Dataset Information

Utilization of metabolomics to identify serum biomarkers for hepatocellular carcinoma in patients with liver cirrhosis

ABSTRACT: Characterizing the metabolic changes pertaining to hepatocellular carcinoma (HCC) in patients with liver cirrhosis is believed to contribute towards early detection, treatment, and understanding of the molecular mechanisms of HCC. In this study, we compare metabolite levels in sera of 78 HCC cases with 184 cirrhotic controls by using ultra performance liquid chromatography coupled with a hybrid quadrupole time-of-flight mass spectrometry (UPLC-QTOF MS). Following data preprocessing, the most relevant ions in distinguishing HCC cases from patients with cirrhosis are selected by parametric and non-parametric statistical methods. Putative metabolite identifications for these ions are obtained through mass-based database search. Verification of the identities of selected metabolites is conducted by comparing their MS/MS fragmentation patterns and retention time with those from authentic compounds. Quantitation of these metabolites is performed in a subset of the serum samples (10 HCC and 10 cirrhosis) using isotope dilution by selected reaction monitoring (SRM) on triple quadrupole linear ion trap (QqQLIT) and triple quadrupole (QqQ) mass spectrometers. The results of this analysis confirm that metabolites involved in sphingolipid metabolism and phospholipid catabolism such as sphingosine-1-phosphate (S-1-P) and lysophosphatidylcholine (lysoPC 17:0) are up-regulated in sera of HCC vs. those with liver cirrhosis. Down-regulated metabolites include those involved in bile acid biosynthesis (specifically cholesterol metabolism) such as glycochenodeoxycholic acid 3-sulfate (3-sulfo-GCDCA), glycocholic acid (GCA), glycodeoxycholic acid (GDCA), taurocholic acid (TCA), and taurochenodeoxycholate (TCDCA). These results provide useful insights into HCC biomarker discovery utilizing metabolomics as an efficient and cost-effective platform. Our work shows that metabolomic profiling is a promising tool to identify candidate metabolic biomarkers for early detection of HCC cases in high risk population of cirrhotic patients.

INSTRUMENT(S): UPLC Q-TOF Premier (Waters)

SUBMITTER: Jinlian Wang Habtom Ressom

PROVIDER: MTBLS17 | MetaboLights | 2013-06-30

REPOSITORIES: MetaboLights

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Dataset's files

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			Action	DRS
	MTBLS17	Other
	FILES	Other
	a_MTBLS17_live_metabolite_profiling_mass_spectrometry.txt	Txt
	i_Investigation.txt	Txt
	m_MTBLS17_live_metabolite_profiling_mass_spectrometry_v2_maf.tsv	Tabular

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Publications

Utilization of metabolomics to identify serum biomarkers for hepatocellular carcinoma in patients with liver cirrhosis.

Ressom Habtom W HW Xiao Jun Feng JF Tuli Leepika L Varghese Rency S RS Zhou Bin B Tsai Tsung-Heng TH Ranjbar Mohammad R Nezami MR Zhao Yi Y Wang Jinlian J Di Poto Cristina C Cheema Amrita K AK Tadesse Mahlet G MG Goldman Radoslav R Shetty Kirti K

Analytica chimica acta 20120720

Characterizing the metabolic changes pertaining to hepatocellular carcinoma (HCC) in patients with liver cirrhosis is believed to contribute towards early detection, treatment, and understanding of the molecular mechanisms of HCC. In this study, we compare metabolite levels in sera of 78 HCC cases with 184 cirrhotic controls by using ultra performance liquid chromatography coupled with a hybrid quadrupole time-of-flight mass spectrometry (UPLC-QTOF MS). Following data preprocessing, the most rel ...[more]

PMID: 22882828

Similar Datasets

Project description:Although hepatocellular carcinoma (HCC) has been subjected to continuous investigation and its symptoms are well-known, early stage diagnosis of this disease remains difficult and the survival rate after diagnosis is typically very low (3−5%). Early and accurate detection of metabolic changes in the sera of patients with liver cirrhosis can help improve the prognosis of HCC and lead to a better understanding of its mechanism at the molecular level, thus providing patients with in-time treatment of the disease. In this study, we compared metabolite levels in sera of 40 HCC patients and 49 cirrhosis patients from Egypt by using ultraperformance liquid chromatography coupled with quadrupole time-of-flight mass spectrometer (UPLC-QTOF MS). Following data preprocessing, the most relevant ions in distinguishing HCC cases from cirrhotic controls are selected by statistical methods. Putative metabolite identifications for these ions are obtained through mass-based database search. The identities of some of the putative identifications are verified by comparing their MS/MS fragmentation patterns and retention times with those from authentic compounds. Finally, the serum samples are reanalyzed for quantitation of selected metabolites as candidate biomarkers of HCC. This quantitation was performed using isotope dilution by selected reaction monitoring (SRM) on a triple quadrupole linear ion trap (QqQLIT) coupled to UPLC. Statistical analysis of the UPLC-QTOF data identified 274 monoisotopic ion masses with statistically significant differences in ion intensities between HCC cases and cirrhotic controls. Putative identifications were obtained for 158 ions by mass based search against databases. We verified the identities of selected putative identifications including glycholic acid (GCA), glycodeoxycholic acid (GDCA), 3β, 6β-dihydroxy-5β-cholan-24-oic acid, oleoyl carnitine, and Phe-Phe. SRM-based quantitation confirmed significant differences between HCC and cirrhotic controls in metabolite levels of bile acid metabolites, long chain carnitines and small peptide. Our study provides useful insight into appropriate experimental design and computational methods for serum biomarker discovery using LC−MS/MS based metabolomics. This study has led to the identification of candidate biomarkers with significant changes in metabolite levels between HCC cases and cirrhotic controls. This is the first MS-based metabolic biomarker discovery study on Egyptian subjects that led to the identification of candidate metabolites that discriminate early stage HCC from patients with liver cirrhosis.

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.