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Cell Biol Int ; 42(4): 425-434, 2018 Apr.
Article in English | MEDLINE | ID: mdl-29144590

ABSTRACT

Hepatocellular carcinoma (HCC) causes death mainly by disseminated metastasis progression from the organ being confined. Different metastatic stages are closely related to cellular metabolic profiles. Normal hepatocyte and HepG2 cell line from low metastatic HCC were studied by NMR-based metabolomic techniques. Multivariate and univariate statistical analyses were utilized to identify characteristic metabolites from cells and cultured media. Elevated levels of acetate, creatine, isoleucine, leucine, and phenylalanine were observed in HepG2 cells, suggesting more active in gathering nutrient components along with altered amino acid metabolisms and enhanced lipid metabolism. High glucose consumption was significantly different in low metastatic cells. A series of characteristic metabolites were identified and served as biomarkers. Relative metabolic pathway analysis shows that low metastatic HepG2 cell line exhibits active behaviors in metabolisms and biosynthesis of specific amino acids and energy metabolism. Moreover, characteristic metabolites-based classification models executed by support vector machines algorithm perform robustly to classify normal hepatocyte and HepG2 cell line. It is concluded that NMR-based metabolomic analyses of cell lines can provide a powerful approach to understand metastasis-related biological alterations. The present study also provides a basis for metabolic markers determination of hepatic carcinoma in the future clinical study.


Subject(s)
Carcinoma, Hepatocellular/metabolism , Hepatocytes/metabolism , Liver Neoplasms/metabolism , Biomarkers, Tumor/metabolism , Carcinoma, Hepatocellular/diagnostic imaging , Carcinoma, Hepatocellular/pathology , Cell Line , Cells, Cultured , Energy Metabolism , Hep G2 Cells , Humans , Lipid Metabolism , Liver Neoplasms/diagnostic imaging , Liver Neoplasms/pathology , Magnetic Resonance Spectroscopy/methods , Metabolic Networks and Pathways , Metabolome , Metabolomics
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