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 analy-sis 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 (betasitosterol, 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. 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 with 2 predicted principal components and 5 orthogonal principal components were established in the three tissue groups. Forty-nine ions were selected (R2X = 76.5%, R2Y = 93.7%, Q2 = 68.7%) , 33 ions passed the 2 related samples nonparametric test (P <0.05) and 14 of these were further 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 conjugates) which had been reported as serum metabolite biomarkers for HCC diagnosis in previous research, and 5 metabolites (betasitosterol, 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.