Construction of HBV-HCC prognostic model and immune characteristics based on potential genes mining through protein interaction networks

Objective To search for human protein-coding genes related to hepatocellular carcinoma (HCC) in the context of hepatitis B virus (HBV) infection, and perform prognosis risk assessment. Methods Genes related to HBV-HCC were selected through literature screening and protein–protein interaction (PPI) network database analysis. Prognosis potential genes (PPGs) were identified using Cox regression analysis. Patients were divided into high-risk and low-risk groups based on PPGs, and risk scores were calculated. Kaplan–Meier plots were used to analyze overall survival rates, and the results were predicted based on clinicopathological variables. Association analysis was also conducted with immune infiltration, immune therapy, and drug sensitivity. Experimental verification of the expression of PPGs was done in patient liver cancer tissue and normal liver tissue adjacent to tumors. Results The use of a prognosis potential genes risk assessment model can reliably predict the prognosis risk of patients, demonstrating strong predictive ability. Kaplan–Meier analysis showed that the overall survival rate of the low-risk group was significantly higher than that of the high-risk group. There were significant differences between the two subgroups in terms of immune infiltration and IC50 association analysis. Experimental verification revealed that CYP2C19, FLNC, and HNRNPC were highly expressed in liver cancer tissue, while UBE3A was expressed at a lower level. Conclusion PPGs can be used to predict the prognosis risk of HBV-HCC patients and play an important role in the diagnosis and treatment of liver cancer. They also reveal their potential role in the tumor immune microenvironment, clinical-pathological characteristics, and prognosis.