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.
Highlights
[1] It is believed that Hepatocellular carcinoma (HCC) develops as a consequence of significant damage to the cellular machinery in the liver after sustained viral infections or cirrhosis. [2,3,4,5] Hepatitis C (HCV) is of particular interest since an estimated 130–170 million people are infected with the virus worldwide, and HCV causes approximately 25% of all reported cases of HCC. [6,7,8] HCV infections can be currently diagnosed with HCV antibody enzyme immunoassays but testing cannot distinguish acute and chronic infections or stratify HCV patients by cancer risk. [9,10,11,12,13] The main objective of this study was to investigate the ability to differentiate patients with HCV who did not develop cancer from those HCV patients who developed HCC
The field of metabolomics provides a powerful approach to identify small molecule biomarkers associated with cancer and other diseases. [14,15,16,17,18,19] By focusing on the concentrations and fluxes of low molecular weight metabolites (< ~1000 m/z) in biofluids, detailed information on biological systems and their concordant correlations across related disease states can be obtained. [20, 21] A significant potential exists to clinically translate a subset of promising biomarker candidates with good diagnostic or prognostic capacity, and metabolomics has become a popular approach in recent years. [22,23,24,25] Metabolomics studies based on liquid chromatography mass spectrometry (LC-MS) have been reported for detecting both aberrant lipid metabolism and characteristic metabolites of interest for individuals with HCC
Available metabolites that had: 1) previously been reported as relevant to liver diseases by other researchers and 2) had an observed mass with an accuracy of 1 ppm or less against the Agilent database were chosen as characteristic metabolites input into iterative supervised partial least-squares discriminant analysis (PLS-DA) models
Summary
Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer, and is responsible for an estimated 660,000 deaths worldwide. [1] It is believed that HCC develops as a consequence of significant damage to the cellular machinery in the liver after sustained viral infections or cirrhosis. [2,3,4,5] Hepatitis C (HCV) is of particular interest since an estimated 130–170 million people are infected with the virus worldwide, and HCV causes approximately 25% of all reported cases of HCC. [6,7,8] HCV infections can be currently diagnosed with HCV antibody enzyme immunoassays but testing cannot distinguish acute and chronic infections or stratify HCV patients by cancer risk. [9,10,11,12,13] The main objective of this study was to investigate the ability to differentiate patients with HCV who did not develop cancer from those HCV patients who developed HCC. [9,10,11,12,13] The main objective of this study was to investigate the ability to differentiate patients with HCV who did not develop cancer from those HCV patients who developed HCC. This differentiation is currently difficult due to the profound changes associated with the inflammatory response in both groups. We first built a supervised model derived from five putative metabolite markers known to be related to liver diseases and evaluated this model in terms of its ability to differentiate patients with HCV-HCC from those with HCV alone.
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