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

ACCESS DATA

Publications

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

Similar Datasets

Project description:BackgroundExosomes are involved in cell-to-cell communication, neovascularization, cancer metastasis, and drug resistance, which all play an important role in the occurrence and progression of hepatocellular carcinoma (HCC). Because there are few mechanistic studies about the function of exosomes in HCC, the goals of this study were to identify exosome-related genes in HCC, to establish a reliable prognostic model for HCC, and to explore underlying mechanisms.MethodsThe exoRBase and The Cancer Genome Atlas (TCGA) databases were used to analyze differentially expressed genes (DEGs). Cox regression and least absolute shrinkage and selection operator analyses were used to identify DEGs closely related to the overall survival of patients with HCC. An exosome-related prognostic model was then constructed in TCGA and validated in the International Cancer Genome Consortium database. A nomogram was developed to predict survival. CIBERSORT was used to estimate the abundance of different types of immune cells. Immunotherapy-related DEGs were used to predict the effect of immunotherapy.ResultsForty-eight exosome-related DEGs were obtained; of them, five [exportin 1 (XPO1), lysosomal thiol reductase (IFI30), F-box protein 16 (FBXO16), calmodulin 1 (CALM1), MORC family CW-type zinc finger 3 (MORC3)] were selected to construct a predictive model. Patients with HCC were then divided into low- and high-risk groups using the best cut-off value, as determined by the X-tile software. Prognosis was significantly poorer in the high-risk than in the low-risk group (P=0.009; hazard ratio =2.65). Features related to exosomes were found to positively regulate immune response. Further analysis showed a higher risk score was associated with higher expression of immune checkpoint molecules, including programmed death ligand 1 (PD-L1), programmed death ligand 2 (PD-L2), T cell Ig and ITIM domain (TIGIT), and indoleamine-2,3-dioxygenase 1 (IDO1).ConclusionsThis study has identified a novel signature based on exosome-related genes that has potential as a prognostic biomarker for HCC. Our research provides an immunological perspective for the development of precision treatment for HCC.

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.

Project description:BACKGROUND & AIMS:We combined gene expression and metabolic profiling analyses to identify factors associated with outcomes of patients with hepatocellular carcinoma (HCC). METHODS:We compared metabolic and gene expression patterns between paired tumor and nontumor tissues from 30 patients with HCC, and validated the results using samples from 356 patients with HCC. A total of 469 metabolites were measured using liquid chromatography/mass spectrometry and gas chromatography/mass spectrometry. Metabolic and genomic data were integrated, and Kaplan-Meier and Cox proportional hazards analyses were used to associate specific patterns with patient outcomes. Associated factors were evaluated for their effects on cancer cells in vitro and tumor formation in nude mice. RESULTS:We identified 28 metabolites and 169 genes associated with aggressive HCC. Lipid metabolites of stearoyl-CoA-desaturase (SCD) activity were associated with aberrant palmitate signaling in aggressive HCC samples. Expression of gene products associated with these metabolites, including SCD, were associated independently with survival times and tumor recurrence in the test and validation sets. Combined expression of SCD and ?-fetoprotein were associated with outcomes of patients with early-stage HCC. Levels of monounsaturated palmitic acid, the product of SCD activity, were increased in aggressive HCCs; monounsaturated palmitic acid increased migration and invasion of cultured HCC cells and colony formation by HCC cells. HCC cells that expressed small interfering RNA against SCD had decreased cell migration and colony formation in culture and reduced tumorigenicity in mice. CONCLUSIONS:By using a combination of gene expression and metabolic profile analysis, we identified a lipogenic network that involves SCD and palmitate signaling and was associated with HCC progression and patient outcomes. The microarray platform and data have been submitted to the Gene Expression Omnibus public database at NCBI following MIAME guidelines. Accession numbers: GPL4700 (platform), and GSE6857 (samples).

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.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets