Heat shock proteins (HSP) have been associated with a range of persistent inflammatory disorders; however, little research has been conducted on the involvement of HSP in the development of ankylosing spondylitis (AS). The research aims to identify a diagnostic signature based on HSP-related genes and determine the molecular subtypes of AS. We gathered the transcriptional data of patients with AS from the GSE73754 dataset and conducted a literature search for HSP-related genes (HRGs). The logistic regression model was utilized for the identification of hub HRGs associated with AS. Subsequently, these HRGs were employed in the construction of a nomogram prediction model. We employed a consensus clustering approach to identify novel molecular subgroups. Subsequently, we conducted functional analyses, encompassing GO, KEGG, and GSEA, to elucidate the underlying mechanisms between these subgroups. To assess the immunological landscape, we employed the xCell algorithm. Through logistic regression analysis, the four core HRGs (CCT2, HSPA6, DNAJB14, and DNAJC5) were confirmed as potential biomarkers for AS. Subsequent stratification revealed two distinct molecular phenotypes, designated as Cluster 1 and Cluster 2. Notably, Cluster 2 was characterized by the upregulation of pathways pertinent to immune response and inflammation. Our research suggests that the CCT2, HSPA6, DNAJB14, and DNAJC5 exhibit potential as effective blood-based diagnostic biomarkers for AS. These findings contribute to a deeper comprehension of the underlying mechanisms involved in the development of AS and offer potential targets for personalized therapeutic interventions.
Keywords: Ankylosing spondylitis; diagnostic biomarkers; heat shock protein; immune infiltration; subtypes.