Construction of a Prognostic Model for Lung Adenocarcinoma Based on Bioinformatics Analysis of Glycolysis-Related Genes

Abstract

Objective: This study aims to collect lung adenocarcinoma samples from the Cancer Genome Atlas (TCGA) database and explore the differential expression of glycolysis-related genes between lung adenocarcinoma tissues and adjacent normal tissues. By combining differentially expressed genes with prognostic data, we investigate the correlation between them and establish a prognostic prediction model for the survival rate of lung adenocarcinoma. Methods: Raw expression data were downloaded from the TCGA database and organized using the Perl language. Differential analysis was performed using the “limma” package in R software. Univariate Cox regression analysis was employed to screen glycolysis-related genes associated with the survival of lung adenocarcinoma patients. Correlation analysis and consensus clustering analysis were then conducted. Lasso regression analysis and 10-fold cross-validation were used to screen glycolysis-related genes associated with prognosis. Kaplan-Meier survival curves were plotted to confirm significant differences between high- and low-risk groups, and the receiver operating characteristic (ROC) curve was plotted to calculate the area under the curve (AUC). Finally, a risk model was constructed. Results: Based on data from the TCGA database, 19 differentially expressed glycolysis-related genes were identified (17 upregulated and 2 downregulated). Univariate Cox regression analysis revealed that 14 genes were significantly associated with prognosis, among which five genes, including PGAM1 and NUP50, were identified as risk factors, while HK3 and PRKACA were protective factors. Following consensus clustering analysis, lung adenocarcinoma patients were classified into three subtypes. Survival analysis demonstrated significant prognostic differences among these subtypes, with subtype 2 exhibiting the worst prognosis. Using LASSO regression, 11 key glycolysis-related genes were selected, and a risk scoring model was constructed based on these genes. According to this model, patients were divided into high- and low-risk groups, revealing significant differences in survival rates between the two groups (P < 0.001). The ROC curve demonstrated the model’s good predictive ability for 1-, 2-, and 3-year survival rates (AUCs of 0.742, 0.725, and 0.673, respectively). Conclusion: This study found a correlation between glycolysis-related genes and the prognosis of lung adenocarcinoma. A risk scoring formula based on 11 key glycolysis-related genes was developed, and a risk model was constructed to predict the survival rate of lung adenocarcinoma patients using their risk scores along with T stage, N stage, and overall stage. This model provides valuable assistance for clinical research and individualized treatment of lung adenocarcinoma.