Abstract
Background: Lung adenocarcinoma (LUAD) remains the most common type of lung cancer and is the main cause of cancer-related death worldwide. Reprogramming of glucose metabolism plays a crucial role in tumorigenesis and progression. However, the regulation of glucose metabolism is still being explored in LUAD. Determining the underlying clinical value of glucose metabolism will contribute in increasing clinical interventions. Our study aimed to conduct a comprehensive analysis of the landscape of glucose metabolism-related genes in LUAD and develop a prognostic risk signature. Methods: We extracted the RNA-seq data and relevant clinical variants from The Cancer Genome Atlas (TCGA) database and identified glucose metabolism-related genes associated with the outcome by correlation analysis. To generate a prognostic signature, least absolute shrinkage and selection operator (LASSO) Cox regression analysis was performed. Results: Finally, ten genes with expression status were identified to generate the risk signature, including FBP2, ADH6, DHDH, PRKCB, INPP5J, ABAT, HK2, GNPNAT1, PLCB3, and ACAT2. Survival analysis indicated that the patients in the high-risk group had a worse survival than those in the low-risk group, which is consistent with the results in validated cohorts. And receiver operating characteristic (ROC) curve analysis further validated the prognostic value and predictive performance of the signature. In addition, the two risk groups had significantly different clinicopathological characteristics and immune cell infiltration status. Notably, the low-risk group is more likely to respond to immunotherapy. Conclusion: Overall, this study systematically explored the prognostic value of glucose metabolism and generated a prognostic risk signature with favorable efficacy and accuracy, which help select candidate patients and explore potential therapeutic approaches targeting the reprogrammed glucose metabolism in LUAD.