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

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, <i>in vitro</i> 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 algorithm served as a risk factor for patients with HCC. Patients with HCC and a high-risk score experienced a poor prognosis, with the area under the curves of 1, 3 and 5-year receiver operating characteristic curves being 0.777, 0.787 and 0.766, respectively. A low-risk score indicated higher levels of CD8⁺ cytotoxic T cells and M1 macrophages, as well as an increased estimation of stromal and immune cells in malignant tumors score. Moreover, increased tumor mutational burden score and programmed cell death protein 1 and cytotoxic T-lymphocyte-associated protein 4 immunophenoscores, as well as decreased Tumor Immune Dysfunction and Exclusion and tumor escape scores were found in patients with HCC that had low-risk scores. The IC₅₀ values of docetaxel, oxaliplatin, crizotinib and osimertinib were lower in HCC cases with a high-risk score. In addition, the gene set scores that were associated with angiogenesis and Notch signaling were higher in the high-risk score group. Downregulation of MCT1 inhibited the proliferation, migration and invasion of HCC cells and promoted the apoptosis of HCC cells. In conclusion, the present study developed a novel GRS for HCC, serving as an indicator for predicting clinical outcomes and responses to immunotherapy.