Clinical significance and immunogenomic landscape analysis of glycolysis-associated prognostic model to guide clinical therapy in hepatocellular carcinoma.

Background: Hepatocellular carcinoma (HCC) is a common malignant tumor with a poor prognosis and high mortality rate worldwide. Glucose metabolism disorder is one of the most important characteristics of HCC. However, as the primary risk factors for the prognosis of HCC patients are unclear, the survival prognosis and therapy response of patients cannot be accurately predicted. Methods: First, gene sets of 29 cancer hallmarks were collected from public databases. The z-score of various cancer hallmarks were quantitively analyzed by a single-sample gene set enrichment analysis (ssGSEA) of HCC patients. Next, a glycolysis-related gene signature (GRS) was constructed using a series of bioinformatics methods, which were used to predict the survival prognosis of HCC patients and the immunotherapy benefits. The prediction accuracy of the GRS was validated in different HCC cohorts and clinical subgroups. Additionally, a decision tree and nomogram were also established based on the GRS and other clinical variables. Finally, the genomic alterations and tumor immune microenvironment of the HCC patients were examined. Results: Among the 29 cancer hallmarks, glycolysis was the most predominant risk factor for a poor prognosis in HCC. We subsequently constructed a novel GRS comprising 12 glycolysis-related genes. The high-GRS patients had a poorer survival prognosis than the low-GRS patients. The GRS exhibited a powerful ability to predict survival prognosis in different HCC cohorts and clinical feature subgroups. Additionally, the decision tree and nomogram aided in the risk stratification and prognosis evaluations of HCC patients. Further, we found that a high GRS was characterized by a severe tumor stage, pathological grade, and other clinical features. There were significant differences in the genomic alterations, immune cells, and immune checkpoints between the low- and high-GRS patients, especially in relation to the tumor protein p53 mutation and immunosuppressive cells. Notably, we also found that the GRS could be used to identify HCC patients who are more sensitive to chemotherapy and immunotherapy. Conclusions: In summary, the GRS may be a useful tool for predicting the prognosis and guiding the clinical therapy of HCC patients.