Screening of Cancer-Specific Biomarkers for Hepatitis B-Related Hepatocellular Carcinoma Based on a Proteome...

Hepatocellular carcinoma (HCC) is associated with one of the highest mortality rates among cancers, rendering its early diagnosis clinically invaluable. Serum biomarkers, specifically alpha-fetoprotein (AFP), represent the most promising and widely used diagnostic biomarkers for HCC. However, its detection rate is low in the early stages of HCC progression, and distinguishing specific false positives for other liver-related diseases, such as cirrhosis and acute hepatitis, remains challenging. Therefore, this study was conducted to identify biomarkers for hepatitis B (HBV)-related liver diseases by screening differentially expressed autoantibodies against tumor-associated antigens (TAAbs). We designed a large-scale multistage investigation, encompassing initial screening, HCC-focused, and ELISA validation cohorts to identify potential TAAbs in HBV-related liver diseases, spanning from healthy control (HC) individuals to patients with chronic hepatitis B (CHB), hepatitis B-related cirrhosis (HBC), and HCC, using protein microarray technology. The differential biological characteristics of TAAbs were analyzed using bioinformatics analysis. Validation of tumor-specific biomarkers for HCC was performed using ELISA. In the screening cohort, 547 candidate TAAbs were identified in the HCC group compared to those in the HC group. In the HCC-focused cohort, 64, 61, and 65 candidate TAAbs were identified in the CHB, HBC, and HCC groups, respectively, compared to those in the HC group. Thirty-four proteins exhibited continuously elevated expression from HCs to patients with CHB, HBC, and HCC. Among these, nine were identified as cancer-specific proteins. In the validation cohort, UBE2Z, CNOT3, and EID3 were correlated with liver function indicators in patients with hepatitis B-related HCC. Overall, UBE2Z, CNOT3, and EID3 emerged as cancer-specific biomarkers for HBV-related liver disease, providing a scientific basis for clinical application.