Objective: To identify differentially expressed genes related to the progression of synovial tissue lesions in rheumatoid arthritis (RA) by bioinformatics analysis. Methods: Gene expression profiles of GSE55235 and GSE55457 were acquired from the NCBI GEO database. Data were combined and re-annotated in Perl; Batch correction and identify differentially expressed genes were performed using R; the competing endogenous RNA (ceRNA) network was constructed based on the differential long non-coding RNA (lncRNA) and mRNA. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was performed. The Hub gene was screened using the cytoHubba plugin and analyzed for the correlation between differential lncRNA. Results: Compared with normal synovial tissue, 143 mRNA and 3 LncRNA in synovial tissue of RA patients were significantly different. The LncRNA-miRNA-mRNA interaction network was constructed based on the differential gene. The network consisted of two LncRNA nodes, 16 miRNA nodes, 17 mRNA nodes and 44 edges. GO functional enrichment analysis mainly were significantly enhanced in positive regulation of programmed cell death, regulation of fibroblast proliferation and immune response-regulating cell surface receptor signaling pathway. KEGG pathway analysis showed that pathways associated with IL-17 signaling pathway, MAPK signaling pathway, Wnt signaling pathway, and TNF signaling pathway. Hub gene MYC, CDKN1A, JUN, FOS and LncRNA MEG3 were down-regulated in RA synovial tissue, and LncRNA MEG3 was correlated with the expression of MYC, CDKN1A, JUN and FOS. Conclusion: The bioinformatics network analysis showed that LncRNA MEG3 may play an important role in the development of RA in ceRNA and providing some new candidate diagnostic biomarkers or potential therapeutic targets for RA.