Detection of hepatocellular carcinoma in hepatitis C patients: Biomarker discovery by LC-MS

Jeremiah Bowers, Emma Hughes, Nicholas Skill, Mary Maluccio, Daniel Raftery

Research output: Contribution to journalArticle

14 Scopus citations

Abstract

Hepatocellular carcinoma (HCC) accounts for most cases of liver cancer worldwide; contraction of hepatitis C (HCV) is considered a major risk factor for liver cancer even when individuals have not developed formal cirrhosis. Global, untargeted metabolic profiling methods were applied to serum samples from patients with either HCV alone or HCC (with underlying HCV). The main objective of the study was to identify metabolite based biomarkers associated with cancer risk, with the long term goal of ultimately improving early detection and prognosis. Serum global metabolite profiles from patients with HCC (n= 37) and HCV (n= 21) were obtained using high performance liquid chromatography-mass spectrometry (HPLC-MS) methods. The selection of statistically significant metabolites for partial least-squares discriminant analysis (PLS-DA) model creation based on biological and statistical significance was contrasted to that of a traditional approach utilizing p-values alone. A PLS-DA model created using the former approach resulted in a model with 92% sensitivity, 95% specificity, and an AUROC of 0.93. A series of PLS-DA models iteratively utilizing three to seven metabolites that were altered significantly (p<. 0.05) and sufficiently (FC. ≤. 0.7 or FC. ≥. 1.3) showed good performance using p-values alone ; the best of these PLS-DA models was capable of generating 73% sensitivity, 95% specificity, and an AUROC of 0.92. Metabolic profiles derived from LC-MS readily distinguish patients with HCC and HCV from those with HCV only. Differences in the metabolic profiles between high-risk individuals and HCC indicate the possibility of identifying the early development of liver cancer in at risk patients. The use of biological significance as a selection process prior to PLS-DA modeling may offer improved probabilities for translation of newly discovered biomarkers to clinical application.

Original languageEnglish (US)
Pages (from-to)154-162
Number of pages9
JournalJournal of Chromatography B: Analytical Technologies in the Biomedical and Life Sciences
Volume966
DOIs
StatePublished - Sep 1 2014

Keywords

  • Biomarker
  • Early cancer detection
  • Hepatitis C
  • Hepatocellular carcinoma
  • HPLC-MS
  • LC-MS
  • Liver cancer
  • Metabolic profiling
  • Metabolomics

ASJC Scopus subject areas

  • Biochemistry
  • Analytical Chemistry
  • Cell Biology
  • Clinical Biochemistry

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