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Identification of network-based biomarkers of cardioembolic stroke using a systems biology approach with time series data

BACKGROUND: Molecular signaling of angiogenesis begins within hours after initiation of a stroke and the following regulation of endothelial integrity mediated by growth factor receptors and vascular growth factors. Recent studies further provided insights into the coordinated patterns of post-strok...

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Autores principales: Wong, Yung-Hao, Wu, Chia-Chou, Lai, Hsien-Yong, Jheng, Bo-Ren, Weng, Hsing-Yu, Chang, Tzu-Hao, Chen, Bor-Sen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4674888/
https://www.ncbi.nlm.nih.gov/pubmed/26679092
http://dx.doi.org/10.1186/1752-0509-9-S6-S4
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author Wong, Yung-Hao
Wu, Chia-Chou
Lai, Hsien-Yong
Jheng, Bo-Ren
Weng, Hsing-Yu
Chang, Tzu-Hao
Chen, Bor-Sen
author_facet Wong, Yung-Hao
Wu, Chia-Chou
Lai, Hsien-Yong
Jheng, Bo-Ren
Weng, Hsing-Yu
Chang, Tzu-Hao
Chen, Bor-Sen
author_sort Wong, Yung-Hao
collection PubMed
description BACKGROUND: Molecular signaling of angiogenesis begins within hours after initiation of a stroke and the following regulation of endothelial integrity mediated by growth factor receptors and vascular growth factors. Recent studies further provided insights into the coordinated patterns of post-stroke gene expressions and the relationships between neurodegenerative diseases and neural function recovery processes after a stroke. RESULTS: Differential protein-protein interaction networks (PPINs) were constructed at 3 post-stroke time points, and proteins with a significant stroke relevance value (SRV) were discovered. Genes, including UBC, CUL3, APP, NEDD8, JUP, and SIRT7, showed high associations with time after a stroke, and Ingenuity Pathway Analysis results showed that these post-stroke time series-associated genes were related to molecular and cellular functions of cell death, cell survival, the cell cycle, cellular development, cellular movement, and cell-to-cell signaling and interactions. These biomarkers may be helpful for the early detection, diagnosis, and prognosis of ischemic stroke. CONCLUSIONS: This is our first attempt to use our theory of a systems biology framework on strokes. We focused on 3 key post-stroke time points. We identified the network and corresponding network biomarkers for the 3 time points, further studies are needed to experimentally confirm the findings and compare them with the causes of ischemic stroke. Our findings showed that stroke-associated biomarker genes at different time points were significantly involved in cell cycle processing, including G(2)-M, G(1)-S and meiosis, which contributes to the current understanding of the etiology of stroke. We hope this work helps scientists reveal more hidden cellular mechanisms of stroke etiology and repair processes.
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spelling pubmed-46748882015-12-15 Identification of network-based biomarkers of cardioembolic stroke using a systems biology approach with time series data Wong, Yung-Hao Wu, Chia-Chou Lai, Hsien-Yong Jheng, Bo-Ren Weng, Hsing-Yu Chang, Tzu-Hao Chen, Bor-Sen BMC Syst Biol Research BACKGROUND: Molecular signaling of angiogenesis begins within hours after initiation of a stroke and the following regulation of endothelial integrity mediated by growth factor receptors and vascular growth factors. Recent studies further provided insights into the coordinated patterns of post-stroke gene expressions and the relationships between neurodegenerative diseases and neural function recovery processes after a stroke. RESULTS: Differential protein-protein interaction networks (PPINs) were constructed at 3 post-stroke time points, and proteins with a significant stroke relevance value (SRV) were discovered. Genes, including UBC, CUL3, APP, NEDD8, JUP, and SIRT7, showed high associations with time after a stroke, and Ingenuity Pathway Analysis results showed that these post-stroke time series-associated genes were related to molecular and cellular functions of cell death, cell survival, the cell cycle, cellular development, cellular movement, and cell-to-cell signaling and interactions. These biomarkers may be helpful for the early detection, diagnosis, and prognosis of ischemic stroke. CONCLUSIONS: This is our first attempt to use our theory of a systems biology framework on strokes. We focused on 3 key post-stroke time points. We identified the network and corresponding network biomarkers for the 3 time points, further studies are needed to experimentally confirm the findings and compare them with the causes of ischemic stroke. Our findings showed that stroke-associated biomarker genes at different time points were significantly involved in cell cycle processing, including G(2)-M, G(1)-S and meiosis, which contributes to the current understanding of the etiology of stroke. We hope this work helps scientists reveal more hidden cellular mechanisms of stroke etiology and repair processes. BioMed Central 2015-12-09 /pmc/articles/PMC4674888/ /pubmed/26679092 http://dx.doi.org/10.1186/1752-0509-9-S6-S4 Text en Copyright © 2015 Wong et al. http://creativecommons.org/licenses/by/4.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Wong, Yung-Hao
Wu, Chia-Chou
Lai, Hsien-Yong
Jheng, Bo-Ren
Weng, Hsing-Yu
Chang, Tzu-Hao
Chen, Bor-Sen
Identification of network-based biomarkers of cardioembolic stroke using a systems biology approach with time series data
title Identification of network-based biomarkers of cardioembolic stroke using a systems biology approach with time series data
title_full Identification of network-based biomarkers of cardioembolic stroke using a systems biology approach with time series data
title_fullStr Identification of network-based biomarkers of cardioembolic stroke using a systems biology approach with time series data
title_full_unstemmed Identification of network-based biomarkers of cardioembolic stroke using a systems biology approach with time series data
title_short Identification of network-based biomarkers of cardioembolic stroke using a systems biology approach with time series data
title_sort identification of network-based biomarkers of cardioembolic stroke using a systems biology approach with time series data
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4674888/
https://www.ncbi.nlm.nih.gov/pubmed/26679092
http://dx.doi.org/10.1186/1752-0509-9-S6-S4
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