Shared gene signatures and molecular mechanisms link ankylosing spondylitis and rheumatoid arthritis.

Ankylosing spondylitis (AS) and rheumatoid arthritis (RA) are closely related autoimmune diseases with shared mechanisms that remain unclear. This study aims to identify shared molecular signatures and hub genes underlying the co-occurrence of AS and RA using clinical and transcriptomic data, focusing on immune dysregulation pathways. The CBC data of 23,289 patients were collected, and six machine learning algorithms were applied to develop disease prediction models for AS and RA. Using permutation feature importance and Shapley Additive Explanations (SHAP) based on the optimal model, the top 10 features most influential for AS and RA prediction were identified, followed by selecting their intersections. Bioinformatics analysis was conducted to identify key immune cells associated with AS and RA and to evaluate the correlation between these immune cells and the hub gene. Clinical data, hematoxylin-eosin (H&E) staining, and immunohistochemical analysis were used to validate the findings. Neutrophils and lymphocytes emerged as key predictors in AS and RA models. Bioinformatics identified MYO1F as a hub gene, significantly upregulated in both diseases, with a strong correlation to neutrophil infiltration (p < 0.05). Clinical data, H&E staining of histological sections of interspinous ligaments from AS patients and synovial tissue from RA patients, and immunohistochemistry confirmed elevated neutrophil counts and MYO1F expression (p < 0.05), supporting their roles in immune dysregulation. This study is the first to identify MYO1F as a hub gene in AS and RA co-occurrence, highlighting neutrophil infiltration as a critical factor in their pathogenesis. Our integrative approach combining machine learning, transcriptomics, and clinical validation, provides novel insights into shared mechanisms, positioning MYO1F and neutrophils as potential diagnostic and therapeutic targets.