Work

Serum Protein Panel Facilitates Surgical Decision-Making for Barcelona Stage B Hepatocellular Carcinoma

Year: 2026

Type: article

Abstract: To address the clinical heterogeneity that limits uniform treatment recommendations, we developed a serum proteomic-based prognostic model for predicting surgical outcomes in patients with Barcelona Clinic Liver Cancer (BCLC) stage B hepatocellular carcinoma (HCC). Preoperative serum samples from patients with HCC undergoing curative resection were retrospectively collected and profiled using nanoparticle-enhanced data-independent acquisition mass spectrometry. A prognostic risk model was developed in a training cohort (N = 52, BCLC stage B) using 5-fold cross-validated Lasso-Cox machine learning, with overall survival as the primary endpoint. The model was validated in an independent BCLC stage B cohort (N = 22), extended to a single huge HCC cohort (N = 26, BCLC stage A with a solitary tumor ≥10 cm), and benchmarked against a comparison cohort (N = 190, BCLC stage 0/A without huge HCC). Orthogonal validation was performed by parallel reaction monitoring in an exploration cohort (N = 51, BCLC stage B). Among 2381 identified proteins, a three-protein panel (LCP1, DOK3, and CNN2) was selected. The model showed strong predictive accuracy for 3-year overall survival in the training cohort (area under the curve [AUC] = 0.936, sensitivity = 0.988, specificity = 0.834), outperforming the clinical Kinki criteria. It also performed well in the validation cohort (AUC = 0.812, sensitivity = 0.875, specificity = 0.786) and the extended cohort of huge HCC patients (AUC = 0.758, sensitivity = 0.709, specificity = 0.852). Notably, low-risk BCLC stage B patients showed prognosis comparable to the comparison cohort, suggesting potential surgical benefit. Parallel reaction monitoring validation confirmed the model's prognostic value (AUC = 0.800, sensitivity = 0.870, specificity = 0.700). This serum proteomic-based model accurately predicts surgical outcomes for BCLC stage B HCC and outperforms clinical stratification. By identifying low-risk patients who may benefit from surgery, it facilitates personalized treatment planning and may improve survival and quality of life.

Source: Molecular & Cellular Proteomics

DOI: 10.1016/j.mcpro.2026.101674