Metabolomic Characterization of Hepatocellular Carcinoma in Patients with Liver Cirrhosis for Biomarker Discovery.

MedStar author(s):
Citation: Cancer Epidemiology, Biomarkers & Prevention. 26(5):675-683, 2017 MayPMID: 27913395Institution: MedStar Washington Hospital CenterDepartment: Surgery/TransplantationForm of publication: Journal ArticleMedline article type(s): Journal ArticleSubject headings: *Biomarkers, Tumor/bl [Blood] | *Carcinoma, Hepatocellular/bl [Blood] | *Liver Neoplasms/bl [Blood] | Adult | Aged | Carcinoma, Hepatocellular/di [Diagnosis] | Female | Humans | Liver Cirrhosis/bl [Blood] | Liver Cirrhosis/di [Diagnosis] | Liver Neoplasms/di [Diagnosis] | Male | Metabolomics/mt [Methods] | Middle Aged | Sensitivity and SpecificityYear: 2016Local holdings: Available online from MWHC library: Nov 1991 - present (after 1 year)ISSN:
  • 1055-9965
Name of journal: Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive OncologyAbstract: BACKGROUND: Metabolomics plays an important role in providing insight into the etiology and mechanisms of hepatocellular carcinoma (HCC). This is accomplished by a comprehensive analysis of patterns involved in metabolic alterations in human specimens. This study compares the levels of plasma metabolites in HCC cases versus cirrhotic patients and evaluates the ability of candidate metabolites in distinguishing the two groups. Also, it investigates the combined use of metabolites and clinical covariates for detection of HCC in patients with liver cirrhosis.CONCLUSIONS: This study demonstrates the combination of metabolites and clinical covariates as an effective approach for early detection of HCC in patients with liver cirrhosis.Copyright (c)2016, American Association for Cancer Research.IMPACT: Further investigation of these findings may improve understanding of HCC pathophysiology and possible implication of the metabolites in HCC prevention and diagnosis.METHODS: Untargeted analysis of metabolites in plasma from 128 subjects (63 HCC cases and 65 cirrhotic controls) was conducted using gas chromatography coupled to mass spectrometry (GC-MS). This was followed by targeted evaluation of selected metabolites. LASSO regression was used to select a set of metabolites and clinical covariates that are associated with HCC. The performance of candidate biomarkers in distinguishing HCC from cirrhosis was evaluated through a leave-one-out cross-validation based on area under the receiver operating characteristics (ROC) curve.RESULTS: We identified 11 metabolites and three clinical covariates that differentiated HCC cases from cirrhotic controls. Combining these features in a panel for disease classification using support vector machines (SVM) yielded better area under the ROC curve compared to alpha-fetoprotein (AFP).All authors: Desai CS, Di Poto C, Ferrarini A, Luo Y, Nezami Ranjbar MR, Ressom HW, Shetty K, Tadesse MG, Tu C, Varghese RS, Wang M, Zhang C, Zhao Y, Zuo YFiscal year: FY2017Digital Object Identifier: Date added to catalog: 2017-05-24
Holdings
Item type Current library Collection Call number Status Date due Barcode
Journal Article MedStar Authors Catalog Article 27913395 Available 27913395

Available online from MWHC library: Nov 1991 - present (after 1 year)

BACKGROUND: Metabolomics plays an important role in providing insight into the etiology and mechanisms of hepatocellular carcinoma (HCC). This is accomplished by a comprehensive analysis of patterns involved in metabolic alterations in human specimens. This study compares the levels of plasma metabolites in HCC cases versus cirrhotic patients and evaluates the ability of candidate metabolites in distinguishing the two groups. Also, it investigates the combined use of metabolites and clinical covariates for detection of HCC in patients with liver cirrhosis.

CONCLUSIONS: This study demonstrates the combination of metabolites and clinical covariates as an effective approach for early detection of HCC in patients with liver cirrhosis.

Copyright (c)2016, American Association for Cancer Research.

IMPACT: Further investigation of these findings may improve understanding of HCC pathophysiology and possible implication of the metabolites in HCC prevention and diagnosis.

METHODS: Untargeted analysis of metabolites in plasma from 128 subjects (63 HCC cases and 65 cirrhotic controls) was conducted using gas chromatography coupled to mass spectrometry (GC-MS). This was followed by targeted evaluation of selected metabolites. LASSO regression was used to select a set of metabolites and clinical covariates that are associated with HCC. The performance of candidate biomarkers in distinguishing HCC from cirrhosis was evaluated through a leave-one-out cross-validation based on area under the receiver operating characteristics (ROC) curve.

RESULTS: We identified 11 metabolites and three clinical covariates that differentiated HCC cases from cirrhotic controls. Combining these features in a panel for disease classification using support vector machines (SVM) yielded better area under the ROC curve compared to alpha-fetoprotein (AFP).

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