Defining lung adenocarcinoma subtypes with glucocorticoid-related genes and constructing a prognostic index for immunotherapy guidance.

Background: Several studies have shown that glucocorticoid-related genes (GCGs) play a crucial role in cancer. However, the mechanism of GCGs in lung adenocarcinoma (LUAD) is not fully understood. This study aimed to identify distinct subtypes of LUAD by integrating GCGs and to develop prognostic models for precise prognosis prediction and immunotherapy guidance. Methods: In this study, sample data of LUAD were collected from The Cancer Genome Atlas (TCGA) database, and unsupervised clustering was used to identify LUAD subtypes with different GCGs characteristics. Survival-related genes were screened by differential expression analysis and protein-protein interaction (PPI) network analysis. After that, the least absolute shrinkage and selection operator (LASSO) combined with Cox regression analysis was used to establish the prognosis model. Differences in the immune microenvironment of different risk groups were analyzed, and Tumor Immune Dysfunction and Exclusion (TIDE) was used to predict the response of patients to immunotherapy. Finally, the CellMiner database was used to predict potential drugs. Results: Two subtypes of LUAD were identified, namely cluster 1 (high survival rate) and cluster 2 (low survival rate). A prognostic model was constructed based on 9 characteristic genes, including Conclusions: Our study elucidates the impact of GCGs on LUAD prognosis and immune responses, offering insights for prognostic forecasting and immunotherapeutic strategies for LUAD patients.