Data_Sheet_1_Diagnosis of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome With Partial Least Squares Discriminant Analysis: Relevance of Blood Extracellular Vesicles.PDF

Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS), a chronic disease characterized by long-lasting persistent debilitating widespread fatigue and post-exertional malaise, remains diagnosed by clinical criteria. Our group and others have identified differentially expressed miRNA profiles in the blood of patients. However, their diagnostic power individually or in combinations seems limited. A Partial Least Squares-Discriminant Analysis (PLS-DA) model initially based on 817 variables: two demographic, 34 blood analytic, 136 PBMC miRNAs, 639 Extracellular Vesicle (EV) miRNAs, and six EV features, selected an optimal number of five components, and a subset of 32 regressors showing statistically significant discriminant power. The presence of four EV-features (size and z-values of EVs prepared with or without proteinase K treatment) among the 32 regressors, suggested that blood vesicles carry relevant disease information. To further explore the features of ME/CFS EVs, we subjected them to Raman micro-spectroscopic analysis, identifying carotenoid peaks as ME/CFS fingerprints, possibly due to erythrocyte deficiencies. Although PLS-DA analysis showed limited capacity of Raman fingerprints for diagnosis (AUC = 0.7067), Raman data served to refine the number of PBMC miRNAs from our previous model still ensuring a perfect classification of subjects (AUC=1). Further investigations to evaluate model performance in extended cohorts of patients, to identify the precise ME/CFS EV components detected by Raman and to reveal their functional significance in the disease are warranted.

肌痛性脑脊髓炎/慢性疲劳综合征（Myalgic Encephalomyelitis/Chronic Fatigue Syndrome，简称ME/CFS）是一种以长期持续的衰弱性全身性疲劳及运动后不适为特征的慢性疾病，目前仍依靠临床标准进行诊断。本团队及其他研究团队已在患者血液中鉴定出差异表达的微小RNA（microRNA，简称miRNA）谱。然而，单一或组合使用这些miRNA谱的诊断效能似乎较为有限。本研究构建的偏最小二乘判别分析（Partial Least Squares-Discriminant Analysis，简称PLS-DA）模型最初基于817个变量：2项人口统计学指标、34项血液分析指标、136个外周血单个核细胞（Peripheral Blood Mononuclear Cell，简称PBMC）miRNA、639个细胞外囊泡（Extracellular Vesicle，简称EV）miRNA，以及6项EV特征；该模型筛选出最优的5个成分，以及32个具有统计学显著判别效能的回归因子子集。在这32个回归因子中，有4项EV特征（经或未经蛋白酶K（proteinase K）处理制备的EV的粒径与Z值）提示血液囊泡携带与疾病相关的重要信息。为进一步探究ME/CFS患者EV的特征，我们对其开展了拉曼显微光谱分析，鉴定出类胡萝卜素（carotenoid）峰可作为ME/CFS的特征指纹，这一现象可能与红细胞（erythrocyte）功能缺陷相关。尽管PLS-DA分析显示拉曼指纹用于诊断的效能有限（受试者工作特征曲线下面积（Area Under the Curve，简称AUC）=0.7067），但拉曼数据可用于优化此前模型中PBMC miRNA的筛选数量，仍可实现对受试者的完美分类（AUC=1）。未来仍需开展进一步研究：在更大规模的患者队列中评估模型性能，鉴定拉曼光谱检测到的ME/CFS患者EV的精准组分，并阐明其在疾病发生发展中的功能意义。