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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...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2019
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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. |
format | Online Article Text |
id | pubmed-6331438 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
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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