Antibodies and upregulated cytokines and chemokines predate the onset of rheumatoid arthritis (RA) symptoms. We aimed to identify the pathways related to the early processes leading to RA development, as well as potential novel biomarkers, using multiple protein analyses. A case-control study was conducted within the Biobank of northern Sweden. The plasma samples from 118 pre-symptomatic individuals (207 samples; median predating time 4.1 years), 79 early RA patients, and 74 matched controls were analyzed. The levels of 122 unique proteins with an acknowledged relationship to autoimmunity were analyzed using 153 antibodies and a bead-based multiplex system (FlexMap3D; Luminex Corp.). The data were analyzed using multifactorial linear regression model, random forest, and network enrichment analysis (NEA) based on the 10 most significantly differentially expressed proteins for each two-by-two group comparison, using the MSigDB collection of hallmarks. There was a high agreement between the different statistical methods to identify the most significant proteins. The adipogenesis and interferon alpha response hallmarks differentiated pre-symptomatic individuals from controls. These two hallmarks included proteins involved in innate immunity. Between pre-symptomatic individuals and RA patients, three hallmarks were identified as follows: apical junction, epithelial mesenchymal transition, and TGF-β signaling, including proteins suggestive of cell interaction, remodulation, and fibrosis. The adipogenesis and heme metabolism hallmarks differentiated RA patients from controls. We confirm the importance of interferon alpha signaling and lipids in the early phases of RA development. Network enrichment analysis provides a tool for a deeper understanding of molecules involved at different phases of the disease progression.

中文翻译：

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类风湿关节炎发作前后个人的蛋白质谱分析和网络富集分析

抗体和上调的细胞因子和趋化因子早于类风湿关节炎（RA）症状的发作。我们旨在通过多种蛋白质分析来鉴定与导致RA发展的早期过程相关的途径，以及潜在的新型生物标志物。在瑞典北部的生物库内进行了病例对照研究。分析了来自118个有症状患者的血浆样本（207个样本；中位捕食时间为4.1年），79名早期RA患者和74个匹配的对照组。使用153种抗体和基于微珠的多重系统（FlexMap3D; Luminex Corp.）分析了与自身免疫性有已知关系的122种独特蛋白质的水平。使用多因素线性回归模型，随机森林，和网络富集分析（NEA），基于10个最显着差异表达的蛋白质，用于每个二到二组比较，使用MSigDB标志集。在使用不同的统计方法来鉴定最重要的蛋白质之间存在高度共识。脂肪形成和干扰素α反应的标志是有症状的个体与对照组的区别。这两个标志包括与先天免疫有关的蛋白质。在有症状的个体和RA患者之间，确定了以下三个标志：根尖连接，上皮间质转化和TGF-β信号传导，包括提示细胞相互作用，重新调节和纤维化的蛋白质。脂肪形成和血红素新陈代谢标志着RA患者与对照组的区别。我们证实干扰素α信号传导和脂质在RA发展的早期阶段的重要性。网络富集分析为深入了解疾病进展不同阶段所涉及的分子提供了一种工具。