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Construction of a Novel Gene-Based Model for Survival Prediction of Hepatitis B Virus Carriers With HCC Development
Yuan Huang1, Wen-Ling Tu2,3, Yan-Qiu Yao1
1Department of Biochemistry and Molecular Biology, School of Bioscience and Technology, Chengdu Medical College, Chengdu, China.
Insights
A new prognostic system using three genes (TP53, CFL1, UBA1) improves prediction of overall survival for hepatitis B virus carriers with liver cancer. This aids personalized treatment decisions for patients at risk of hepatocellular carcinoma.
Area of Science:
- Hepatology and Oncology
- Bioinformatics and Computational Biology
Background:
- Hepatitis B virus (HBV) vaccination reduces infection and liver disease, but many remain at risk for chronic infection, cirrhosis, and hepatocellular carcinoma (HCC).
- Accurate prognostication is crucial for managing HBV carriers at risk of HCC development and guiding personalized treatment strategies.
Purpose of the Study:
- To develop and validate a comprehensive prognostic system for predicting overall survival (OS) in hepatitis B virus carriers with HCC.
- To identify a robust gene signature for improved predictive accuracy in this patient population.
Main Methods:
- Utilized Weighted Gene Co-Expression Network Analysis (WGCNA), LASSO, SVM-RFE, and multivariate COX analysis to identify key prognostic genes.
- Developed a three-gene signature model (TP53, CFL1, UBA1) and calculated a gene-based risk score.
- Assessed prognostic power using time-dependent ROC (tROC) and Kaplan-Meier (KM) survival analysis; validated a nomogram integrating risk score and clinical factors.
Main Results:
- A three-gene signature (TP53, CFL1, UBA1) was identified as a significant predictor of overall survival in HBV-related HCC.
- The gene-based risk score effectively stratified patients based on survival outcomes.
- The integrated nomogram demonstrated strong predictive accuracy and calibration, validated in an independent dataset.
Conclusions:
- The developed prognostic system, based on a three-gene signature and clinical parameters, offers enhanced predictive accuracy for OS in HBV carriers with HCC.
- This tool can aid in clinical decision-making for personalized treatment strategies, potentially improving outcomes for at-risk populations.
Abstract:
Despite the effectiveness of hepatitis B virus (HBV) vaccination in reducing the prevalence of chronic HBV infection as well as the incidence of acute hepatitis B, fulminant hepatitis, liver cirrhosis and hepatocellular carcinoma (HCC), there was still a large crowd of chronically infected populations at risk of developing cirrhosis or HCC. In this study, we established a comprehensive prognostic system covering multiple signatures to elevate the predictive accuracy for overall survival (OS) of hepatitis B virus carriers with HCC development. Weighted Gene Co-Expression Network Analysis (WGCNA), Least Absolute Shrinkage and Selection Operator (LASSO), Support Vector Machine Recursive Feature Elimination (SVM-RFE), and multivariate COX analysis, along with a suite of other online analyses were successfully applied to filtrate a three-gene signature model (TP53, CFL1, and UBA1). Afterward, the gene-based risk score was calculated based on the Cox coefficient of the individual gene, and the prognostic power was assessed by time-dependent receiver operating characteristic (tROC) and Kaplan-Meier (KM) survival analysis. Furthermore, the predictive power of the nomogram, integrated with the risk score and clinical parameters (age at diagnosis and TNM stage), was revealed by the calibration plot and tROC curves, which was verified in the validation set. Taken together, our study may be more effective in guiding the clinical decision-making of personalized treatment for HBV carriers.
