Potential Pathogenic Genes and Mechanism of Ankylosing Spondylitis: A Study Based on WGCNA and Bioinformatics Analysis

Abstract

Objective: The purpose of this study is to explore the high-risk pathogenic driver genes for the occurrence and development of ankylosing spondylitis (AS) based on the bioinformatics method at the molecular level, to further elaborate the molecular mechanism of the pathogenesis of AS, and to provide potential biological targets for the diagnosis and treatment of clinical AS.

Methods: The gene expression profile data GSE16879 were downloaded from the GEO (Gene Expression Omnibus) database, and weighted gene coexpression network analysis was performed. Highly correlated genes were divided into 14 modules, and 582 genes contained in the yellow (classic module) and 59 genes contained in grey60 (hematologic module) modules had the strongest correlation with AS. After protein-protein interaction (PPI) analysis, the top 20 genes with the highest scores were obtained from classic module and hematologic module, respectively. The DAVID (Database for Annotation, Visualization, and Integrated Discovery) database was used for Gene Ontology analysis and Kyoto Encyclopedia of Genes and Genomes analysis to analyze the biological functions of high-risk genes related to AS.

Results: The results showed that the process of signal recognition particle-dependent cotranslational protein targeting to membrane, ribosome, nicotinamide adenine diphosphate hydride dehydrogenase (ubiquinone) activity, platelet activation, integrin complex, and extracellular matrix binding were enriched.

Conclusions: In this study, weighted gene coexpression network analysis, an efficient system biology algorithm, was used to analyze the high-risk pathogenic driver gene of AS. We provide new targets for the diagnosis and treatment of clinical AS and new ideas for further study.

Keywords: Ankylosing spondylitis; Bioinformatics; Mechanism; Pathogenic genes; WGCNA.