Abstract
BACKGROUND: Early diagnosis of hepatocellular carcinoma may help to ensure that patients have a chance for long-term survival; however, currently available biomarkers lack sensitivity and specificity. AIM: To characterize the serum metabolome of hepatocellular carcinoma in order to develop a new metabolomics diagnostic model and identifying novel biomarkers for screening hepatocellular carcinoma based on the pattern recognition method. METHODS: Ultra-performance liquid chromatography-mass spectroscopy was used to characterize the serum metabolome of hepatocellular carcinoma (n = 30) and cirrhosis (n = 29) patients, followed by sequential feature selection combined with linear discriminant analysis to process the multivariate data. RESULTS: The concentrations of most metabolites, including proline, were lower in patients with hepatocellular carcinoma, whereas the hydroxypurine levels were higher in these patients. As ordinary analysis models failed to discriminate hepatocellular carcinoma from cirrhosis, pattern recognition analysis was used to establish a pattern recognition model that included hydroxypurine and proline. The leave-one-out cross-validation accuracy and area under the receiver operating characteristic curve analysis were 95.00% and 0.90 [95% Confidence Interval (CI): 0.81-0.99] for the training set, respectively, and 78.95% and 0.84 (95%CI: 0.67-1.00) for the validation set, respectively. In contrast, for α-fetoprotein, the accuracy and area under the receiver operating characteristic curve were 65.00% and 0.69 (95%CI: 0.52-0.86) for the training set, respectively, and 68.42% and 0.68 (95%CI: 0.41-0.94) for the validation set, respectively. The Z test revealed that the area under the curve of the linear discriminant analysis model was significantly higher than the area under the curve of α-fetoprotein (P < 0.05) in both the training and validation sets. CONCLUSION: Hydroxypurine and proline might be novel biomarkers for hepatocellular carcinoma, and this disease could be diagnosed by the metabolomics model based on pattern recognition.