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

Area of Science:

• Hepatology and Oncology
• Infectious Diseases
• Hematology

Background:

• Hepatitis B Virus (HBV) infection is a major cause of Hepatocellular Carcinoma (HCC).
• Acute bacterial infections can influence immune and hematological parameters, potentially confounding predictive models for HBV-associated HCC.
• Identifying reliable predictors is crucial for early detection and management of HBV-associated HCC.

Purpose of the Study:

• To identify optimal predictors of HBV-associated HCC using Lasso regression.
• To establish a robust prediction model for HBV-associated HCC.
• To investigate the influence of acute bacterial infections on predictive factors.

Main Methods:

• Retrospective analysis of 5441 patients with CBC and CRP testing.
• Exclusion of acute bacterial infections (CRP <10 mg/L).
• Application of Lasso and logistic regression on training and validation sets to identify predictors.

Main Results:

• Significant variations in age, gender, and blood parameters were observed across study groups.
• Optimal predictors for HBV-associated HCC included gender, age, MONO, EO%, MCHC, MPV, and PCT.
• Acute bacterial infections significantly impacted immune, erythrocyte, and platelet systems.

Conclusions:

• Acute bacterial infections can alter immune and hematological status, affecting predictive accuracy.
• Gender, age, MONO, EO%, MCHC, MPV, and PCT are effective predictors for HBV-associated HCC after excluding acute bacterial infections.
• The developed model aids in the clinical prediction of HCC development in HBV-infected patients.