BACKGROUND: Metabolomic studies have been applied to disease biomarkers selection. With the metabolomic technique, gas chromatography/mass spectrometry (GC/MS), human serum metabolites can be detected and identified. The purpose of this study was to investigate the serum metabolic profile of hepatitis B virus (HBV) infected cirrhosis patients and to detect disease biomarkers. METHODS: HBV infected non-cirrhosis male subjects (n=20) and HBV infected cirrhosis male patients (n=20) participated in this experiment. Serum metabolome was detected through chemical derivatization followed by GC/MS. The high-flux metabolomic data were analyzed by stepwise discriminant analysis. RESULTS: Out of the 41 metabolites detected in serum, we selected metabolites, including acetic acid, sorbitol, D-lactic acid, hexanoic acid, 1-naphthalenamine, butanoic acid, phosphoric acid, D-glucitol, and glucose, which in combination with each other could segregate the two groups. The error count was 0% for the non-cirrhosis group and 25% for the cirrhosis group. CONCLUSIONS: This technique can be used to select biomarkers for hepatic cirrhosis.
BACKGROUND: Metabolomic studies have been applied to disease biomarkers selection. With the metabolomic technique, gas chromatography/mass spectrometry (GC/MS), human serum metabolites can be detected and identified. The purpose of this study was to investigate the serum metabolic profile of hepatitis B virus (HBV) infected cirrhosispatients and to detect disease biomarkers. METHODS:HBV infected non-cirrhosis male subjects (n=20) and HBV infected cirrhosis malepatients (n=20) participated in this experiment. Serum metabolome was detected through chemical derivatization followed by GC/MS. The high-flux metabolomic data were analyzed by stepwise discriminant analysis. RESULTS: Out of the 41 metabolites detected in serum, we selected metabolites, including acetic acid, sorbitol, D-lactic acid, hexanoic acid, 1-naphthalenamine, butanoic acid, phosphoric acid, D-glucitol, and glucose, which in combination with each other could segregate the two groups. The error count was 0% for the non-cirrhosis group and 25% for the cirrhosis group. CONCLUSIONS: This technique can be used to select biomarkers for hepatic cirrhosis.
Authors: Konstantinos John Dabos; John Andrew Parkinson; Ian Howard Sadler; John Nicholas Plevris; Peter Clive Hayes Journal: World J Hepatol Date: 2015-06-28
Authors: Courtney E Hershberger; Alejandro I Rodarte; Shirin Siddiqi; Amika Moro; Lou-Anne Acevedo-Moreno; J Mark Brown; Daniela S Allende; Federico Aucejo; Daniel M Rotroff Journal: Liver Cancer Int Date: 2021-05-20