Psoriatic arthritis (PsA) is an immune-mediated chronic inflammatory disease that causes chronic pain, psychological problems, and a significant economic burden, and therefore must be diagnosed and treated early. Existing treatments have limited efficacy and side effects. The study aimed to identify potential drug targets associated with psoriatic arthritis through proteomics and Mendelian randomization (MR) analysis.
Materials and methods
Large-scale genome-wide association studies and proteomics data were used to assess the causal relationship between plasma proteins and PsA through MR analysis, Bayesian colocalization analysis, summary data-based Mendelian randomization (SMR) analysis, and heterogeneity in dependent instruments (HEIDI) test, and to analyze protein-protein interaction networks.
Results
The study identified 26 proteins that may be causally related to PsA, of which 15 were positively correlated and 11 negatively correlated. According to the results of SMR analysis and colocalization analysis, these targets were further analyzed and classified into high, medium, and low confidence levels. High confidence targets include APOF, PRSS27, and DDX58, which were consistently supported by multiple analyses.
Conclusion
The study identified several promising targets for the treatment of psoriatic arthritis through multiple analysis methods, providing a theoretical basis for future treatment strategies, but further experimental verification and clinical research are needed. Key Points • Using large-scale genome-wide association studies and proteomics data, drug targets for psoriatic arthritis (PsA) were identified through Mendelian randomization analysis, Bayesian colocalization analysis, and summary-data-based Mendelian randomization (SMR) analysis. • The study identified 26 proteins that are causally related to psoriatic arthritis, of which 15 are positively and 11 are negatively associated with psoriatic arthritis. • Among the identified proteins, APOF, PRSS27, and DDX58 were ranked as high confidence targets.
