Subtype Classification, Immune Infiltration, and Prognosis Analysis of Lung Adenocarcinoma Based on Pyroptosis-Related Genes

Abstract

The effect of pyroptosis-related genes (PRGs) on the tumor microenvironment (TME) in lung adenocarcinoma (LUAD) remains unclear. Thus, this study is aimed at evaluating the prognostic value of PRGs in patients with LUAD and to elucidate their role in the TME and their effect on immunotherapy. Transcriptomic and clinical data were obtained from the Cancer Genome Atlas and the Gene Expression Omnibus databases (GSE3141, GSE31210). Patients with LUAD were classified using consistent clustering, and the differences in the TME for each type were determined using the ESTIMATE and CIBERSORT algorithms. PRGs were screened using univariate regression analysis, and a prognostic risk model was constructed using LASSO regression analysis. The tumor mutational burden and the tumor immune dysfunction and exclusion algorithms were used to predict therapeutic sensitivity in LUAD patients. Then, we evaluated the potential therapeutic interventions using the GDSC database. LUAD patients in cluster 2 had significantly shorter overall survival and progression-free survival rates, lower immune scores, and higher infiltration of T follicular helper cells than those in cluster 1. We used five PRGs to classify patients with LUAD into different risks groups and found that the high-risk group is sensitive to immunotherapy; however, its immune-related pathways were inhibited, which may be related to tumor metabolic reprogramming. Lastly, we identified several potential therapeutic drugs for application in low-risk patients who were less sensitive to immunotherapy. Overall, our findings showed that PRGs can be used to predict prognosis and may aid in the development of personalized therapeutic strategies in LUAD patients.