Abstract: Objective To identify candidate genes associated with ankylosing spondylitis (AS) through bioinformatics analysis, so as to provide potential diagnostic markers and therapeutic targets for AS. Methods The original data sets of GSE25101, GSE41038 and GSE73754 were downloaded from Gene Expression Omnibus (GEO). Three groups of microarrays were used to screen out the differentially expressed genes (DEGs) between AS and normal samples. Then DEGs' Gene Ontology (GO) functional annotation, Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathway enrichment analysis and KEGG disease enrichment analysis were carried out, and the real core pathway was determined through intersection of the three enrichment results. The PPI network was constructed to screen out the core genes respectively; the intersection of the three core genes was verified to obtain the real key genes. Results The results of GO enrichment were mainly related to the immune process of T cells. The results of KEGG enrichment were mainly related to cancer pathway, metabolic pathway, the PI3K-Akt signaling pathway, regulation of actin cytoskeleton, Th17 cell differentiation and Th1 and Th2 cell differentiation. KEGG disease enrichment results showed that differential genes were mainly involved in immune system diseases, metabolic diseases and skeletal muscle diseases. According to PPI core gene screening, TP53 presented the highest correlation degree and the intersection of three chips was performed to get RUNX3, IL2RB and RPL17 as the key genes. Conclusions Immune and metabolic abnormalities play an important role in the pathogenesis of AS. The PI3K-Akt signaling pathway, Th17 cell differentiation and Th1 and Th2 cell differentiation may be important signaling pathways during the development of AS. TP53, RUNX3, IL2RB and RPL17 may be potential key genes and biomarkers of AS.