Development and Validation of a Blood-Biomarker-Based Predictive Model for HBV-Associated Hepatocellular Carcinoma.

Abstract

Objective: This study aims to explore the optimal predictors of HBV-associated HCC using Lasso, and establish a prediction model.

Methods: A retrospective analysis was conducted on patients who underwent CBC and CRP testing between January 2016 and March 2024. The study population comprised 5441 cases divided into three cohorts: non-HBV-infected (1333 cases), HBV-infected (1023 cases), and HBV-associated HCC (3085 cases). A value of CRP <10 mg/L was used to exclude cases of acute bacterial infections. Baseline data and blood parameters were compared across the three groups (control group (n = 1049), the HBV-infected group (n = 789), and the HBV-associated HCC group (n = 1367)). HBV-infected group and the HBV-associated HCC group were used as modeling subjects which 70% were classified as training set (n = 1512) and 30% were classified as validation set (n = 644). Lasso regression and logistic regression were employed to identify the most effective predictors of HBV-associated HCC, which were subsequently incorporated into a predictive model by training set.

Results: Significant variations in age, gender, and blood parameter indices were observed between individuals with acute bacterial infections and non-infections in the study population, and also between three groups. The optimal predictors identified for HBV-associated HCC included gender, age, MONO, EO%, MCHC, MPV, and PCT.

Conclusions: The study highlights the significant impact of acute bacterial infections on immune status, erythrocyte system, and platelet system. After excluding acute bacterial infections, factors such as gender, age, MONO, EO%, MCHC, MPV, and PCT are effective predictors for clinical prediction of HCC development in HBV-infected patients.