Prognostic Modeling of Lysosomal Cell Death Signatures in Lung Adenocarcinoma: A Machine Learning Framework for Survival Outcome Prediction
Runzhe Wang, Qingtao Zhao, Shicheng Liu, Lingxin Kong, Dahu Ren, Jianghong Yan, Shuo Yang, Guochen DuanBackground
Lung cancer remains a leading cause of cancer‐related mortality worldwide. This study provides a comprehensive characterization of the transcriptomic landscape, cellular composition, and immune architecture of lung adenocarcinoma using multiomics approaches.
Methods
Gene expression data were obtained from GEO (GSE131907) and TCGA‐LUAD ( n = 526). LRCD gene sets were curated from GeneCards, MSigDB, and published literature. Analyses included differential expression profiling, single‐cell RNA sequencing (GSE131907, n = 11,376 cells), GSEA, and cell‐cell communication mapping. A LASSO‐based prognostic model with 10‐fold cross‐validation was built and validated across three independent cohorts using ROC and Kaplan–Meier analyses.
Results
Fourteen prognostically relevant genes were identified, including protective factors (BTK, CLNK, CTSG; HR < 1) and risk factors (CTSV, HMOX1; HR > 1). Single‐cell analysis profiled 11,376 cells from one primary tumor and one normal lung sample. The prognostic model achieved moderate‐to‐good discrimination (AUC : 0.545–0.692) and effectively stratified patients by risk. Model genes correlated significantly with immune cell infiltration and checkpoint expression across risk groups, and functional enrichment highlighted immune trafficking, cytokine signaling, and cell adhesion pathways.
Conclusions
This multiomics study identifies reliable LRCD‐based prognostic markers and offers mechanistic insights into lung adenocarcinoma biology, with potential utility for clinical risk stratification.