Abstract:[Objective] To utilize bioinformatics methods to screen for differentially expressed genes (DEGs) associated with ankylosingspondylitis (AS) in the Gene Expression Omnibus database (GEO), aiming to provide new biological targeting strategies for the clinical diag-nosis and treatment of AS. [Methods] Datasets GSE41038 and GSE39340 were downloaded from the GEO database. After data processing,DEGs related to AS were selected. Functional enrichment and pathway analyses were then performed on these DEGs. Subsequently, the pro-tein-protein interaction (PPI) network of the identified genes was constructed using the online database (Search Tool for Retrieval of Interact-ing Genes, STRING) and visualized using Cytoscape software. [Results] A total of 433 DEGs were identified between AS patients andhealthy individuals, with 276 upregulated and 157 downregulated. GO analysis revealed that these DEGs were mainly involved in positiveregulation of T cell activation and collagen-containing extracellular matrix. KEGG enrichment results were primarily associated with NFkappaB signaling pathway and TNF signaling pathway. Using the STRING database, a protein interaction network was constructed, with Cy-toscape identified the top 10 genes with the highest connectivity, including CASP3, CD36, CXCR4, EGFR, FGF10, IL-1β, MMP1, MMP3,SELL and TLR2. [Conclusion] In this study, the potential mechanism of AS was analyzed by bioinformatics method, and 10 important mole-cules were screened, which may be the potential key genes and biological markers of AS.