Abdelati Abeer A, Elnemr Rehab A, Kandil Noha S, Dwedar Fatma I, Ghazala Rasha A
Department of Internal Medicine, Rheumatology and Clinical Immunology Unit, Faculty of Medicine, Alexandria University, Alexandria 21612, Egypt.
Department of Physical Medicine Rheumatology and Rehabilitation, Faculty of Medicine, Alexandria University, Alexandria 22511, Egypt.
Int J Rheumatol. 2020 Aug 3;2020:6069484. doi: 10.1155/2020/6069484. eCollection 2020.
Over the last decades, there has been an increasing need to discover new diagnostic RA biomarkers, other than the current serologic biomarkers, which can assist early diagnosis and response to treatment. The purpose of this study was to analyze the serum peptidomic profile in patients with rheumatoid arthritis (RA) by using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS). The study included 35 patients with rheumatoid arthritis (RA), 35 patients with primary osteoarthritis (OA) as the disease control (DC), and 35 healthy controls (HC). All participants were subjected to serum peptidomic profile analysis using magnetic bead (MB) separation (MALDI-TOF-MS). The trial showed 113 peaks that discriminated RA from OA and 101 peaks that discriminated RA from HC. Moreover, 95 peaks were identified and discriminated OA from HC; 38 were significant ( < 0.05) and 57 nonsignificant. The genetic algorithm (GA) model showed the best sensitivity and specificity in the three trials (RA versus HC, OA versus HC, and RA versus OA). The present data suggested that the peptidomic pattern is of value for differentiating individuals with RA from OA and healthy controls. We concluded that MALDI-TOF-MS combined with MB is an effective technique to identify novel serum protein biomarkers related to RA.
在过去几十年中，除了目前的血清学生物标志物外，发现新的类风湿关节炎（RA）诊断生物标志物的需求日益增加，这些生物标志物可协助早期诊断和评估治疗反应。本研究的目的是使用基质辅助激光解吸/电离飞行时间质谱（MALDI-TOF-MS）分析类风湿关节炎（RA）患者的血清肽组学图谱。该研究纳入了35例类风湿关节炎（RA）患者、35例原发性骨关节炎（OA）患者作为疾病对照（DC）以及35例健康对照（HC）。所有参与者均采用磁珠（MB）分离（MALDI-TOF-MS）进行血清肽组学图谱分析。试验显示，有113个峰可区分RA与OA，101个峰可区分RA与HC。此外，鉴定出95个峰可区分OA与HC；其中38个具有显著性（<0.05），57个无显著性。在三项试验（RA与HC、OA与HC以及RA与OA）中，遗传算法（GA）模型显示出最佳的敏感性和特异性。目前的数据表明，肽组学模式对于区分RA患者与OA患者及健康对照具有重要价值。我们得出结论，MALDI-TOF-MS结合MB是一种识别与RA相关的新型血清蛋白生物标志物的有效技术。
