Identification of cuproptosis-related genes related to the progression of ankylosing spondylitis by integrated bioinformatics analysis

Ankylosing spondylitis (AS) is an autoimmune disease, and the relationship between copper death and AS is not clear. The aim of this study was to analyze and identify potential cuprosis-related genes associated with the onset of AS by bioinformatics methods. We obtained the AS gene expression profile GSE25101 from the Gene Expression Omnibus (GEO) database, which consists of blood samples from 16 active AS patients and 16 sex-and age-matched controls. After analyzing the data, we utilized the WGCNA method to identify genes that exhibited significant differential expression. In order to assess the prognostic and predictive power of these genes, we constructed receiver operating characteristic (ROC) curves. To further validate our predictions, we employed nomograms, calibration curves, decision curve analysis, and external datasets. Lastly, we conducted an analysis on immune infiltration and explored the correlation between key genes and immune response. Three genes, namely INPP5E, CYB5R1, and HGD, have been identified through analysis to be associated with AS. The diagnosis of patients using these genes has been found to possess a high level of accuracy. The area under the ROC curve is reported to be 0.816 for INPP5E, 0.879 for CYB5R1, and also 0.879 for HGD. Furthermore, the nomogram demonstrates an excellent predictive power, and it has been calibrated using a Calibration curve. Its clinical usefulness and net benefit have been thoroughly analyzed and estimated through the use of a DCA curve. Moreover, INPP5E, CYB5R1, and HGD are found to be associated with various types of immune cells. In conclusion, the systematic analysis of cuprosis-related genes may aid in the identification of mechanisms related to copper-induced cell death in AS and offer valuable biomarkers for the diagnosis and treatment of AS.