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

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

Posted September 13, 2014 by Matrix-Bio • No Comments


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 PLSDA
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 PLSDA
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.


 


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