Abstract
BACKGROUND: Ankylosing spondylitis (AS) is an autoimmune disease characterized by low back stiffness and pain, with no cure and a dearth of therapeutic targets. METHODS: Identifying novel AS targets from a list of 5884 druggable genes using weighted gene coexpression network analysis (WGCNA), machine learning, and Mendelian randomization analysis. Investigating the biological functions of the targets through comprehensive bio-functional analyses; exploring immune-related functions of the targets based on single-cell analyses; developing a reliable AS risk prediction model based on the identified targets and clinical data; conducting target drug prediction and molecular docking based on the Enrichr database and the LeDock software. RESULTS: A novel AS target, diazepam binding inhibitor (DBI), was identified from among 5884 druggable genes. Bio-functional enrichment analysis indicated that this gene plays a key role in AS by modulating lipid metabolism disorders. Furthermore, single-cell analysis revealed that the gene likely influences the onset or progression of AS by impairing the cytotoxic function of CD56(dim) natural killer (NK) cells. Finally, a reliable AS risk prediction model was developed using DBI and clinical data, and several potential therapeutic compounds were identified. CONCLUSION: In this study, a novel therapeutic target for AS was identified using multiple algorithms, and it was found to be involved in lipid metabolism and cytotoxic function of CD56(dim) NK cells. Additionally, a reliable prediction model was developed, and potential therapeutic compounds were identified. In conclusion, this study presents a novel approach for AS treatment.