Abstract
BACKGROUND: This study aims to identify new therapeutic targets and explore the molecular mechanism of ankylosing spondylitis (AS), a rheumatic immune disease that mainly affects the sacroiliac and spinal joints. Despite extensive research, the exact cause of AS is still unknown. The research team utilized a bioinformatics approach to achieve their objectives. METHODS: The GSE73754 dataset was downloaded from GEO database. Autophagy-related genes (ARGs) were collected from the Human Autophagy-dedicated Database. The limma package was used to screen for differentially expressed genes (DEGs), which were then intersected with the autophagy-related genes (ARGs) to identify differentially expressed autophagy-related genes (DEARGs). Subsequently, the DEARGs associated with AS were subjected to GO-BP and KEGG enrichment analyses using the clusterProfiler package. Core genes were identified using the cytoHubba plug-in of Cytoscape and were validated by clinical blood samples. Additionally, the Cell algorithm was utilized to evaluate the proportion of immune cell infiltration. RESULTS: A total of 29 DEARGs were identified, which were found to be mainly enriched in autophagy, apoptosis, and necroptosis through functional enrichment analysis. Two core genes, HSPA5 and SQSTM1, were confirmed to have diagnostic value in AS. Immune cell infiltration analysis revealed CD8+ T cells, CD8+ T effector memory (Tem), natural killer (NK) cells, T gamma delta (Tgd) cells, and T-helper 1 (Th1) cells as major participants in AS development. Furthermore, HSPA5 expression was significantly correlated with Th1 cells, CD8+ T cells, CD4+ memory cells, and macrophages. CONCLUSION: This study suggested that HSPA5 and SQSTM1 can serve as useful diagnostic biomarkers for AS. These findings lay the foundation for identifying crucial mRNAs in the whole blood of AS patients, which may aid in the development of novel markers for AS.