binding, platelets-derived growth factor receptor binding, glucocorticoid receptor binding, etc. KEGG analysis showed that the differential genes were mainly enriched in MAPK signaling pathway, insulin signaling pathway, osteoclast differentiation as well as parathyroid hormone synthesis, secretion and action pathways. Protein-protein interaction network analysis screened two hub genes of osteoarthritis, namely heat shock protein 90α class A member 1 (HSP90AA1) and suppressors of cytokine signaling 3 (SOCS3). These findings confirm that HSP90AA1 and SOCS3 are differentially expressed in synovial tissue of patients with osteoarthritis and healthy subjects, and may serve as surveillance markers and therapeutic target, which provide sound ideas for further study of molecular mechanisms of osteoarthritis. [ABSTRACT FROM AUTHOR]