Longitudinal Autoantibody and Proteomic Profiles Uncover Biomarkers and Mechanisms of Disease Progression in Systemic Sclerosis

To evaluate dynamic changes in autoantibody and proteomic profiles in treatment-naïve systemic sclerosis (SSc) patients and identify biomarkers and mechanisms associated with disease progression. Serum samples from 30 baseline and 49 follow-up SSc patients, along with 38 controls, were analyzed. Autoantibody profiles were assessed using an autoantigen microarray targeting 120 autoantibodies, while proteomic analysis was conducted via liquid chromatography-mass spectrometry in data-independent acquisition mode. Cox proportional hazards models were used to assess associations with disease progression, and Spearman's correlation was applied to analyze antibody-protein interactions. Baseline autoantibody profiling identified two patient subgroups: high-expression (SSc_high) and low-expression (SSc_low), though their clinical characteristics were similar. Longitudinal analysis revealed that 88.9% of patients with rising autoantibody titers experienced disease progression, while 60% of those with decreasing titers showed clinical improvement. Notably, increased CENP-B titers (HR = 5.036) were strongly linked to disease progression. Proteomic analysis identified 112 proteins involved in tissue repair and immune modulation in patients with declining titers and clinical improvement, whereas 46 proteins linked to lipid metabolism and oxidative stress were enriched in those with rising titers and disease progression. SERPINA10 emerged as the strongest risk factor for progression, while ENTPD5 was most protective. Antibody-protein correlations highlighted key molecular interactions, including SERPINE1 and muscarinic receptor antibodies, were identified. Dynamic changes in autoantibody titers, rather than baseline levels, were more strongly associated with SSc trajectory. Proteomic signatures suggest lipid metabolism and oxidative stress as potential drivers, highlighting novel biomarkers and therapeutic targets for personalized SSc management.