Metabolomics Study of Stepwise Hepatocarcinogenesis From the Model Rats to Patients: Potential Biomarkers Effective for Small Hepatocellular Carcinoma Diagnosis

The aim of this study is to find the potential biomarkers from the rat hepatocellular carcinoma (HCC) disease model by using a non-target metabolomics method, and test their usefulness in early human HCC diagnosis. The serum metabolic profiling of the diethylnitrosamine-induced rat HCC model, which presents a stepwise histopathological progression that is similar to human HCC, was performed using liquid chromatography-mass spectrometry. Multivariate data analysis methods were utilized to identify the potential biomarkers. Three metabolites, taurocholic acid, lysophosphoethanolamine 16:0, and lysophosphatidylcholine 22:5, were defined as “marker metabolites,” which can be used to distinguish the different stages of chemical hepatocarcinogenesis. These metabolites represented the abnormal metabolism during the progress of hepatocarcinogenesis, which could also be found in patients. To test their diagnosis potential 412 sera from 262 patients with HCC, 76 patients with cirrhosis and 74 patients with chronic hepatitis B were collected and studied, it was found that 3 marker metabolites were effective for the discrimination of small liver tumor (solitary nodules of less than 2 cm in diameter) patients, achieved a sensitivity of 80.5% and a specificity of 80.1%,which is better than those of α-fetoprotein (53 and 64%, respectively). Moreover, they were also effective for the discrimination of all HCCs and chronic liver disease patients, which could achieve a sensitivity of 87.5% and a specificity of 72.3%, better than those of α-fetoprotein (61.2 and 64%). These results indicate metabolomics method has the potential of finding biomarkers for the early diagnosis of HCC. The aim of this study is to find the potential biomarkers from the rat hepatocellular carcinoma (HCC) disease model by using a non-target metabolomics method, and test their usefulness in early human HCC diagnosis. The serum metabolic profiling of the diethylnitrosamine-induced rat HCC model, which presents a stepwise histopathological progression that is similar to human HCC, was performed using liquid chromatography-mass spectrometry. Multivariate data analysis methods were utilized to identify the potential biomarkers. Three metabolites, taurocholic acid, lysophosphoethanolamine 16:0, and lysophosphatidylcholine 22:5, were defined as “marker metabolites,” which can be used to distinguish the different stages of chemical hepatocarcinogenesis. These metabolites represented the abnormal metabolism during the progress of hepatocarcinogenesis, which could also be found in patients. To test their diagnosis potential 412 sera from 262 patients with HCC, 76 patients with cirrhosis and 74 patients with chronic hepatitis B were collected and studied, it was found that 3 marker metabolites were effective for the discrimination of small liver tumor (solitary nodules of less than 2 cm in diameter) patients, achieved a sensitivity of 80.5% and a specificity of 80.1%,which is better than those of α-fetoprotein (53 and 64%, respectively). Moreover, they were also effective for the discrimination of all HCCs and chronic liver disease patients, which could achieve a sensitivity of 87.5% and a specificity of 72.3%, better than those of α-fetoprotein (61.2 and 64%). These results indicate metabolomics method has the potential of finding biomarkers for the early diagnosis of HCC. Hepatocellular carcinoma (HCC) 1The abbreviations used are:HCChepatocellular carcinomaCLDChronic liver diseasesDENdiethylnitrosamineLPElysophosphoethanolamineTCATaurocholic acidPEphosphoethanolaminePCphosphatidylcholineLPClysophosphatidylcholineHBVhepatitis B virusCTcomputed tomographyMRImagnetic resonance imagingAFPAlpha FetoproteinSDSprague-DawleyPLS-DApartial least squares discriminate analysisTICtotal ion counts chromatogramVIPvariable importance in the projectionHCAhierarchical cluster analysisROCreceiver operating characteristic curveAUCarea under the curveALTAlanine AminotransferaseASTAspertate AminotransferasePEMT 2PE N-methyltransferase 2PUFApoly unsaturated fatty acidFFAFree fatty acid. 1The abbreviations used are:HCChepatocellular carcinomaCLDChronic liver diseasesDENdiethylnitrosamineLPElysophosphoethanolamineTCATaurocholic acidPEphosphoethanolaminePCphosphatidylcholineLPClysophosphatidylcholineHBVhepatitis B virusCTcomputed tomographyMRImagnetic resonance imagingAFPAlpha FetoproteinSDSprague-DawleyPLS-DApartial least squares discriminate analysisTICtotal ion counts chromatogramVIPvariable importance in the projectionHCAhierarchical cluster analysisROCreceiver operating characteristic curveAUCarea under the curveALTAlanine AminotransferaseASTAspertate AminotransferasePEMT 2PE N-methyltransferase 2PUFApoly unsaturated fatty acidFFAFree fatty acid. is a type of malignancy with a high mortality rate worldwide, especially in the East Asian countries (1Gomaa A.I. 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Hence, new biomarkers for monitoring hepatocarcinogenesis would be of great clinical importance. hepatocellular carcinoma Chronic liver diseases diethylnitrosamine lysophosphoethanolamine Taurocholic acid phosphoethanolamine phosphatidylcholine lysophosphatidylcholine hepatitis B virus computed tomography magnetic resonance imaging Alpha Fetoprotein Sprague-Dawley partial least squares discriminate analysis total ion counts chromatogram variable importance in the projection hierarchical cluster analysis receiver operating characteristic curve area under the curve Alanine Aminotransferase Aspertate Aminotransferase PE N-methyltransferase 2 poly unsaturated fatty acid Free fatty acid. hepatocellular carcinoma Chronic liver diseases diethylnitrosamine lysophosphoethanolamine Taurocholic acid phosphoethanolamine phosphatidylcholine lysophosphatidylcholine hepatitis B virus computed tomography magnetic resonance imaging Alpha Fetoprotein Sprague-Dawley partial least squares discriminate analysis total ion counts chromatogram variable importance in the projection hierarchical cluster analysis receiver operating characteristic curve area under the curve Alanine Aminotransferase Aspertate Aminotransferase PE N-methyltransferase 2 poly unsaturated fatty acid Free fatty acid. 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