Metabolic Reprogramming and Risk Stratification of Hepatocellular Carcinoma Studied by Using Gas Chromatography-Mass Spectrometry-Based Metabolomics

Simple Summary Hepatocellular carcinoma (HCC) displays dismal prognosis even after surgical resection. Metabolic reprogramming is a hallmark of cancers, but the existence of tumor heterogeneity makes it difficult to comprehensively reflect the overall characteristics of HCC prognosis with only a single or a few biomarkers. The aim of our study was to elucidate HCC metabolic reprogramming based on metabolomics and enable HCC prognostic risk evaluation using metabolic characteristics. We identified three distinct metabolic clusters and a metabolite classifier composed of six fatty acids for HCC prognosis risk stratification, which was externally validated in another independent dataset. Metabolic classification may provide a new insight into the molecular pathological characteristics of HCC for clinical prognosis evaluation and personalized treatment. Hepatocellular carcinoma (HCC) displays a high degree of metabolic and phenotypic heterogeneity and has dismal prognosis in most patients. Here, a gas chromatography-mass spectrometry (GC-MS)-based nontargeted metabolomics method was applied to analyze the metabolic profiling of 130 pairs of hepatocellular tumor tissues and matched adjacent noncancerous tissues from HCC patients. A total of 81 differential metabolites were identified by paired nonparametric test with false discovery rate correction to compare tumor tissues with adjacent noncancerous tissues. Results demonstrated that the metabolic reprogramming of HCC was mainly characterized by highly active glycolysis, enhanced fatty acid metabolism and inhibited tricarboxylic acid cycle, which satisfied the energy and biomass demands for tumor initiation and progression, meanwhile reducing apoptosis by counteracting oxidative stress. Risk stratification was performed based on the differential metabolites between tumor and adjacent noncancerous tissues by using nonnegative matrix factorization clustering. Three metabolic clusters displaying different characteristics were identified, and the cluster with higher levels of free fatty acids (FFAs) in tumors showed a worse prognosis. Finally, a metabolite classifier composed of six FFAs was further verified in a dependent sample set to have potential to define the patients with poor prognosis. Together, our results offered insights into the molecular pathological characteristics of HCC.