Establishing Classification Tree Models in Rheumatoid Arthritis Using Combination of Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry and Magnetic Beads - Ma, Dan; Liang, Nana; Zhang, Liyun (Frontiers in Medicine, 2021)
Background: There is no simple method for early diagnosis and evaluation of rheumatoid arthritis (RA). This study aimed to determine potential biomarkers and establish diagnostic patterns for RA using proteomic fingerprint technology combined with magnetic beads. Methods: The serum protein profiles of 97 RA patients and 76 healthy controls (HCs) were analyzed by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) with weak cationic exchange (WCX) magnetic beads. Samples were randomly divided into training (83 RA patients and 56 HCs) and test sets (14 RA patients and 20 HCs). Patients were classified according to their Disease Activity Score: in remission (n=28) and with low (n=17), moderate (n=21), and high disease activity (n=31). Eleven patients were treated with etanercept only for half year, after which their serum protein profiles were detected. The proteomic pattern was identified by Biomarker Patterns Software, which also constructed and evaluated the biomarker models. Results: The diagnostic pattern with four potential protein biomarkers, mass-to-charge (m/z) 3448.85, 4716.71, 8214.29, and 10,645.10, could accurately recognize RA patients from HCs (specificity, 91.57%; sensitivity, 92.86%). All 14 RA patients and 20 HCs in the test set were correctly classified by this model (sensitivity, 95%; specificity, 100%). The diagnostic models could effectively discriminate between RA alone and RA with complications (with interstitial lung disease: m/z 10,645.10 and 12,595.86; with secondary Sjögren’s syndrome: m/z 6635.62 and 33,897.72; with osteonecrosis of the femoral head: m/z 2071.689) and assess disease activity. The classification model, including m/z 1130.776, 1501.065, 2091.198, and 11,381.87, could distinguish between RA patients with disease activity and those in remission. RA with low disease activity could be efficiently discriminated from other disease activity patients by specific protein biomarkers (m/z 2032.31, 2506.214, and Z9286.495). Two biomarkers (m/z 2032.31 and 4716.71) were applied to build the classification model for RA patients with moderate and high disease activities. Biological markers for etanercept (m/z 2671.604064, 5801.840579, 8130.195641, and 9286.49499) were observed between the responder (n=7) and nonresponder groups (n=4) (p<0.05). Conclusion: Using MALDI-TOF-MS combined with WCX magnetic beads might help distinguish between RA and RA with complications as well as assess disease activity