Machine Learning Approach and Bioinformatics Analysis Discovered Key Genomic Signatures for Hepatitis B Virus-Associated Hepatocyte Remodeling and Hepatocellular Carcinoma

Abstract

Hepatitis B virus (HBV) causes liver cancer, which is the third most common cause of cancer-related death worldwide. Chronic inflammation via HBV in the host hepatocytes causes hepatocyte remodeling (hepatocyte transformation and immortalization) and hepatocellular carcinoma (HCC). Recognizing cancer stages accurately to optimize early screening and diagnosis is a primary concern in the outlook of HBV-induced hepatocyte remodeling and liver cancer. Genomic signatures play important roles in addressing this issue. Recently, machine learning (ML) models and bioinformatics analysis have become very important in discovering novel genomic signatures for the early diagnosis, treatment, and prognosis of HBV-induced hepatic cell remodeling and HCC. We discuss the recent literature on the ML approach and bioinformatics analysis revealed novel genomic signatures for diagnosing and forecasting HBV-associated hepatocyte remodeling and HCC. Various genomic signatures, including various microRNAs and their associated genes, long noncoding RNAs (lncRNAs), and small nucleolar RNAs (snoRNAs), have been discovered to be involved in the upregulation and downregulation of HBV-HCC. Moreover, these genetic biomarkers also affect different biological processes, such as proliferation, migration, circulation, assault, dissemination, antiapoptosis, mitogenesis, transformation, and angiogenesis in HBV-infected hepatocytes.

Keywords: HBV; HCC; bioinformatics analysis; genomic signatures; machine learning approach.

Figure 1.

The analysis of large-scale genomic datasets using a machine learning approach to discover key genomic signatures for HBV-HCC and hepatocyte remodeling.

Figure 2.

The analysis of large-scale multi-omics datasets via a bioinformatics approach to discover key genomic signatures for HBV-HCC and hepatocyte remodeling.

Figure 3.

The application of bioinformatics analysis to determine the potential miRNA signals for HBV-HCC and hepatocyte remodeling from miRNA-miRNA interaction networks.