Integration and deconvolution methodology deciphering prognosis-related signatures in lung adenocarcinoma

Abstract

Abstract Purpose To develop a machine learning classifier using 16 prognosis-related genes (PRGs) to stratify lung adenocarcinoma (LUAD) patients according to their risk score. Validate the model's performance, interpretability and generalizability. Investigate the role of PRGs in LUAD prognosis through single-cell and bulk transcriptome analysis. Methods We constructed a machine learning classifier based on 16 PRGs to categorize LUAD patients by risk score. We combined the risk score with clinicopathological factors to predict overall survival. We assembled a single-cell atlas of LUAD comprising over 50,000 cells to identify cell types linked to prognosis and studied PRG expression in different cell types. We analyzed PRG involvement in tumor pathways and interactions using gene set variation analysis (GSVA). Results The machine learning classifier combining risk score and clinicopathological factors showed strong performance in predicting overall survival. The single-cell atlas revealed that epithelial cells were primarily associated with prognosis. PRGs were predominantly expressed in malignant epithelial cells and influenced epithelial cell growth and progression, especially in tumor states. PRGs were involved in tumor pathways like epithelial-mesenchymal transition, hypoxia and KRAS_UP. High PRG GSVA scores correlated with worse outcomes in LUAD patients. Conclusions The model provides a valuable tool for clinicians to personalize LUAD treatment based on risk stratification. The study elucidated the biological basis of PRG signatures in LUAD through integrated single-cell and bulk transcriptome analysis, contributing to a better understanding of LUAD prognosis and guiding targeted therapy development.