Assessment and prediction of prognostic risk in patients with hepatocellular carcinoma (HCC) would greatly benefit the optimal treatment selection. Here, we aimed to identify the critical metabolites associated with the outcomes and develop a risk score to assess the prognosis of HCC patients after curative resection. A total of 78 serum samples of HCC patients were analyzed by liquid chromatography-mass spectrometry to characterize the metabolic profiling. A novel network-based feature selection method (NFSM) was developed to define the critical metabolites with the most discriminant capacity to outcomes. The metabolites defined by NFSM was further reduced by Cox regression analysis to generate a prognostic metabolite panel-phenylalanine and choline. Furthermore, univariate and multivariate Cox regression analyses were applied to combine the metabolite panel with the presence of satellite nodes to generate a global prognostic index (GPI) score for overall survival assessment. Compared with the current clinical classification systems, including the Barcelona-clinic liver cancer stage, tumor-node-metastasis stage, and albumin-bilirubin grade, the GPI score presented comparable performance, according to the time-dependent receiver operating characteristic curves and was validated in an independent cohort, which suggested that metabolomics could serve as a helpful tool to stratify the HCC prognostic risk after operation.
Keywords: global prognostic index; hepatocellular carcinoma; metabolomics; network-based feature selection method; prognosis.