Identification of Biomarkers for Response to Interferon in Chronic Hepatitis B Based on Bioinformatics Analysis and Machine Learning

Interferon (IFN) is a pivotal agent against hepatitis B virus (HBV) in clinic, but there is a lack of accurate biomarkers to predict the response to IFN therapy in patients with chronic hepatitis B (CHB). Our study aimed to investigate potential targets for IFN therapy and to explore the network of interactions associated with IFN response. MicroRNA (miRNA) (GSE29911) and messenger RNA (GSE27555) datasets were used to screen the differentially expressed miRNAs (DEmiRNAs) and differentially expressed genes (DEGs). The random forest and k-nearest neighbors algorithm were used to further screen the core DEmiRNAs and build a prediction model. A Protein-Protein Interaction (PPI) network based on the STRING database was constructed and visualized by the Cytoscape software. Then, we collected transcription factors (TFs) from the TransmiR database to construct the TF-miRNA-hub gene regulatory network. Finally, real-time quantitative polymerase chain reaction was used to verify the expression of four miRNAs in HepG2-NTCP and Huh-7, and the effect of IFN treatment on four miRNAs' expression was preliminarily explored. Eighteen DEmiRNAs in GSE29911 and 700 DEGs in GSE27555 were identified. Boruta feature selection identified four miRNAs (miR-873, miR-200a, miR-30b, and let-7g) from 18 DEmiRNAs. We identified 48 TFs, 4 miRNAs, and 10 hub genes and constructed a TF-miRNA-hub gene network to suggest the mechanism of IFN response. According to the experimental results, miR-873 was upregulated and IFN treatment could inhibit it in HBV-transfected cells (p < 0.05). We constructed a TF-miRNA-hub gene regulatory network, and our results demonstrate that miR-873 was identified as a potential biomarker of IFN response in patients with CHB. This information provides an initial basis for understanding the complex IFN response regulatory mechanisms.

chronic hepatitis B; interferon response; miR-873; regulatory network.