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Identification of Blood Biomarkers for Alzheimer's Disease Through Computational Prediction and Experimental Validation

Background: Alzheimer's disease (AD) is the major cause of dementia in population aged over 65 years, accounting up to 70% dementia cases. However, validated peripheral biomarkers for AD diagnosis are not available up to present. In this study, we adopted a new strategy of combination of comput...

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Autores principales: Yao, Fang, Zhang, Kaoyuan, Zhang, Yan, Guo, Yi, Li, Aidong, Xiao, Shifeng, Liu, Qiong, Shen, Liming, Ni, Jiazuan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6331438/
https://www.ncbi.nlm.nih.gov/pubmed/30671019
http://dx.doi.org/10.3389/fneur.2018.01158
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author Yao, Fang
Zhang, Kaoyuan
Zhang, Yan
Guo, Yi
Li, Aidong
Xiao, Shifeng
Liu, Qiong
Shen, Liming
Ni, Jiazuan
author_facet Yao, Fang
Zhang, Kaoyuan
Zhang, Yan
Guo, Yi
Li, Aidong
Xiao, Shifeng
Liu, Qiong
Shen, Liming
Ni, Jiazuan
author_sort Yao, Fang
collection PubMed
description Background: Alzheimer's disease (AD) is the major cause of dementia in population aged over 65 years, accounting up to 70% dementia cases. However, validated peripheral biomarkers for AD diagnosis are not available up to present. In this study, we adopted a new strategy of combination of computational prediction and experimental validation to identify blood protein biomarkers for AD. Methods: First, we collected tissue-based gene expression data of AD patients and healthy controls from GEO database. Second, we analyzed these data and identified differentially expressed genes for AD. Third, we applied a blood-secretory protein prediction program on these genes and predicted AD-related proteins in blood. Finally, we collected blood samples of AD patients and healthy controls to validate the potential AD biomarkers by using ELISA experiments and Western blot analyses. Results: A total of 2754 genes were identified to express differentially in brain tissues of AD, among which 296 genes were predicted to encode AD-related blood-secretory proteins. After careful analysis and literature survey on these predicted blood-secretory proteins, ten proteins were considered as potential AD biomarkers, five of which were experimentally verified with significant change in blood samples of AD vs. controls by ELISA, including GSN, BDNF, TIMP1, VLDLR, and APLP2. ROC analyses showed that VLDLR and TIMP1 had excellent performance in distinguishing AD patients from controls (area under the curve, AUC = 0.932 and 0.903, respectively). Further validation of VLDLR and TIMP1 by Western blot analyses has confirmed the results obtained in ELISA experiments. Conclusion: VLDLR and TIMP1 had better discriminative abilities between ADs and controls, and might serve as potential blood biomarkers for AD. To our knowledge, this is the first time to identify blood protein biomarkers for AD through combination of computational prediction and experimental validation. In addition, VLDLR was first reported here as potential blood protein biomarker for AD. Thus, our findings might provide important information for AD diagnosis and therapies.
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spelling pubmed-63314382019-01-22 Identification of Blood Biomarkers for Alzheimer's Disease Through Computational Prediction and Experimental Validation Yao, Fang Zhang, Kaoyuan Zhang, Yan Guo, Yi Li, Aidong Xiao, Shifeng Liu, Qiong Shen, Liming Ni, Jiazuan Front Neurol Neurology Background: Alzheimer's disease (AD) is the major cause of dementia in population aged over 65 years, accounting up to 70% dementia cases. However, validated peripheral biomarkers for AD diagnosis are not available up to present. In this study, we adopted a new strategy of combination of computational prediction and experimental validation to identify blood protein biomarkers for AD. Methods: First, we collected tissue-based gene expression data of AD patients and healthy controls from GEO database. Second, we analyzed these data and identified differentially expressed genes for AD. Third, we applied a blood-secretory protein prediction program on these genes and predicted AD-related proteins in blood. Finally, we collected blood samples of AD patients and healthy controls to validate the potential AD biomarkers by using ELISA experiments and Western blot analyses. Results: A total of 2754 genes were identified to express differentially in brain tissues of AD, among which 296 genes were predicted to encode AD-related blood-secretory proteins. After careful analysis and literature survey on these predicted blood-secretory proteins, ten proteins were considered as potential AD biomarkers, five of which were experimentally verified with significant change in blood samples of AD vs. controls by ELISA, including GSN, BDNF, TIMP1, VLDLR, and APLP2. ROC analyses showed that VLDLR and TIMP1 had excellent performance in distinguishing AD patients from controls (area under the curve, AUC = 0.932 and 0.903, respectively). Further validation of VLDLR and TIMP1 by Western blot analyses has confirmed the results obtained in ELISA experiments. Conclusion: VLDLR and TIMP1 had better discriminative abilities between ADs and controls, and might serve as potential blood biomarkers for AD. To our knowledge, this is the first time to identify blood protein biomarkers for AD through combination of computational prediction and experimental validation. In addition, VLDLR was first reported here as potential blood protein biomarker for AD. Thus, our findings might provide important information for AD diagnosis and therapies. Frontiers Media S.A. 2019-01-08 /pmc/articles/PMC6331438/ /pubmed/30671019 http://dx.doi.org/10.3389/fneur.2018.01158 Text en Copyright © 2019 Yao, Zhang, Zhang, Guo, Li, Xiao, Liu, Shen and Ni. http://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 Neurology
Yao, Fang
Zhang, Kaoyuan
Zhang, Yan
Guo, Yi
Li, Aidong
Xiao, Shifeng
Liu, Qiong
Shen, Liming
Ni, Jiazuan
Identification of Blood Biomarkers for Alzheimer's Disease Through Computational Prediction and Experimental Validation
title Identification of Blood Biomarkers for Alzheimer's Disease Through Computational Prediction and Experimental Validation
title_full Identification of Blood Biomarkers for Alzheimer's Disease Through Computational Prediction and Experimental Validation
title_fullStr Identification of Blood Biomarkers for Alzheimer's Disease Through Computational Prediction and Experimental Validation
title_full_unstemmed Identification of Blood Biomarkers for Alzheimer's Disease Through Computational Prediction and Experimental Validation
title_short Identification of Blood Biomarkers for Alzheimer's Disease Through Computational Prediction and Experimental Validation
title_sort identification of blood biomarkers for alzheimer's disease through computational prediction and experimental validation
topic Neurology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6331438/
https://www.ncbi.nlm.nih.gov/pubmed/30671019
http://dx.doi.org/10.3389/fneur.2018.01158
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