Abstract:
Objective To identify risk scoring models for assessing glycolysis-related genes in patients with lung adenocarcinoma (LUAD).
Methods A risk prediction model was constructed through gene set enrichment analysis (GSEA), differentially expressed genes (DEGs) analysis, and least absolute shrinkage and selection operator (LASSO) regression using The Cancer Genome Atlas (TCGA) public database for patients with LUAD transcriptomic data. The model prediction performance was further validated using Kaplan–Meier analysis, receiver operating characteristic (ROC) curve analysis, and univariate and multivariate Cox analyses. The CIBERSORT algorithm was used to calculate differences in immune cell infiltration between the high- and low-risk groups. Finally, a nomogram was constructed for clinical predictions of patient prognosis.
Results Three glycolysis-related gene sets were identified, and six glycolysis-related genes were needed to construct a risk score model. The overall survival rate was significantly lower in the high-risk group than in the low-risk group. The validation results revealed that the model had good predictive performance. Significant differences in immune cell infiltration were noted between the two groups. The constructed column line graphs developed a quantitative method that could predict the survival of patients with LUAD.
Conclusions The risk score models established based on glycolysis-related genes provide novel biomarkers for predicting the prognosis of patients with early stage LUAD.