Research Papers: Pathology:

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

Jung Woo Eun, Hee Doo Yang, Soo Hyun Kim, Sungyoup Hong, Kyu Nam Park, Suk Woo Nam and Sikyoung Jeong _

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Oncotarget. 2017; 8:16144-16157. https://doi.org/10.18632/oncotarget.14877

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Jung Woo Eun1, Hee Doo Yang1, Soo Hyun Kim2, Sungyoup Hong3, Kyu Nam Park2, Suk Woo Nam1 and Sikyoung Jeong3

1 Department of Pathology, Functional RNomics Research Center, Cancer Evolution Research Center, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea

2 Department of Emergency Medicine, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea

3 Department of Emergency Medicine, College of Medicine, The Catholic University of Korea, Daejeon, Republic of Korea

Correspondence to:

Sikyoung Jeong, email:

Keywords: peripheral blood transcriptome, molecular signature, neurological prognosis, cardiac arrest, cerebral performance category, Pathology Section

Received: October 27, 2016 Accepted: January 19, 2017 Published: January 28, 2017


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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