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IUBMB Life. 2018 Jun;70(6):553-562. doi: 10.1002/iub.1748. Epub 2018 Apr 6.

Discriminating gastric cancer and gastric ulcer using human plasma amino acid metabolic profile.

Jing F1, Hu X2, Cao Y3,4, Xu M4,5, Wang Y4,5, Jing Y4,5, Hu X1, Gao Y4,5, Zhu Z4,5.

Author information

1
Jinzhou Medical University, Jinzhou, China.
2
Internal Medicine Ward, General Hospital of Benxi Iron and Steel Co. Ltd., Benxi, China.
3
Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, China.
4
Key Laborotary of Liaoning Tumor Clinical Metabolomics, Jinzhou, China.
5
Cancer Center, The First Affiliated Hospital of Jinzhou Medical University, Jinzhou, China.

Abstract

Patients with gastric ulcer (GU) have a significantly higher risk of developing gastric cancer (GC), especially within 2 years after diagnosis. The main way to improve the prognosis of GC is to predict the tumorigenesis and metastasis in the early stage. The objective of this study was to demonstrate the ability of human plasma amino acid metabolic profile for discriminating GC and GU. In this study, we first used liquid chromatography-tandem mass spectrometry technique to characterize the plasma amino acid metabolism in GC and GU patients. Plasma samples were collected from 84 GC patients and 82 GU patients, and 22 amino acids were detected in each patient. Partial least squares-discriminant analysis model was performed to analyze the data of these amino acids. We observed seven differential amino acids between GC and GU. A regression analysis model was established using these seven amino acids. Finally, a panel of five differential amino acids, including glutamine, ornithine, histidine, arginine and tryptophan, was identified for discriminating GC and GU with good specificity and sensitivity. The receiver operating characteristic curve was used to evaluate diagnostic ability of the regression model and area under the curve was 0.922. In conclusion, this study demonstrated the potential values of plasma amino acid metabolic profile and metabolomic analysis technique in assisting diagnosis of GC. More studies are needed to highlight the theoretical strengths of metabolomics to understand the potential metabolic mechanisms in GC.

KEYWORDS:

amino acid; gastric cancer; metabolomics

PMID:
29626382
DOI:
10.1002/iub.1748
[Indexed for MEDLINE]
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