Huan-Liang Liu

Publications (2)2.91 Total impact

• Article: Serum metabolic profiling of human gastric cancer based on gas chromatography/mass spectrometry.

  Hu Song, Jun-Sheng Peng, Yao Dong-Sheng, Zu-Li Yang, Huan-Liang Liu, Yi-Ke Zeng, Xian-Ping Shi, Bi-Yan Lu
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  ABSTRACT: Research on molecular mechanisms of carcinogenesis plays an important role in diagnosing and treating gastric cancer. Metabolic profiling may offer the opportunity to understand the molecular mechanism of carcinogenesis and help to non-invasively identify the potential biomarkers for the early diagnosis of human gastric cancer. The aims of this study were to explore the underlying metabolic mechanisms of gastric cancer and to identify biomarkers associated with morbidity. Gas chromatography/mass spectrometry (GC/MS) was used to analyze the serum metabolites of 30 Chinese gastric cancer patients and 30 healthy controls. Diagnostic models for gastric cancer were constructed using orthogonal partial least squares discriminant analysis (OPLS-DA). Acquired metabolomic data were analyzed by the nonparametric Wilcoxon test to find serum metabolic biomarkers for gastric cancer. The OPLS-DA model showed adequate discrimination between cancer and non-cancer cohorts while the model failed to discriminate different pathological stages (I-IV) of gastric cancer patients. A total of 44 endogenous metabolites such as amino acids, organic acids, carbohydrates, fatty acids, and steroids were detected, of which 18 differential metabolites were identified with significant differences. A total of 13 variables were obtained for their greatest contribution in the discriminating OPLS-DA model [variable importance in the projection (VIP) value >1.0], among which 11 metabolites were identified using both VIP values (VIP >1) and the Wilcoxon test. These metabolites potentially revealed perturbations of glycolysis and of amino acid, fatty acid, cholesterol, and nucleotide metabolism of gastric cancer patients. These results suggest that gastric cancer serum metabolic profiling has great potential in detecting this disease and helping to understand its metabolic mechanisms.
  Brazilian journal of medical and biological research = Revista brasileira de pesquisas medicas e biologicas / Sociedade Brasileira de Biofisica ... [et al.] 11/2011; 45(1):78-85. · 1.08 Impact Factor
• Article: Tissue metabolomic fingerprinting reveals metabolic disorders associated with human gastric cancer morbidity.

  Hu Song, Lei Wang, Huan-Liang Liu, Xiao-Bin Wu, Hua-She Wang, Zhong-Hui Liu, Yun Li, De-Chang Diao, Hong-Lei Chen, Jun-Sheng Peng
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  ABSTRACT: The principal way to improve the outcome of gastric cancer (GC) is to predict carcinogenesis and metastasis at an early stage. The aims of the present study were to test the hypothesis that distinct metabolic profiles are reflected in GC tissues and to further explore potential biomarkers for GC diagnosis. Gas chromatography/mass spectrometry (GC/MS) was utilized to analyze tissue metabolites from 30 GC patients. A diagnostic model for GC was constructed using orthogonal partial least squares discriminant analysis (OPLS-DA), and the metabolomic data were analyzed using the non-parametric Wilcoxon rank sum test to identify the metabolic tissue biomarkers for GC. Over 100 signals were routinely detected in one single total ion current (TIC) chromatogram, and the OPLS-DA model generated from the metabolic profile of the tissues adequately discriminated the GC tissues from the normal mucosae. Among the low-molecular-weight endogenous metabolites, a total of 41 compounds, such as amino acids, organic acids, carbohydrates, fatty acids and steroids, were detected, and 15 differential metabolites were identified with significant difference (p<0.05). A total of 20 variables were noted which contributed to a great extent in the discriminating OPLS-DA model (VIP value >1.0), among which 12 metabolites were identified using both VIP values (VIP >1) and the Wilcoxon test (p<0.05). In conclusion, the identification of the metabolites associated with GC morbidity potentially revealed perturbations of glycolysis, fatty acid β-oxidation, cholesterol and amino acid metabolism. These results suggest that tissue metabolic profiles have great potential in detecting GC and may aid in understanding its underlying mechanisms.
  Oncology Reports 08/2011; 26(2):431-8. · 1.84 Impact Factor