Metabolite profiling analysis of hepatitis B virus–induced liver cirrhosis patients with minimal hepatic encephalopathy using gas chromatography‐time‐of‐flight mass spectrometry and ultra‐performance liquid chromatography‐quadrupole‐time‐of‐flight mass spectrometry
Authors: Huang Guochu , Xie Sheng , Wang Meng , Mao Dewen , Huang Guye , Huang Jingjing , Liu Xirong , Zhang Rongzhen , Xie Jiacheng , Huang Liang Jiang , Cheng Chen , Yao Fan , Zhong Yu , Lin Long , Yao Chun 
Published: October 2022

This study used gas chromatography‐time‐of‐flight mass spectrometry (GC‐TOFMS) and ultra‐performance liquid chromatography‐quadrupole TOFMS (UPLC‐QTOFMS) metabonomic analytical techniques in combination with bioinformatics and pattern recognition analysis methods to analyze the serum metabolite profiling of hepatitis B virus (HBV)–induced liver cirrhosis patients with minimal hepatic encephalopathy (MHE), to find the specific biomarkers of MHE, to reveal the pathogenesis of MHE, and to determine a promising approach for early diagnosis of MHE. Serum samples of 100 normal controls (NC group), 29 HBV‐induced liver cirrhosis patients with MHE (MHE group), and 24 HBV‐induced liver cirrhosis patients without MHE [comprising 12 cases of compensated cirrhosis (CS group) and 12 cases of decompensated cirrhosis (DS group)] were collected and employed into GC‐TOFMS and UPLC‐QTOFMS platforms for serum metabolite detection; the outcome data were then analyzed using principal component analysis and orthogonal partial least squares‐discriminant analysis (OPLS‐DA). There were no significant differential metabolites between the NC group and the CS group. A series of key differential metabolites were detected. According to the variable influence in projection values and P‐values, 60 small‐molecule metabolites were considered to be dysregulated in the MHE group (compared to the NC group); 27 of these 60 dysregulated differential metabolites were considered to be the potential biomarkers (see Table 4, marked in bold); 66 small‐molecule metabolites were considered to be dysregulated in the DS group (compared to the NC group); 34 of these 66 dysregulated differential metabolites were considered to be the potential biomarkers (see Table 5, marked in bold). According to the fold‐change values, 9 of these 27 metabolites, namely valine, oxalic acid, erythro‐sphingosine, 4,7,10,13,16,19‐docosahexaenoic acid, isoleucine, allo‐isoleucine, thyroxine, rac‐octanoyl carnitine, and tocopherol (vitamin E), were downregulated in the MHE group (compared to the NC group); the other 18, namely adenine, glycochenodeoxycholic acid, fucose, allothreonine, glycohyocholic acid, glycoursodeoxycholic acid, tyrosine, taurocheno‐deoxycholate, phenylalanine, 2‐hydroxy‐3‐methyl‐butanoic acid, hydroxyacetic acid, taurocholate, sorbitol, rhamnose, tauroursodeoxycholate, tolbutamide, pyroglutamic acid, and malic acid, were upregulated; 6 of these 34 metabolites were downregulated in the DS group (compared to the NC group), and the other 28 were upregulated, as shown in Table 5. (a) GC‐TOFMS and UPLC‐QTOFMS metabonomic analytical platforms can detect a range of metabolites in the serum; this might be of great help to study the pathogenesis of MHE and may provide a new approach for the early diagnosis of MHE. (b) Metabonomics analysis in combination with pattern recognition analysis might have great potential to distinguish the HBV‐induced liver cirrhosis patients who have MHE from the normal healthy population and HBV‐induced liver cirrhosis patients without MHE.