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Urinary protein biomarkers based on LC–MS/MS analysis to discriminate vascular dementia from Alzheimer’s disease in Han Chinese population

OBJECTIVE: This study aimed to identify the potential urine biomarkers of vascular dementia (VD) and unravel the disease-associated mechanisms by applying Liquid chromatography tandem-mass spectrometry (LC–MS/MS). METHODS: LC–MS/MS proteomic analysis was applied to urine samples from 3 groups, inclu...

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Autores principales: Chen, Ruijuan, Yi, Yuanjing, Xiao, Wenbiao, Zhong, Bowen, Zhang, Le, Zeng, Yi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9905227/
https://www.ncbi.nlm.nih.gov/pubmed/36761180
http://dx.doi.org/10.3389/fnagi.2023.1070854
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author Chen, Ruijuan
Yi, Yuanjing
Xiao, Wenbiao
Zhong, Bowen
Zhang, Le
Zeng, Yi
author_facet Chen, Ruijuan
Yi, Yuanjing
Xiao, Wenbiao
Zhong, Bowen
Zhang, Le
Zeng, Yi
author_sort Chen, Ruijuan
collection PubMed
description OBJECTIVE: This study aimed to identify the potential urine biomarkers of vascular dementia (VD) and unravel the disease-associated mechanisms by applying Liquid chromatography tandem-mass spectrometry (LC–MS/MS). METHODS: LC–MS/MS proteomic analysis was applied to urine samples from 3 groups, including 14 patients with VD, 9 patients with AD, and 21 normal controls (NC). By searching the MS data by Proteome Discoverer software, analyzing the protein abundances qualitatively and quantitatively, comparing between groups, combining bioinformatics analysis using Gene Ontology (GO) and pathway crosstalk analysis using Kyoto Encyclopedia of Genes and Genomes (KEGG), and literature searching, the differentially expressed proteins (DEPs) of VD can be comprehensively determined at last and were further quantified by receiver operating characteristic (ROC) curve methods. RESULTS: The proteomic findings showed quantitative changes in patients with VD compared to patients with NC and AD groups; among 4,699 identified urine proteins, 939 and 1,147 proteins displayed quantitative changes unique to VD vs. NC and AD, respectively, including 484 overlapped common DEPs. Then, 10 unique proteins named in KEGG database (including PLOD3, SDCBP, SRC, GPRC5B, TSG101/STP22/VPS23, THY1/CD90, PLCD, CDH16, NARS/asnS, AGRN) were confirmed by a ROC curve method. CONCLUSION: Our results suggested that urine proteins enable detection of VD from AD and VC, which may provide an opportunity for intervention.
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spelling pubmed-99052272023-02-08 Urinary protein biomarkers based on LC–MS/MS analysis to discriminate vascular dementia from Alzheimer’s disease in Han Chinese population Chen, Ruijuan Yi, Yuanjing Xiao, Wenbiao Zhong, Bowen Zhang, Le Zeng, Yi Front Aging Neurosci Aging Neuroscience OBJECTIVE: This study aimed to identify the potential urine biomarkers of vascular dementia (VD) and unravel the disease-associated mechanisms by applying Liquid chromatography tandem-mass spectrometry (LC–MS/MS). METHODS: LC–MS/MS proteomic analysis was applied to urine samples from 3 groups, including 14 patients with VD, 9 patients with AD, and 21 normal controls (NC). By searching the MS data by Proteome Discoverer software, analyzing the protein abundances qualitatively and quantitatively, comparing between groups, combining bioinformatics analysis using Gene Ontology (GO) and pathway crosstalk analysis using Kyoto Encyclopedia of Genes and Genomes (KEGG), and literature searching, the differentially expressed proteins (DEPs) of VD can be comprehensively determined at last and were further quantified by receiver operating characteristic (ROC) curve methods. RESULTS: The proteomic findings showed quantitative changes in patients with VD compared to patients with NC and AD groups; among 4,699 identified urine proteins, 939 and 1,147 proteins displayed quantitative changes unique to VD vs. NC and AD, respectively, including 484 overlapped common DEPs. Then, 10 unique proteins named in KEGG database (including PLOD3, SDCBP, SRC, GPRC5B, TSG101/STP22/VPS23, THY1/CD90, PLCD, CDH16, NARS/asnS, AGRN) were confirmed by a ROC curve method. CONCLUSION: Our results suggested that urine proteins enable detection of VD from AD and VC, which may provide an opportunity for intervention. Frontiers Media S.A. 2023-01-25 /pmc/articles/PMC9905227/ /pubmed/36761180 http://dx.doi.org/10.3389/fnagi.2023.1070854 Text en Copyright © 2023 Chen, Yi, Xiao, Zhong, Zhang and Zeng. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Aging Neuroscience
Chen, Ruijuan
Yi, Yuanjing
Xiao, Wenbiao
Zhong, Bowen
Zhang, Le
Zeng, Yi
Urinary protein biomarkers based on LC–MS/MS analysis to discriminate vascular dementia from Alzheimer’s disease in Han Chinese population
title Urinary protein biomarkers based on LC–MS/MS analysis to discriminate vascular dementia from Alzheimer’s disease in Han Chinese population
title_full Urinary protein biomarkers based on LC–MS/MS analysis to discriminate vascular dementia from Alzheimer’s disease in Han Chinese population
title_fullStr Urinary protein biomarkers based on LC–MS/MS analysis to discriminate vascular dementia from Alzheimer’s disease in Han Chinese population
title_full_unstemmed Urinary protein biomarkers based on LC–MS/MS analysis to discriminate vascular dementia from Alzheimer’s disease in Han Chinese population
title_short Urinary protein biomarkers based on LC–MS/MS analysis to discriminate vascular dementia from Alzheimer’s disease in Han Chinese population
title_sort urinary protein biomarkers based on lc–ms/ms analysis to discriminate vascular dementia from alzheimer’s disease in han chinese population
topic Aging Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9905227/
https://www.ncbi.nlm.nih.gov/pubmed/36761180
http://dx.doi.org/10.3389/fnagi.2023.1070854
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