Abeer A Abdelati, Rehab A Elnemr, Noha S Kandil, Fatma I Dwedar, Rasha A Ghazala
{"title":"Serum Peptidomic Profile as a Novel Biomarker for Rheumatoid Arthritis.","authors":"Abeer A Abdelati, Rehab A Elnemr, Noha S Kandil, Fatma I Dwedar, Rasha A Ghazala","doi":"10.1155/2020/6069484","DOIUrl":null,"url":null,"abstract":"<p><p>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 (<i>p</i> < 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.</p>","PeriodicalId":51715,"journal":{"name":"International Journal of Rheumatology","volume":"2020 ","pages":"6069484"},"PeriodicalIF":2.3000,"publicationDate":"2020-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1155/2020/6069484","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Rheumatology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1155/2020/6069484","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2020/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"RHEUMATOLOGY","Score":null,"Total":0}
引用次数: 2
Abstract
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 (p < 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.
在过去的几十年里,除了目前的血清学生物标志物之外,人们越来越需要发现新的RA诊断生物标志物,以帮助早期诊断和治疗反应。本研究的目的是利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)患者的血清肽谱。研究纳入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)和35例健康对照(HC)。所有受试者均采用磁珠(MB)分离(MALDI-TOF-MS)进行血清肽谱分析。试验结果显示,区分RA与OA的峰有113个,区分RA与HC的峰有101个。鉴定出95个峰,并将OA与HC区分开来;38例差异有统计学意义(p < 0.05), 57例差异无统计学意义。遗传算法(GA)模型在三个试验(RA vs HC, OA vs HC, RA vs OA)中表现出最佳的敏感性和特异性。目前的数据表明,肽组学模式是有价值的区分个体RA与OA和健康对照。我们认为MALDI-TOF-MS联合MB是一种有效的鉴定与RA相关的新型血清蛋白生物标志物的技术。