巨细胞动脉炎的血浆蛋白质组分析。

IF 20.3 1区 医学 Q1 RHEUMATOLOGY
Kevin Y Cunningham, Benjamin Hur, Vinod K Gupta, Matthew J Koster, Cornelia M Weyand, David Cuthbertson, Nader A Khalidi, Curry L Koening, Carol A Langford, Carol A McAlear, Paul A Monach, Larry W Moreland, Christian Pagnoux, Rennie L Rhee, Philip Seo, Peter A Merkel, Kenneth J Warrington, Jaeyun Sung
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引用次数: 0

摘要

研究目的本研究旨在确定能将活动性和非活动性巨细胞动脉炎(GCA)与非疾病对照组区分开来的血浆蛋白质组特征。通过全面分析巨细胞性动脉炎患者和对照组的血浆蛋白质组,我们旨在找出(1)可将患者与对照组区分开来的血浆蛋白质,以及(2)与巨细胞性动脉炎疾病活动相关的血浆蛋白质:在一项多机构、前瞻性纵向研究中,我们从 30 位 GCA 患者身上采集了血浆样本:其中一份样本采集于疾病活动期,另一份采集于临床缓解期。同时还收集了 30 名年龄匹配/性别匹配/种族匹配的非疾病对照者的样本。研究采用了一种基于适配体的高通量蛋白质组学检测方法,该方法可检测 7000 多种蛋白质特征,用于生成研究参与者的血浆蛋白质组图谱:结果:在对潜在的混杂因素进行调整后,我们发现活动性 GCA 和对照组之间有 537 种蛋白质含量不同,非活动性 GCA 和对照组之间有 781 种蛋白质含量不同。这些蛋白质表明,每种疾病状态都涉及不同的免疫反应、代谢途径和潜在的新生理过程。此外,我们还发现 16 种蛋白质与活动性 GCA 患者的疾病活动有关。根据血浆蛋白质组图谱训练的随机森林模型能准确地区分活动性和非活动性 GCA 组与对照组(在 10 倍交叉验证中分别为 95.0% 和 98.3%)。然而,仅凭血浆蛋白对同一患者的活动和非活动疾病状态进行区分的能力有限:对 GCA 血浆蛋白质组的综合分析表明,血液蛋白质特征与机器学习相结合,有望发现 GCA 的多重生物标记物。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Plasma proteome profiling in giant cell arteritis.

Objectives: This study aimed to identify plasma proteomic signatures that differentiate active and inactive giant cell arteritis (GCA) from non-disease controls. By comprehensively profiling the plasma proteome of both patients with GCA and controls, we aimed to identify plasma proteins that (1) distinguish patients from controls and (2) associate with disease activity in GCA.

Methods: Plasma samples were obtained from 30 patients with GCA in a multi-institutional, prospective longitudinal study: one captured during active disease and another while in clinical remission. Samples from 30 age-matched/sex-matched/race-matched non-disease controls were also collected. A high-throughput, aptamer-based proteomics assay, which examines over 7000 protein features, was used to generate plasma proteome profiles from study participants.

Results: After adjusting for potential confounders, we identified 537 proteins differentially abundant between active GCA and controls, and 781 between inactive GCA and controls. These proteins suggest distinct immune responses, metabolic pathways and potentially novel physiological processes involved in each disease state. Additionally, we found 16 proteins associated with disease activity in patients with active GCA. Random forest models trained on the plasma proteome profiles accurately differentiated active and inactive GCA groups from controls (95.0% and 98.3% in 10-fold cross-validation, respectively). However, plasma proteins alone provided limited ability to distinguish between active and inactive disease states within the same patients.

Conclusions: This comprehensive analysis of the plasma proteome in GCA suggests that blood protein signatures integrated with machine learning hold promise for discovering multiplex biomarkers for GCA.

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来源期刊
Annals of the Rheumatic Diseases
Annals of the Rheumatic Diseases 医学-风湿病学
CiteScore
35.00
自引率
9.90%
发文量
3728
审稿时长
1.4 months
期刊介绍: Annals of the Rheumatic Diseases (ARD) is an international peer-reviewed journal covering all aspects of rheumatology, which includes the full spectrum of musculoskeletal conditions, arthritic disease, and connective tissue disorders. ARD publishes basic, clinical, and translational scientific research, including the most important recommendations for the management of various conditions.
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