Original Article
Copyright ©2013 Baishideng Publishing Group Co., Limited. All rights reserved.
World J Gastroenterol. Jun 14, 2013; 19(22): 3423-3432
Published online Jun 14, 2013. doi: 10.3748/wjg.v19.i22.3423
Human liver tissue metabolic profiling research on hepatitis B virus-related hepatocellular carcinoma
Shu-Ye Liu, Rikki-Lei Zhang, Hua Kang, Zhi-Juan Fan, Zhi Du
Shu-Ye Liu, Rikki-Lei Zhang, Hua Kang, Zhi-Juan Fan, Zhi Du, Department of Hepatobiliary Surgery, Tianjin Third Central Hospital, Tianjin 300170, China
Author contributions: Liu SY and Du Z conceived and designed the research, and revised the manuscript; Zhang RL and Kang H performed the experiment, and data acquisition, analysis and interpretation; Fan ZJ collected samples and drafted the manuscript.
Correspondence to: Zhi Du, MD, Department of Hepatobiliary Surgery, Tianjin Third Central Hospital, No. 83 Jintang Road, Hedong District, Tianjin 300170, China. graylion@163.com
Telephone: +86-22-84112299 Fax: +86-22-24384350
Received: December 13, 2012
Revised: March 21, 2013
Accepted: April 3, 2013
Published online: June 14, 2013

AIM: To select characteristic endogenous metabolites in hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC) patients and to identify their molecular mechanism and potential clinical value.

METHODS: An ultra performance liquid chromatography and linear trap quadrupole-Orbitrap XL-mass spectrometry platform was used to analyze endogenous metabolites in the homogenate of central tumor tissue, adjacent tissue and distant tissue obtained from 10 HBV-related HCC patients. After pretreatment with Mzmine software, including peak detection, alignment and normalization, the acquired data were treated with Simca-P+software to establish multivariate statistical analysis based on a pattern recognition technique and characteristic metabolites highly correlated with changing trends in metabolic profiling were selected and further identified.

RESULTS: Based on data acquired using Mzmine software, a principal component analysis model (R2X = 66.9%, Q2 = 21.7%) with 6 principal components and an orthogonal partial least squares discriminant analysis model (R2X = 76.5%, R2Y = 93.7%, Q2 = 68.7%) with 2 predicted principal components and 5 orthogonal principal components were established in the three tissue groups. Forty-nine ions were selected, 33 ions passed the 2 related samples nonparametric test (P < 0.05) and 14 of these were further identified as characteristic metabolites that showed significant differences in levels between the central tumor tissue group and distant tumor tissue group, including 9 metabolites (L-phenylalanine, glycerophosphocholine, lysophosphatidylcholines, lysophosphatidylethanolamines and chenodeoxycholic acid glycine conjugate) which had been reported as serum metabolite biomarkers for HCC diagnosis in previous research, and 5 metabolites (beta-sitosterol, quinaldic acid, arachidyl carnitine, tetradecanal, and oleamide) which had not been reported before.

CONCLUSION: Characteristic metabolites and metabolic pathways highly related to HCC pathogenesis and progression are identified through metabolic profiling analysis of HCC tissue homogenates.

Keywords: Hepatocellular carcinoma, Metabolomics, Characteristic metabolites, Potential biomarker, Ultra performance liquid chromatography-mass spectrometry

Core tip: An ultra performance liquid chromatography-mass spectrometry platform was used in the present study to identify characteristic metabolites in hepatitis B virus-related hepatocellular carcinoma tumor tissues. From an orthogonal partial least squares discriminant analysis model established to determine metabolic profiling in the central tumor tissue group, adjacent tissue group and distant tissue group, 49 ions were selected and 14 of these were identified as characteristic metabolites. The detection of these metabolites in tumor tissue not only confirmed the targeted traceability of previously reported serum biomarkers related to cancer diagnosis, but also provided novel targets for anticancer research.