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Identification of novel biomarkers for prediction of neurological prognosis following cardiac arrest

BACKGROUND: Early prognostication of neurological outcome in comatose patients after cardiac arrest (CA) is important for devising patient treatment strategies. However, there is still a lack of sensitive and specific biomarkers for easy identification of these patients. We evaluated whether molecul...

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Autores principales: Eun, Jung Woo, Yang, Hee Doo, Kim, Soo Hyun, Hong, Sungyoup, Park, Kyu Nam, Nam, Suk Woo, Jeong, Sikyoung
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Impact Journals LLC 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5369953/
https://www.ncbi.nlm.nih.gov/pubmed/28147324
http://dx.doi.org/10.18632/oncotarget.14877
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author Eun, Jung Woo
Yang, Hee Doo
Kim, Soo Hyun
Hong, Sungyoup
Park, Kyu Nam
Nam, Suk Woo
Jeong, Sikyoung
author_facet Eun, Jung Woo
Yang, Hee Doo
Kim, Soo Hyun
Hong, Sungyoup
Park, Kyu Nam
Nam, Suk Woo
Jeong, Sikyoung
author_sort Eun, Jung Woo
collection PubMed
description BACKGROUND: Early prognostication of neurological outcome in comatose patients after cardiac arrest (CA) is important for devising patient treatment strategies. However, there is still a lack of sensitive and specific biomarkers for easy identification of these patients. We evaluated whether molecular signatures from blood of CA patients might help to improve the prediction of neurological outcome. METHODS: We examined 22 comatose patients resuscitated after CA and obtained peripheral blood samples 48 hours after CA. To identify novel blood biomarkers, we aimed to measure neurological outcomes according to the Cerebral Performance Category (CPC) score at 6 months after CA and to determine blood transcriptome-based molecular signature of poor neurological outcome group. RESULTS: According to the CPC score, 10 patients exhibited a CPC score of one and 12 patients, a CPC score four to five. Blood transcriptomics revealed differently expressed profiles between the good outcome group and poor outcome group. A total of 150 genes were down-regulated and 237 genes were up-regulated in the poor neurological outcome group compared with good outcome group. From the blood transcriptome-based signatures, we identified that MAPK3, BCL2 and AKT1 were more specific and sensitive diagnostic biomarkers in poor neurological outcome with an area under the curve of 0.867 (p<0.0001), 0.800 (p=0.003), and 0.767 (p=0.016) respectively. CONCLUSIONS: We identify three biomarkers as potential predictors of neurological outcome following CA. Further assessment of the prognostic value of transcriptomic analysis in larger cohorts of CA patients is needed.
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spelling pubmed-53699532017-04-17 Identification of novel biomarkers for prediction of neurological prognosis following cardiac arrest Eun, Jung Woo Yang, Hee Doo Kim, Soo Hyun Hong, Sungyoup Park, Kyu Nam Nam, Suk Woo Jeong, Sikyoung Oncotarget Research Paper: Pathology BACKGROUND: Early prognostication of neurological outcome in comatose patients after cardiac arrest (CA) is important for devising patient treatment strategies. However, there is still a lack of sensitive and specific biomarkers for easy identification of these patients. We evaluated whether molecular signatures from blood of CA patients might help to improve the prediction of neurological outcome. METHODS: We examined 22 comatose patients resuscitated after CA and obtained peripheral blood samples 48 hours after CA. To identify novel blood biomarkers, we aimed to measure neurological outcomes according to the Cerebral Performance Category (CPC) score at 6 months after CA and to determine blood transcriptome-based molecular signature of poor neurological outcome group. RESULTS: According to the CPC score, 10 patients exhibited a CPC score of one and 12 patients, a CPC score four to five. Blood transcriptomics revealed differently expressed profiles between the good outcome group and poor outcome group. A total of 150 genes were down-regulated and 237 genes were up-regulated in the poor neurological outcome group compared with good outcome group. From the blood transcriptome-based signatures, we identified that MAPK3, BCL2 and AKT1 were more specific and sensitive diagnostic biomarkers in poor neurological outcome with an area under the curve of 0.867 (p<0.0001), 0.800 (p=0.003), and 0.767 (p=0.016) respectively. CONCLUSIONS: We identify three biomarkers as potential predictors of neurological outcome following CA. Further assessment of the prognostic value of transcriptomic analysis in larger cohorts of CA patients is needed. Impact Journals LLC 2017-01-28 /pmc/articles/PMC5369953/ /pubmed/28147324 http://dx.doi.org/10.18632/oncotarget.14877 Text en Copyright: © 2017 Eun et al. http://creativecommons.org/licenses/by/3.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Paper: Pathology
Eun, Jung Woo
Yang, Hee Doo
Kim, Soo Hyun
Hong, Sungyoup
Park, Kyu Nam
Nam, Suk Woo
Jeong, Sikyoung
Identification of novel biomarkers for prediction of neurological prognosis following cardiac arrest
title Identification of novel biomarkers for prediction of neurological prognosis following cardiac arrest
title_full Identification of novel biomarkers for prediction of neurological prognosis following cardiac arrest
title_fullStr Identification of novel biomarkers for prediction of neurological prognosis following cardiac arrest
title_full_unstemmed Identification of novel biomarkers for prediction of neurological prognosis following cardiac arrest
title_short Identification of novel biomarkers for prediction of neurological prognosis following cardiac arrest
title_sort identification of novel biomarkers for prediction of neurological prognosis following cardiac arrest
topic Research Paper: Pathology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5369953/
https://www.ncbi.nlm.nih.gov/pubmed/28147324
http://dx.doi.org/10.18632/oncotarget.14877
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