4210 Da and 1866 Da polypeptides as potential biomarkers of liver disease progression in hepatitis B virus patients

Abstract

Abstract HBV infection is recognized as a serious global health problem, and hepatitis B virus infection is a complicated chronic disease leading to liver cirrhosis (LC) and hepatocellular carcinoma (HCC). New biochemical serum markers could be used to advance the diagnosis and prognosis of HBV-associated liver diseases during the progression of chronic hepatitis B into cirrhosis and HCC. We determined whether the 4210 Da and 1866 Da polypeptides are serum metabolite biomarkers of hepatopathy with hepatitis B virus. A total of 570 subjects were divided into five groups: healthy controls, those with natural clearance, and patients with CHB, LC, and HCC. The 1866 Da and 4210 Da polypeptides were measured by Clin-ToF II MALDI-TOF–MS. There were significant differences in 4210 Da and 1866 Da levels among the five groups (P < 0.001). For the differential diagnosis of CHB from normal liver, the areas under the receiver operating characteristic (ROC) curve of 4210 Da and 1866 Da and their combination via logistic regression were 0.961, 0.849 and 0.967. For the differential diagnosis of LC from CHB, the areas under the ROC curve were 0.695, 0.841 and 0.826. For the differential diagnosis of HCC from CHB, the areas under the ROC curve were 0.744, 0.710 and 0.761, respectively. For the differential diagnosis of HCC from LC, the areas under the ROC curve of 4210 Da and 1866 Da were 0.580 and 0.654. The positive rate of 1866 Da was 45.5% and 69.0% in AFP-negative HCC patients and that of 4210 Da was 60.6% 58.6% in AFP-negative HCC patients of the study HCC vs. CHB and HCC vs. LC. The 4210 Da and 1866 Da polypeptide levels were positively correlated with HBV DNA levels (P < 0.001, r = 0.269; P < 0.001, r = 0.285). The 4210 Da and 1866 Da polypeptides had good diagnostic value for the occurrence and progression of HBV-related chronic hepatitis, liver cirrhosis and hepatocellular carcinoma and could serve to accurately guide treatment management and predict clinical outcomes.