Machine learning-based signature for prognosis and drug sensitivity in hepatocellular carcinoma using glycolysis-related gene.

ABSTRACT: Hepatocellular carcinoma (HCC) is one of the most prevalent tumors in the world and poses a considerable threat to global healthcare. The reprogramming of glucose metabolism in tumor cells has demonstrated a notable association with the genesis, advancement and resistance to the chemotherapy of malignancies. In the present study, 10 integrative machine learning algorithms were used to develop a glycolysis-related signature (GRS) using four datasets. Several predictive approaches were used to evaluate the performance of GRS in predicting the immunology response. In addition, in vitro experiments were performed to explore the biological functions of monocarboxylic acid transporter 1 (MCT1) in HCC. The optimal GRS developed by the Least Absolute Shrinkage and Selection Operator algorit

SUBMITTER: Zhou Y 

PROVIDER: S-EPMC12641473 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature