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NMR and LC/MS‐based global metabolomics to identify serum biomarkers differentiating hepatocellular carcinoma from liver cirrhosis

Hepatocellular carcinoma (HCC) is one of the most common malignant tumors in the world. However, current biomarkers that discriminate HCC from liver cirrhosis (LC) are important but are limited. More reliable biomarkers for HCC diagnosis are therefore needed. erum from HCC patients, LC patients and... Full description

Journal Title: International Journal of Cancer 01 August 2014, Vol.135(3), pp.658-668
Main Author: Liu, Yue
Other Authors: Hong, Zhanying , Tan, Guangguo , Dong, Xin , Yang, Genjin , Zhao, Liang , Chen, Xiaofei , Zhu, Zhenyu , Lou, Ziyang , Qian, Baohua , Zhang, Guoqing , Chai, Yifeng
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ID: ISSN: 0020-7136 ; E-ISSN: 1097-0215 ; DOI: 10.1002/ijc.28706
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recordid: wj10.1002/ijc.28706
title: NMR and LC/MS‐based global metabolomics to identify serum biomarkers differentiating hepatocellular carcinoma from liver cirrhosis
format: Article
creator:
  • Liu, Yue
  • Hong, Zhanying
  • Tan, Guangguo
  • Dong, Xin
  • Yang, Genjin
  • Zhao, Liang
  • Chen, Xiaofei
  • Zhu, Zhenyu
  • Lou, Ziyang
  • Qian, Baohua
  • Zhang, Guoqing
  • Chai, Yifeng
subjects:
  • Metabolomics
  • Random Forest
  • Hepatocellular Carcinoma
  • Nuclear Magnetic Resonance
  • Liquid Chromatography‐Mass Spectrometry
ispartof: International Journal of Cancer, 01 August 2014, Vol.135(3), pp.658-668
description: Hepatocellular carcinoma (HCC) is one of the most common malignant tumors in the world. However, current biomarkers that discriminate HCC from liver cirrhosis (LC) are important but are limited. More reliable biomarkers for HCC diagnosis are therefore needed. erum from HCC patients, LC patients and healthy volunteers were analyzed using NMR and LC/MS‐based approach in conjunction with random forest (RF) analysis to discriminate their serum metabolic profiles. Thirty‐two potential biomarkers have been identified, and the feasibility of using these biomarkers for the diagnosis of HCC was evaluated, where 100% sensitivity was achieved in detecting HCC patients even with AFP values lower than 20 ng/mL. The metabolic alterations induced by HCC showed perturbations in synthesis of ketone bodies, citrate cycle, phospholipid metabolism, sphingolipid metabolism, fatty acid oxidation, amino acid catabolism and bile acid metabolism in HCC patients. Our results suggested that these potential biomarkers identified appeared to have diagnostic and/or prognostic values for HCC, which deserve to be further investigated. In addition, it also suggested that RF is a classification algorithm well suited for selection of biologically relevant features in metabolomics. What's new? Hepatocellular cancer is frequently deadly, and difficult to detect early. Clinicians need a better way to identify the disease in high‐risk populations, such as cirrhosis patients. To find one, these authors combined imaging techniques with a sophisticated classification algorithm to tease out predictive biomarkers. They analyzed serum from healthy individuals, cirrhosis patients, and those with hepatocellular cancer, profiling the metabolome to get a comprehensive snapshot of the cell's contents. Applying the classification algorithm revealed a distinctive biomarker profile in the hepatocellular cancer patients, which could help provide better diagnostic techniques for this cancer.
language:
source:
identifier: ISSN: 0020-7136 ; E-ISSN: 1097-0215 ; DOI: 10.1002/ijc.28706
fulltext: fulltext
issn:
  • 0020-7136
  • 00207136
  • 1097-0215
  • 10970215
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titleNMR and LC/MS‐based global metabolomics to identify serum biomarkers differentiating hepatocellular carcinoma from liver cirrhosis
creatorLiu, Yue ; Hong, Zhanying ; Tan, Guangguo ; Dong, Xin ; Yang, Genjin ; Zhao, Liang ; Chen, Xiaofei ; Zhu, Zhenyu ; Lou, Ziyang ; Qian, Baohua ; Zhang, Guoqing ; Chai, Yifeng
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subjectMetabolomics ; Random Forest ; Hepatocellular Carcinoma ; Nuclear Magnetic Resonance ; Liquid Chromatography‐Mass Spectrometry
descriptionHepatocellular carcinoma (HCC) is one of the most common malignant tumors in the world. However, current biomarkers that discriminate HCC from liver cirrhosis (LC) are important but are limited. More reliable biomarkers for HCC diagnosis are therefore needed. erum from HCC patients, LC patients and healthy volunteers were analyzed using NMR and LC/MS‐based approach in conjunction with random forest (RF) analysis to discriminate their serum metabolic profiles. Thirty‐two potential biomarkers have been identified, and the feasibility of using these biomarkers for the diagnosis of HCC was evaluated, where 100% sensitivity was achieved in detecting HCC patients even with AFP values lower than 20 ng/mL. The metabolic alterations induced by HCC showed perturbations in synthesis of ketone bodies, citrate cycle, phospholipid metabolism, sphingolipid metabolism, fatty acid oxidation, amino acid catabolism and bile acid metabolism in HCC patients. Our results suggested that these potential biomarkers identified appeared to have diagnostic and/or prognostic values for HCC, which deserve to be further investigated. In addition, it also suggested that RF is a classification algorithm well suited for selection of biologically relevant features in metabolomics. What's new? Hepatocellular cancer is frequently deadly, and difficult to detect early. Clinicians need a better way to identify the disease in high‐risk populations, such as cirrhosis patients. To find one, these authors combined imaging techniques with a sophisticated classification algorithm to tease out predictive biomarkers. They analyzed serum from healthy individuals, cirrhosis patients, and those with hepatocellular cancer, profiling the metabolome to get a comprehensive snapshot of the cell's contents. Applying the classification algorithm revealed a distinctive biomarker profile in the hepatocellular cancer patients, which could help provide better diagnostic techniques for this cancer.
