The Immune Signature of CSF in Multiple Sclerosis with and without Oligoclonal Bands: A Machine Learning Approach to Proximity Extension Assay Analysis

IF 4.9 2区 生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY International Journal of Molecular Sciences Pub Date : 2023-12-21 DOI:10.3390/ijms25010139
L. Gaetani, G. Bellomo, Elena Di Sabatino, S. Sperandei, Andrea Mancini, K. Blennow, Henrik Zetterberg, L. Parnetti, M. Di Filippo
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Abstract

Early diagnosis of multiple sclerosis (MS) relies on clinical evaluation, magnetic resonance imaging (MRI), and cerebrospinal fluid (CSF) analysis. Reliable biomarkers are needed to differentiate MS from other neurological conditions and to define the underlying pathogenesis. This study aimed to comprehensively profile immune activation biomarkers in the CSF of individuals with MS and explore distinct signatures between MS with and without oligoclonal bands (OCB). A total of 118 subjects, including relapsing–remitting MS with OCB (MS OCB+) (n = 58), without OCB (MS OCB−) (n = 24), and controls with other neurological diseases (OND) (n = 36), were included. CSF samples were analyzed by means of proximity extension assay (PEA) for quantifying 92 immune-related proteins. Neurofilament light chain (NfL), a marker of axonal damage, was also measured. Machine learning techniques were employed to identify biomarker panels differentiating MS with and without OCB from controls. Analyses were performed by splitting the cohort into a training and a validation set. CSF CD5 and IL-12B exhibited the highest discriminatory power in differentiating MS from controls. CSF MIP-1-alpha, CD5, CXCL10, CCL23 and CXCL9 were positively correlated with NfL. Multivariate models were developed to distinguish MS OCB+ and MS OCB− from controls. The model for MS OCB+ included IL-12B, CD5, CX3CL1, FGF-19, CST5, MCP-1 (91% sensitivity and 94% specificity in the training set, 81% sensitivity, and 94% specificity in the validation set). The model for MS OCB− included CX3CL1, CD5, NfL, CCL4 and OPG (87% sensitivity and 80% specificity in the training set, 56% sensitivity and 48% specificity in the validation set). Comprehensive immune profiling of CSF biomarkers in MS revealed distinct pathophysiological signatures associated with OCB status. The identified biomarker panels, enriched in T cell activation markers and immune mediators, hold promise for improved diagnostic accuracy and insights into MS pathogenesis.
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有无少克隆带的多发性硬化 CSF 免疫特征:近距离延伸测定分析的机器学习方法
多发性硬化症(MS)的早期诊断依赖于临床评估、磁共振成像(MRI)和脑脊液(CSF)分析。要将多发性硬化症与其他神经系统疾病区分开来并确定其潜在的发病机制,需要可靠的生物标志物。本研究旨在全面分析多发性硬化症患者脑脊液中的免疫激活生物标志物,并探索有寡克隆带(OCB)和无寡克隆带(OCB)的多发性硬化症之间的不同特征。研究共纳入了 118 名受试者,包括伴有寡克隆带的复发性多发性硬化症(MS OCB+)(58 人)、不伴有寡克隆带的多发性硬化症(MS OCB-)(24 人)以及伴有其他神经系统疾病(OND)的对照组(36 人)。CSF 样本通过近距离延伸测定法(PEA)进行分析,以量化 92 种免疫相关蛋白。此外,还测量了轴突损伤标志物神经丝蛋白轻链(NfL)。研究人员采用机器学习技术来识别生物标志物面板,以区分患有或未患有 OCB 的多发性硬化症与对照组。分析方法是将队列分成训练集和验证集。CSF CD5和IL-12B在区分多发性硬化症与对照组方面表现出最高的鉴别力。CSF MIP-1-alpha、CD5、CXCL10、CCL23 和 CXCL9 与 NfL 呈正相关。建立了多变量模型来区分 MS OCB+ 和 MS OCB- 与对照组。MS OCB+的模型包括IL-12B、CD5、CX3CL1、FGF-19、CST5和MCP-1(在训练集中灵敏度为91%,特异性为94%;在验证集中灵敏度为81%,特异性为94%)。MS OCB- 模型包括 CX3CL1、CD5、NfL、CCL4 和 OPG(训练集的灵敏度为 87%,特异性为 80%;验证集的灵敏度为 56%,特异性为 48%)。对多发性硬化症患者脑脊液生物标志物的全面免疫分析显示了与OCB状态相关的独特病理生理学特征。已确定的生物标记物面板富含 T 细胞活化标记物和免疫介质,有望提高诊断准确性并深入了解多发性硬化症的发病机制。
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来源期刊
International Journal of Molecular Sciences
International Journal of Molecular Sciences Chemistry-Organic Chemistry
CiteScore
8.10
自引率
10.70%
发文量
13472
审稿时长
17.49 days
期刊介绍: The International Journal of Molecular Sciences (ISSN 1422-0067) provides an advanced forum for chemistry, molecular physics (chemical physics and physical chemistry) and molecular biology. It publishes research articles, reviews, communications and short notes. Our aim is to encourage scientists to publish their theoretical and experimental results in as much detail as possible. Therefore, there is no restriction on the length of the papers or the number of electronics supplementary files. For articles with computational results, the full experimental details must be provided so that the results can be reproduced. Electronic files regarding the full details of the calculation and experimental procedure, if unable to be published in a normal way, can be deposited as supplementary material (including animated pictures, videos, interactive Excel sheets, software executables and others).
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