AB0058 SERUM ANTIGENOME PROFILING REVEALS DIAGNOSTIC MODELS FOR RHEUMATOID ARTHRITIS

Abstract

BackgroundRheumatoid arthritis (RA) is a chronic autoimmune disease that leads to joint damage, systemic inflammation and early mortality. Though the precise molecular mechanism in the triggering immune response are not fully understood, the emergence of antibodies against self-antigens can serve as diagnostic biomarker. Multiple antigens have been confirmed. However, the profiling of serum antigen, antigenome, remains poorly known.ObjectivesThe study aimed to investigate the serum antigenomic profiling and determine potential diagnostic biomarkers using label-free proteomic technology implemented with machine-learning algorithm.MethodsWe captured serum antigens from a cohort consisting of 60 RA patients (45 ACPA-positive RA patients and 15 ACPA-negative RA patients), sex- and age-matched 30 osteoarthritis patients and 30 healthy controls. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) was performed. We then trained a machine learning model to classify RA, ACPA-positive RA and ACPA-negative RA based on proteomic data and validated in the cohort.ResultsWe identified 62, 71 and 49 differentially expressed proteins (DEPs) in RA, ACPA-positive RA and ACPA-negative RA respectively, compared to OA and healthy controls. Among these DEPs, the pathway enrichment analysis and protein-protein interactions networks were conducted. Three panels were constructed to classify RA, ACPA-positive RA and ACPA-negative RA using random forest models algorithm based on the molecular signature of DEPs, whose area under curve (AUC) were calculated as 0.9949 (95% CI = 0.9792-1), 0.9913 (95%CI = 0.9653-1) and 1.0 (95% CI = 1-1).ConclusionThis study presented serum antigen profiling of RA. Among them, three panels of antigens were identified to classify RA, ACPA-positive and ACPA-negative RA patients as diagnostic biomarkers.