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Issue 41, 2018, Issue in Progress
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Identification of potential diagnostic biomarkers of cerebral infarction using gas chromatography-mass spectrometry and chemometrics

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Abstract

Cerebral infarction (CI) is one of the most common cerebrovascular diseases and remains a major health problem worldwide. In this study, we evaluated the potential diagnostic biomarkers and important relevant metabolic pathways associated with CI. Metabolomics based on gas chromatography-mass spectrometry coupled with the multivariate pattern recognition technique were used to characterize the potential serum metabolic profiles of CI. Forty healthy controls and thirty-three cerebral infarction patients were recruited for the nontargeted global metabolites' study and subsequent targeted fatty acid analysis. Overall, thirty-four endogenous metabolites were found in serum from the untargeted global study, four of which were detected to be significantly different between the CI group and healthy controls, including L-lysine, octadecanoic acid (fatty acid), L-tyrosine and lactic acid. Additionally, fourteen free fatty acids were identified by the subsequent targeted fatty acid analysis, and seven of them were detected to be significantly different between the CI group and healthy controls, which were mainly associated with arachidonic acid metabolism and fatty acid metabolism. Our results suggest several potential diagnostic biomarkers, and serum metabolism research is demonstrated as a powerful tool to explore the pathogenesis of CI.

Graphical abstract: Identification of potential diagnostic biomarkers of cerebral infarction using gas chromatography-mass spectrometry and chemometrics

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Supplementary files

Article information


Submitted
12 Apr 2018
Accepted
05 Jun 2018
First published
21 Jun 2018

This article is Open Access

RSC Adv., 2018,8, 22866-22875
Article type
Paper

Identification of potential diagnostic biomarkers of cerebral infarction using gas chromatography-mass spectrometry and chemometrics

M. Li, H. Xiao, Y. Qiu, J. Huang, R. Man, Y. Qin, G. Xiong, Q. Peng, Y. Jian, C. Peng, W. Zhang and W. Wang, RSC Adv., 2018, 8, 22866
DOI: 10.1039/C8RA03132K

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