urinary proteomics to support diagnosis of stroke支持诊断中风的尿蛋白质组学.pdf

Urinary Proteomics to Support Diagnosis of Stroke 1 1 1 1,2 1 Jesse Dawson *, Matthew Walters , Christian Delles , Harald Mischak , William Mullen 1 Institute of Cardiovascular and Medical Sciences, College of Medicine, Veterinary Life Sciences, University of Glasgow, Glasgow, United Kingdom, 2 Mosaiques Diagnostics, Hannover, Germany Abstract Accurate diagnosis in suspected ischaemic stroke can be difficult. We explored the urinary proteome in patients with stroke (n = 69), compared to controls (n = 33), and developed a biomarker model for the diagnosis of stroke. We performed capillary electrophoresis online coupled to micro-time-of-flight mass spectrometry. Potentially disease-specific peptides were identified and a classifier based on these was generated using support vector machine-based software. Candidate biomarkers were sequenced by liquid chromatography-tandem mass spectrometry. We developed two biomarker-based classifiers, employing 14 biomarkers (nominal p-value ,0.004) or 35 biomarkers (nominal p-value ,0.01). When tested on a blinded test set of 47 independent samples, the classification factor was significantly different between groups; for the 35 biomarker model, median value of the classifier was 0.49 (20.30 to 1.25) in cases compared to 21.04 (IQR 21.86 to 20.09) in controls, p,0.001. The 35 biomarker classifier gave sensitivity of 56%, specificity was 93% and the AUC on ROC analysis was 0.86. This study supports the potential for urinary proteomic biomarker models to assist with the diagnosis of acute stroke in those with mild symptoms. We now plan to refine further and explore the clinical utility of such a