A High-Throughput Sequencing Data-Based Classifier Reveals the Metabolic Heterogeneity of Hepatocellular Carcinoma.

ABSTRACT: Metabolic heterogeneity plays a key role in poor outcomes in malignant tumors, but its role in hepatocellular carcinoma (HCC) remains largely unknown. In the present study, we aim to disentangle the metabolic heterogeneity features of HCC by developing a classification system based on metabolism pathway activities in high-throughput sequencing datasets. As a result, HCC samples were classified into two distinct clusters: cluster 1 showed high levels of glycolysis and pentose phosphate pathway activity, while cluster 2 exhibited high fatty acid oxidation and glutaminolysis status. This metabolic reprogramming-based classifier was found to be highly correlated with several clinical variables, including overall survival, prognosis, TNM stage, and 𝛼-fetoprotein (AFP) expression. Of note, acti

SUBMITTER: Ye M 

PROVIDER: S-EPMC9913608 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature