Dataset Information

Determination of candidate metabolite biomarkers associated with recurrence of HCV-related hepatocellular carcinoma.

ABSTRACT: Hepatitis C virus (HCV) infection is associated with a high risk of developing hepatocellular carcinoma (HCC) and HCC recurrence remains the primary threat to outcomes after curative therapy. In this study, we compared recurrent and non-recurrent HCC patients treated with radiofrequency ablation (RFA) in order to identify characteristic metabolic profile variations associated with HCC recurrence. Gas chromatography-mass spectrometry (GC-MS) -based metabolomic analyses were conducted on serum samples obtained before and after RFA therapy. Significant variations were observed in metabolites in the glycerolipid, tricarboxylic acid (TCA) cycle, fatty acid, and amino acid pathways between recurrent and non-recurrent patients. Observed differences in metabolites associated with recurrence did not coincide before and after treatment except for fatty acids. Based on the comparison of serum metabolomes between recurrent and non-recurrent patients, key discriminatory metabolites were defined by a random forest (RF) test. Two combinations of these metabolites before and after RFA treatment showed outstanding performance in predicting HCV-related HCC recurrence, they were further confirmed by an external validation set. Our study showed that the determined combination of metabolites may be potential biomarkers for the prediction of HCC recurrence before and after RFA treatment.

SUBMITTER: Liu Z

PROVIDER: S-EPMC5814209 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature

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Determination of candidate metabolite biomarkers associated with recurrence of HCV-related hepatocellular carcinoma.

Liu Zhicheng Z Nahon Pierre P Li Zaifang Z Yin Peiyuan P Li Yanli Y Amathieu Roland R Ganne-Carrié Nathalie N Ziol Marianne M Sellier Nicolas N Seror Olivier O Le Moyec Laurence L Savarin Philippe P Xu Guowang G

Oncotarget 20171215 5

Hepatitis C virus (HCV) infection is associated with a high risk of developing hepatocellular carcinoma (HCC) and HCC recurrence remains the primary threat to outcomes after curative therapy. In this study, we compared recurrent and non-recurrent HCC patients treated with radiofrequency ablation (RFA) in order to identify characteristic metabolic profile variations associated with HCC recurrence. Gas chromatography-mass spectrometry (GC-MS) -based metabolomic analyses were conducted on serum sam ...[more]

PMID: 29464069

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Project description:Introduction: Metabolomics provides simultaneous assessment of a broad range of metabolites that can potentially serve as indicators of the overall physiology status as well as the response to host and environmental stimuli. It has been broadly used for biomarker discovery and characterization of complex diseases such as cancer. The evaluation of the changes in the levels of metabolites in samples stratified by race could lead to the identification of more reliable biomarkers than those obtained through whole-population-based approaches. In this study, we used plasma samples collected from patients recruited at MedStar Georgetown University Hospital to investigate metabolites that may be associated with hepatocellular carcinoma (HCC) in a race-specific manner. Methods: Plasma samples were protein depleted using a solution composed of acetonitrile:isopropanol:water (3:3:2) containing a mixture of isotope-labeled internal standards. The extracted metabolites were trimethylsilyl derivatized prior to analysis by GC-MS. A quality control (QC) sample derived by pooling plasma from multiple subjects was run in between samples to assess reproducibility. A mixture of fatty acids methyl esters (FAMEs) and alkane standards was run for retention index calibration. The mixture of isotope-labeled internal standards was used to evaluate the reproducibility of the GC-MS data across multiple runs. Preliminary Data: Plasma samples collected from 40 HCC cases and 44 patients with liver cirrhosis were analyzed. The cirrhotic controls were frequency matched with the HCC cases by demographic variables. The participants included 19 African Americans (AA) and 50 European Americans (EA). The analysis targeted 46 metabolites for quantitative analysis by Agilent GC-qMS in selected ion monitoring (SIM) mode. The data were pre-processed by the Automated Mass Spectral Deconvolution and Identification System (AMDIS) for peak detection, deconvolution, and identification. The resulting peaks were aligned using Mass Profiler Professional (MPP) from Agilent Technologies. A LASSO regression model was applied to select metabolites with significant changes in HCC vs. cirrhosis in three groups: (1) AA and EA combined; (2) AA only; and (3) EA only. Also, metabolites that distinguish HCC cases from cirrhosis in the three groups were selected by considering only those subjects with Hepatitis C virus (HCV) infection. The performances of the metabolites selected by LASSO in each group were evaluated through a leave-one-out cross-validation. We identified race-specific metabolites that differentiated HCC cases from cirrhotic controls, yielding better area under the ROC curve compared to alpha-fetoprotein (AFP) - the serological marker widely used for the diagnosis of HCC. Novel Aspect: We identified race-associated metabolites that are significantly altered in HCC vs. cirrhosis, suggesting the potential role of race in HCC.

Dataset Information

Determination of candidate metabolite biomarkers associated with recurrence of HCV-related hepatocellular carcinoma.

Publications

Determination of candidate metabolite biomarkers associated with recurrence of HCV-related hepatocellular carcinoma.

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