Work

Prognostic Modeling of Lysosomal Cell Death Signatures in Lung Adenocarcinoma: A Machine Learning Framework for Survival Outcome Prediction

Year: 2026

Type: article

Abstract: Background: 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: = 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.

Source: International Journal of Genomics

DOI: 10.1155/ijog/8237477