Abstract
Interferon-alpha (IFN-α) therapy of chronic hepatitis B (CHB) patients is constrained by limited response and side effects. We described a panel of circulating microRNAs (miRNAs) which could potentially predict outcome of IFN-α therapy. Here, we report development of a simplified scoring model for personalized treatment of CHB patients. 112 CHB patients receiving IFN-α treatment were randomly divided into a training (n = 75) or a validation group (n = 37). The expression of 15 candidate miRNAs was detected in training group with 5 miRNAs exhibiting significantly different levels (p < 0.0001) between early virological response (EVR) and non-early virological response (N-EVR). These 5 miRNAs were further tested in validation phase. Refinement analyses of results from training phase established a model composed of miR-210, miR-22 and alanine aminotransferase (ALT), with area under ROC curve (AUC) of 0.874 and 0.816 in training and validation groups, respectively. In addition, this model showed prognostic value for sustained virological response (SVR) (AUC = 0.821). Collectively, this simplified scoring model composed of miR-210, miR-22 and ALT can reproducibly predict the EVR and SVR of IFN-α therapy in CHB patients. The model should help to forecast the outcome of IFN-α treatment prior to therapy decision involving nucleoside analogs or IFNs.
Highlights
Hepatitis B is a major global health problem
Treatment of chronic hepatitis B (CHB) patients with IFN-α/ Peg-IFN-α increases the rate of hepatitis B surface antigen (HBsAg) clearance and improves the seroconversion rate of hepatitis B e antigen (HBeAg)[3,4,5]
It is well recognized that the host and viral factors, including baseline serum alanine aminotransferase (ALT), baseline Hepatitis B virus (HBV) DNA, HBV genotype, patients’ gender and a dynamics of surface antigen titer may influence the sustained response to IFN-α treatment of CHB patients[9,10,11,12]
Summary
Hepatitis B is a major global health problem. Infection by the Hepatitis B virus (HBV) can lead to multiple stages of liver disease including asymptomatic HBV carrier state, chronic hepatitis B (CHB), liver cirrhosis, liver failure and hepatocellular carcinoma[1,2]. It is well recognized that the host and viral factors, including baseline serum alanine aminotransferase (ALT), baseline HBV DNA, HBV genotype, patients’ gender and a dynamics of surface antigen titer may influence the sustained response to IFN-α treatment of CHB patients[9,10,11,12]. In view of such complexity, a discovery of novel biomarkers facilitating the prediction of IFN-α treatment outcomes is of paramount importance. The present study was undertaken with the aim to extend these earlier findings by further validating the prediction efficacy of identified plasma miRNAs and to construct a simplified scoring model, which could be used to improve personalized treatment of CHB patients
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