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titleNMR and LC/MS‐based global metabolomics to identify serum biomarkers differentiating hepatocellular carcinoma from liver cirrhosis
descriptionHepatocellular carcinoma (HCC) is one of the most common malignant tumors in the world. However, current biomarkers that discriminate HCC from liver cirrhosis (LC) are important but are limited. More reliable biomarkers for HCC diagnosis are therefore needed. erum from HCC patients, LC patients and healthy volunteers were analyzed using NMR and LC/MS‐based approach in conjunction with random forest (RF) analysis to discriminate their serum metabolic profiles. Thirty‐two potential biomarkers have been identified, and the feasibility of using these biomarkers for the diagnosis of HCC was evaluated, where 100% sensitivity was achieved in detecting HCC patients even with AFP values lower than 20 ng/mL. The metabolic alterations induced by HCC showed perturbations in synthesis of ketone bodies, citrate cycle, phospholipid metabolism, sphingolipid metabolism, fatty acid oxidation, amino acid catabolism and bile acid metabolism in HCC patients. Our results suggested that these potential biomarkers identified appeared to have diagnostic and/or prognostic values for HCC, which deserve to be further investigated. In addition, it also suggested that RF is a classification algorithm well suited for selection of biologically relevant features in metabolomics. What's new? Hepatocellular cancer is frequently deadly, and difficult to detect early. Clinicians need a better way to identify the disease in high‐risk populations, such as cirrhosis patients. To find one, these authors combined imaging techniques with a sophisticated classification algorithm to tease out predictive biomarkers. They analyzed serum from healthy individuals, cirrhosis patients, and those with hepatocellular cancer, profiling the metabolome to get a comprehensive snapshot of the cell's contents. Applying the classification algorithm revealed a distinctive biomarker profile in the hepatocellular cancer patients, which could help provide better diagnostic techniques for this cancer.
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abstractHepatocellular carcinoma (HCC) is one of the most common malignant tumors in the world. However, current biomarkers that discriminate HCC from liver cirrhosis (LC) are important but are limited. More reliable biomarkers for HCC diagnosis are therefore needed. erum from HCC patients, LC patients and healthy volunteers were analyzed using NMR and LC/MS‐based approach in conjunction with random forest (RF) analysis to discriminate their serum metabolic profiles. Thirty‐two potential biomarkers have been identified, and the feasibility of using these biomarkers for the diagnosis of HCC was evaluated, where 100% sensitivity was achieved in detecting HCC patients even with AFP values lower than 20 ng/mL. The metabolic alterations induced by HCC showed perturbations in synthesis of ketone bodies, citrate cycle, phospholipid metabolism, sphingolipid metabolism, fatty acid oxidation, amino acid catabolism and bile acid metabolism in HCC patients. Our results suggested that these potential biomarkers identified appeared to have diagnostic and/or prognostic values for HCC, which deserve to be further investigated. In addition, it also suggested that RF is a classification algorithm well suited for selection of biologically relevant features in metabolomics. What's new? Hepatocellular cancer is frequently deadly, and difficult to detect early. Clinicians need a better way to identify the disease in high‐risk populations, such as cirrhosis patients. To find one, these authors combined imaging techniques with a sophisticated classification algorithm to tease out predictive biomarkers. They analyzed serum from healthy individuals, cirrhosis patients, and those with hepatocellular cancer, profiling the metabolome to get a comprehensive snapshot of the cell's contents. Applying the classification algorithm revealed a distinctive biomarker profile in the hepatocellular cancer patients, which could help provide better diagnostic techniques for this cancer.
doi10.1002/ijc.28706
pages658-668
date2014-08-01