Machine learning-derived intracellular protein-based prognostic model reflecting a senescence-associated secretory phenotype-driven immunosuppressive microenvironment in lung adenocarcinoma

Background

This study aimed to investigate the associations of senescence-associated secretory phenotype (SASP)-related genes with the tumor immune microenvironment (TIME) and clinical outcomes in lung adenocarcinoma (LUAD) and to develop and evaluate a prognostic signature for risk stratification and exploratory treatment-response prediction.