A novel autoantibody panel as potential diagnostic markers for hepatocellular carcinoma.

AIMS: Hepatocellular carcinoma (HCC) represents a major global health burden. Tumor-associated autoantibodies (TAAs) represent promising biomarkers for cancer detection. This study aims to evaluate the diagnostic value of autoantibody panels in HCC. PATIENTS AND METHODS: Candidate antigens were identified via multi-omics screening (Gene Expression Omnibus (GEO), Gene Expression Profiling Interactive Analysis (GEPIA), Clinical Proteomic Tumor Analysis Consortium (CPTAC), Human Protein Atlas (HPA)) and validated by enzyme-linked immunosorbent assay (ELISA) in 280 HCC patients and 280 controls. Diagnostic models were constructed using eight machines learning algorithms. RESULTS: A total of 10 TAAs were identified, with AUCs ranging from 0.610 to 0.729. Logistic regression (LR) was identified as the optimal model. The LR model predicted that the positive rate of early HCC (62.39%) was significantly higher than that of AFP (47.71%). Notably, this model demonstrated superior predictive capability for AFP-negative HCC (AUC = 0.751). Combining the LR model with AFP for diagnosis achieved a positive rate of 96.36%, significantly higher than the 64.78% positive rate obtained with AFP alone. CONCLUSION: This novel serum autoantibody panel serves as a valuable diagnostic biomarker. Its combination with AFP significantly reduces missed diagnoses, offering a promising strategy to optimize HCC screening.