PeerJ
Machine learning-derived intracellular protein-based prognostic model reflecting a senescence-associated secretory phenotype-driven immunosuppressive microenvironment in lung adenocarcinoma
By Chang X, Jiang L, Zhang L and 3 others.
Abstract
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.
Methods
Non-negative matrix factorization (NMF) was conducted to identify distinct molecular subtypes. Least absolute shrinkage and selection operator (LASSO), Random Forest, and extreme gradient boosting (XGBoost) were utilized to identify core prognostic SASP genes. The expression patterns of selected SASP-related genes were explored using multi-omics datasets, LUAD cell lines, and clinical specimens.