CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, 116023, Liaoning, China.
University of Chinese Academy of Sciences, Beijing, 100049, China.
Anal Bioanal Chem. 2021 May;413(12):3153-3165. doi: 10.1007/s00216-021-03281-z. Epub 2021 Apr 2.
Comprehensive prognostic risk prediction of hepatocellular carcinoma (HCC) after surgical treatment is particularly important for guiding clinical decision-making and improving postoperative survival. Hence, we aimed to build prognostic models based on serum metabolomics data, and assess the prognostic risk of HCC within 5 years after surgical resection. A pseudotargeted gas chromatography-mass spectrometry (GC-MS)-based metabolomics method was applied to analyze serum profiling of 78 HCC patients. Important metabolic features with discriminant ability were identified by a novel network-based metabolic feature selection method based on combinational significance index (N-CSI). Subsequently, phenylalanine and galactose were further identified to be relevant with mortality by the Cox regression analysis, while galactose and tyrosine were associated with recurrence and metastasis. Two models to predict risk of mortality (risk score of overall survival, RS) and risk of recurrence and metastasis (risk score of disease-free survival, RS) were generated based on two panels of metabolites, respectively, which present favorable ability to predict prognosis of HCC, especially when combined with clinical staging system. The performance of models was further validated in an external independent cohort from 91 HCC patients. This study demonstrated that metabolomics is a powerful tool for risk screening of HCC prognosis.
综合预测肝癌（HCC）患者手术后的预后风险对于指导临床决策和改善术后生存至关重要。因此，我们旨在建立基于血清代谢组学数据的预后模型，并评估 HCC 患者手术后 5 年内的预后风险。采用基于伪靶向气相色谱-质谱（GC-MS）的代谢组学方法分析了 78 例 HCC 患者的血清特征。通过基于组合显著性指数（N-CSI）的新型网络代谢特征选择方法，确定了具有判别能力的重要代谢特征。随后，通过 Cox 回归分析发现苯丙氨酸和半乳糖与死亡率相关，而半乳糖和酪氨酸与复发和转移相关。基于两组代谢物，分别建立了预测死亡率风险（总生存风险评分，RS）和复发转移风险（无病生存风险评分，RS）的模型，这些模型在预测 HCC 预后方面表现出良好的能力，尤其是与临床分期系统相结合时。在来自 91 例 HCC 患者的外部独立队列中进一步验证了模型的性能。本研究表明，代谢组学是筛选 HCC 预后风险的有力工具。
