Abstract
BACKGROUND: Migrasomes, a newly identified subtype of extracellular vesicles generated during cell migration, play crucial roles in tumor microenvironment modulation. However, their systematic characterization in lung adenocarcinoma (LUAD) remains unexplored. This study is aimed at deciphering migrasome-related molecular features and their clinical significance through multiomics integration. METHODS: We integrated bulk transcriptomes (541 LUAD samples from TCGA/GEO) with single-cell RNA-seq (GSE156632). Migrasome-related genes (MIGgenes) were identified through WGCNA and differential expression analysis. A machine learning framework incorporating 10 algorithms generated 101 combinatorial models, with the optimal prognostic signature (MIGsig) selected via 10-fold cross-validation. Biological mechanisms were investigated through ssGSEA, TME analysis, and in vitro validation. RESULTS: Our analysis revealed significant migrasome activity enrichment in endothelial cells and fibroblasts, with 115 cross-omics MIGgenes identified including 31 prognostic markers. The Lasso-Cox-derived 3-gene signature (GSTM5/DNASE1L3/PDGFB) demonstrated robust predictive performance (training set C index = 0.703; validation set GSE50081 AUC = 0.678). The low-MIGsig group exhibited characteristic "hot tumor" features, including elevated immune infiltration and higher tumor mutational burden, and significantly improved immunotherapy response rates in the IMvigor210 cohort. Finally, MIGsig-related genes were further validated by in vitro experiments and public database. CONCLUSIONS: This study establishes the first migrasome-based prognostic model for LUAD, demonstrating both independent survival prediction capability and clinical utility for identifying immunotherapy beneficiaries. The MIGsig signature provides novel biological insights into migrasome-mediated tumor-immune interactions and represents a promising tool for precision oncology applications in LUAD management.