Predicting the potential ankylosing spondylitis-related genes utilizing bioinformatics approaches.

Given that ankylosing spondylitis (AS) occurs in approximately 5 out of 1,000 adults of European descent and the unclear pathogenesis, the aim of the research was to further predict the molecular mechanism of this disease. The Affymetrix chip data GSE25101 were available from Gene Expression Omnibus database. First of all, differentially expressed genes (DEGs) were identified by Limma package in R. Moreover, DAVID was used to perform gene set enrichment analysis of DEGs. In addition, miRanda, miRDB, miRWalk, RNA22 and TargetScan were applied to predict microRNA-target associations. Meanwhile, STRING 9.0 was utilized to collect protein-protein interactions (PPIs) with confidence score >0.4. Then, the PPI networks for up- and down-regulated genes were constructed, and the clustering analysis was undergone using ClusterONE. Finally, protein-domain enrichment analysis of modules was conducted using DAVID. Total 145 DEGs were identified, including 103 up-regulated and 42 down-regulated genes. These DEGs were significantly enriched in phosphorylation (p = 1.21E-05) and positive regulation of gene expression (p = 1.25E-03). Furthermore, one module was screened out from the up-regulated network, which contained 39 nodes and 205 edges. Moreover, the nodes in the module were significantly enriched in ribosomal protein (RPL17, ribosomal protein L17 and MRPL22, mitochondrial ribosomal protein L22) and proteasome (PSMA6, proteasome subunit, alpha type 6, PSMA4)-related domains. Our findings that might explore the potential pathogenesis of AS and RPL17, MRPL22, PSMA6 and PSMA4 have the potential to be the biomarkers for the disease.