Abstract
Lung adenocarcinoma (LUAD), characterized by its heterogeneity and complex pathogenesis, is the focus of this study which investigates the association between cell death-related genes and LUAD. Through machine learning, a risk score model was developed using the Coxboost rsf algorithm, demonstrating strong prognostic accuracy in both validation (GSE30219, GSE31210, GSE72094) and training (TCGA-LUAD) datasets with C-indices of 0.93, 0.67, 0.68, and 0.64, respectively. The study reveals that the expression of Keratin 18 (KRT18), a key cytoskeletal protein, varies across LUAD cell lines (DV-90, PC-9, A549) compared to normal bronchial epithelial cells (BEAS-2B), suggesting its potential role in LUAD's pathogenesis. Kaplan-Meier survival curves further validate the model, indicating longer survival in the low-risk group. A comprehensive analysis of gene expression, functional differences, immune infiltration, and mutations underscores significant variations between risk groups, highlighting the high-risk group's immunological dysfunction. This points to a more intricate tumor immune environment and the possibility of alternative therapeutic strategies. The study also delves into drug sensitivity, showing distinct responses between risk groups, underscoring the importance of risk stratification in treatment decisions for LUAD patients. Additionally, it explores KRT18's epigenetic regulation and its correlation with immune cell infiltration and immune regulatory molecules, suggesting KRT18's significant role in the tumor immune landscape. This research not only offers a valuable prognostic tool for LUAD but also illuminates the complex interplay between cell death-related genes, drug sensitivity, and immune infiltration, positioning KRT18 as a potential therapeutic or prognostic target to improve patient outcomes by personalizing LUAD treatment strategies.