Background: The clinical manifestations of psoriatic arthritis (PsA) are highly heterogeneous and no reliable diagnostic biomarkers exist.
Objective: We explored the role of DNA methylation CpG markers in the diagnosis of PsA.
Methods: DNA methylation array was used to screen for differentially methylated sites (DMSs) in the discovery phase (PsA, n = 25; healthy controls [HCs], n = 19; psoriasis vulgaris [PsV], n = 20). In the validation phase, pyrosequencing was used to identify the DMSs in an expanded cohort (PsA, n = 60; HCs, n = 91; PsV, n = 48; rheumatoid arthritis [RA], n = 60). Logistic regression prediction models were established based on the identified DMSs for the diagnosis of PsA.
Results: A total of 17 DMSs differentiating PsA and HCs as well as 11 DMSs differentiating PsA and PsV were screened in the discovery phase. A total of six DMSs (chr14: cg07940072, chr14: 38061320, chr9: cg15734589, chr6: cg12800266, chr3: cg12992827, chr6: cg24500972) differentiating PsA and HCs and two DMSs (chr12: cg16459382, chr2: cg16348668) differentiating PsA and PsV were identified using pyrosequencing. Three logistic regression prediction models were established based on the identified DMSs, which distinguished PsA, RA, PsV, and HCs (P < 0.001). The models performed well in differentiating PsA from HCs, RA, and PsV (AUC: 0.858, 0.851, and 0.976, respectively).
Conclusions: The models based on methylated CpG sites are useful for distinguishing patients with PsA from HCs and those with RA or PsV and are a highly sensitive and specific diagnostic biomarker for PsA.
Keywords: Biomarkers; CpG methylation; Psoriasis; Psoriatic arthritis.
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