Urinary proteomics to support diagnosis of stroke.

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-fli...

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Autores principales:	Jesse Dawson, Matthew Walters, Christian Delles, Harald Mischak, William Mullen
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2012
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/27f4745986b04d73a0b10bc68e7b8e44
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spelling	oai:doaj.org-article:27f4745986b04d73a0b10bc68e7b8e442021-11-18T07:18:35ZUrinary proteomics to support diagnosis of stroke.1932-620310.1371/journal.pone.0035879https://doaj.org/article/27f4745986b04d73a0b10bc68e7b8e442012-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22615742/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203Accurate 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 (-0.30 to 1.25) in cases compared to -1.04 (IQR -1.86 to -0.09) 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 test in large prospective clinical trials.Jesse DawsonMatthew WaltersChristian DellesHarald MischakWilliam MullenPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 7, Iss 5, p e35879 (2012)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Jesse Dawson Matthew Walters Christian Delles Harald Mischak William Mullen Urinary proteomics to support diagnosis of stroke.
description	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 (-0.30 to 1.25) in cases compared to -1.04 (IQR -1.86 to -0.09) 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 test in large prospective clinical trials.
format	article
author	Jesse Dawson Matthew Walters Christian Delles Harald Mischak William Mullen
author_facet	Jesse Dawson Matthew Walters Christian Delles Harald Mischak William Mullen
author_sort	Jesse Dawson
title	Urinary proteomics to support diagnosis of stroke.
title_short	Urinary proteomics to support diagnosis of stroke.
title_full	Urinary proteomics to support diagnosis of stroke.
title_fullStr	Urinary proteomics to support diagnosis of stroke.
title_full_unstemmed	Urinary proteomics to support diagnosis of stroke.
title_sort	urinary proteomics to support diagnosis of stroke.
publisher	Public Library of Science (PLoS)
publishDate	2012
url	https://doaj.org/article/27f4745986b04d73a0b10bc68e7b8e44
work_keys_str_mv	AT jessedawson urinaryproteomicstosupportdiagnosisofstroke AT matthewwalters urinaryproteomicstosupportdiagnosisofstroke AT christiandelles urinaryproteomicstosupportdiagnosisofstroke AT haraldmischak urinaryproteomicstosupportdiagnosisofstroke AT williammullen urinaryproteomicstosupportdiagnosisofstroke
_version_	1718423651833872384

Urinary proteomics to support diagnosis of stroke.

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