Molecular & cellular proteomics : MCP · 2026
Serum Protein Panel Facilitates Surgical Decision-Making for Barcelona Stage B Hepatocellular Carcinoma.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Wang Jin-Hua, Ren Liang-Liang, Zhang Wan-Jun, Xing Xiao-Hua, Li Jun-Qing, Li Chao-Ying, Qin Wei-Jie, Liu Xiao-Long, Jiang Ying
Аннотация
PURPOSE: To develop a serum proteomic-based prognostic model for predicting surgical outcomes in BCLC stage B hepatocellular carcinoma (HCC) patients, addressing the clinical heterogeneity that limits uniform treatment recommendations.
METHODS: Preoperative serum samples from HCC patients undergoing curative resection were retrospectively collected and subjected to proteomic profiling using nanoparticle-enhanced data-independent acquisition mass spectrometry (DIA-MS). 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 (OS) 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 HCC with a solitary tumor measuring at least 10 cm), and benchmarked against a comparison cohort (N = 190, BCLC 0/A without huge HCC). Orthogonal validation was performed by parallel reaction monitoring (PRM) in an exploration cohort (N = 51, BCLC stage B HCC).
RESULTS: Among 2,381 identified proteins, a three-protein panel (LCP1, DOK3, CNN2) was selected to construct the model. The model demonstrated strong predictive accuracy for 3-year OS in the training cohort (AUC = 0.936, sensitivity = 0.988, specificity = 0.834), outperforming the clinical Kinki criteria, in the validation cohort (AUC = 0.812, sensitivity = 0.875, specificity = 0.786) and in the extended cohort of huge HCC patients (AUC = 0.758, sensitivity = 0.709, specificity = 0.852). Notably, low-risk BCLC stage B patients showed comparable prognosis to the comparison cohort of BCLC 0/A patients, suggesting potential surgical benefit. The PRM validation in an independent exploration cohort confirmed the model's prognostic value (AUC = 0.800, sensitivity = 0.870, specificity = 0.700).
CONCLUSIONS: This serum proteomic-based prognostic model accurately predicts surgical outcomes for BCLC stage B HCC patients and outperforms clinical stratification. By identifying low-risk patients who may benefit from surgery, this tool facilitates personalized treatment planning and has the potential to improve survival and quality of life.
METHODS: Preoperative serum samples from HCC patients undergoing curative resection were retrospectively collected and subjected to proteomic profiling using nanoparticle-enhanced data-independent acquisition mass spectrometry (DIA-MS). 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 (OS) 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 HCC with a solitary tumor measuring at least 10 cm), and benchmarked against a comparison cohort (N = 190, BCLC 0/A without huge HCC). Orthogonal validation was performed by parallel reaction monitoring (PRM) in an exploration cohort (N = 51, BCLC stage B HCC).
RESULTS: Among 2,381 identified proteins, a three-protein panel (LCP1, DOK3, CNN2) was selected to construct the model. The model demonstrated strong predictive accuracy for 3-year OS in the training cohort (AUC = 0.936, sensitivity = 0.988, specificity = 0.834), outperforming the clinical Kinki criteria, in the validation cohort (AUC = 0.812, sensitivity = 0.875, specificity = 0.786) and in the extended cohort of huge HCC patients (AUC = 0.758, sensitivity = 0.709, specificity = 0.852). Notably, low-risk BCLC stage B patients showed comparable prognosis to the comparison cohort of BCLC 0/A patients, suggesting potential surgical benefit. The PRM validation in an independent exploration cohort confirmed the model's prognostic value (AUC = 0.800, sensitivity = 0.870, specificity = 0.700).
CONCLUSIONS: This serum proteomic-based prognostic model accurately predicts surgical outcomes for BCLC stage B HCC patients and outperforms clinical stratification. By identifying low-risk patients who may benefit from surgery, this tool facilitates personalized treatment planning and has the potential to improve survival and quality of life.