Metabonomics Studies Of Hepatocellular Carcinomaand Mechanism Of Glaucocalyxin A

Hepatocellular carcinoma(HCC) is one of the most common malignancies with high incidence and mortality which is a threaten to human health. However, current biomarkers that discriminate HCC from liver cirrhosis are important but are limited. More reliable biomarkers for HCC diagnosis are therefore needed. There is also a urgent need to find more effective drugs to cure HCC. By combining NMR, LC/MS and GC/MS along with pattern recognition methods to discover the nature of HCC and the mechanism of glaucocalyxin A(GLA).The following study investigates the preparation of plasma for metabolomic profiling analysis by LC/MS and LC/MS. Three different organic solvents(acetonitrile, methanol, and ethanol) were used to assess rat plasma preparation via protein precipitation. The optimal conditions for sample preparation were investigated, with consideration to the number of extracted markers and data quality/reproducibility. Two choices of solvent compositions were found to be “optimal” for preparation of plasma for RP analysis; these were “methanol/ethanol”(1:1, v/v) and “methanol/ ethanol / acetonitrile”(1: 1:1, v/v/v); for HILIC analysis; these were “methanol/ acetonitrile”(1:1, v/v) and “ethanol / acetonitrile”(1: 1:1, v/v/v). Analysis of plasma metabolomic samples by gas chromatography–mass spectrometry always requires comprehensive pretreatment including oximation and silylation. Although heating block(HB) is a commonly used method, it is time consuming. This study describes an extremely time-effective microwave-assisted(MA) oximation and silylation approach for metabolomic study of plasma samples. The Box-Behnken design was employed to optimize the MA conditions. The results showed that microwave irradiation decreased the sample preparation time from approximately 180 min to 5 min without loss of information for the metabolites in plasma samples. Com-pared with HB method, the newly developed MA oximation and silylation of plasma metabolome samples was more efficient and time-effective and may prove to be an attractive alternative for high-throughput sample preparation in plasma metabolomics.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. Serum 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/m L. 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 fur-ther investigated. In addition, it also suggested that RF is a classification algorithm well suited for selection of biologically rel-evant features in metabolomics.A GC-MS, RPLC-MS and HILIC-MS-based metabonomic approach, combined with pattern recognition methods including PCA and OPLS-DA, has been developed to characterize the global cell extraction metabolic profile treated with GLA. As the VIP-value threshold cutoff of the metabolites was set to 1.5, metabolites above this threshold were filtered out as potential target biomarkers. Twenty four distinct potential biomarkers in cell extraction were identified. To further elucidate the mechanism of GLA, related metabolic pathways have been studied. It was found that GLA were relevant to the disturbance of metabolism primarily involving lipid metabolism, sphingolipid metabolism, purine metabolism, pyrimidine metabolism, phospholipid catabolism, phenylalanine metabolism, proline metabolism, tyrosine metabolism, tryptophan metabolism and methionine metabolism. Furthermore, it was confirmed that Chinese Medicine monomer GLA could play its role in inhibiting liver cancer cells by disturbing some metabolic pathways, which might be mapped by producing alterations in some biomarkers found in this research.