Abstract
Hepatitis B virus (HBV) infection is a global public health challenge that alters the immune microenvironment of the liver and drives disease progression by triggering chronic inflammation that leads to hepatic cell death through multiple programmed cell death (PCD) modalities. Due to the persistence of covalently closed circular DNA in hepatocytes, there is a lack of curative drugs that can completely eradicate HBV. Therefore, revealing how HBV infection leads to changes in the hepatic immune microenvironment, as well as searching for specific molecular targets, is crucial for controlling the onset and progression of chronic hepatitis B (CHB). In this study, we used the single sample gene set enrichment analysis and CIBERSORT algorithms to assess immune cell infiltration in the livers of CHB patients. With three advanced machine learning algorithms, random forest, least absolute shrinkage and selection operator, and selected support vector machine recursive feature elimination, we identified the PCD signature genes associated with CHB from the candidate genes. We further validated that ubiquitin-specific peptidase 21 could differentiate CHB patients with different natural courses by receiver operating characteristic analysis. These findings enhance our understanding of the mechanisms of HBV infection.