Serum Antigenome Profiling Reveals Diagnostic Models for Rheumatoid Arthritis - Han, Peng; Hou, Chao; Zheng, Xi; Cao, Lulu; Shi, Xiaomeng; Zhang, Xiaohui; Ye, Hua; Pan, Hudan; Liu, Liang; Li, Tingting; Hu, Fanlei; Li, Zhanguo (Frontiers in Immunology, 2022)
Objective The study aimed to investigate the serum antigenomic profiling in rheumatoid arthritis (RA) and determine potential diagnostic biomarkers using label - free proteomic technology implemented with machine-learning algorithm. Method Serum antigens were captured from a cohort consisting of 60 RA patients (45 ACPA-positive RA patients and 15 ACPA-negative RA patients), together with sex- and age-matched 30 osteoarthritis (OA) patients and 30 healthy controls. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) was then performed. The significantly upregulated and downregulated proteins with fold change > 1.5 (p < 0.05) were selected. Based on these differentially expressed proteins (DEPs), a machine learning model was trained and validated to classify RA, ACPA-positive RA, and ACPA-negative RA. Results We identified 62, 71, and 49 DEPs in RA, ACPA-positive RA, and ACPA-negative RA, respectively, as compared to OA and healthy controls. Typical pathway enrichment and protein-protein interaction networks were shown among these DEPs. 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). Conclusion This study illustrated the serum auto - antigen profiling of RA. Among them, three panels of antigens were identified as diagnostic biomarkers to classify RA, ACPA-positive, and ACPA-negative RA patients, respectively.