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Background Exosomes can promote tumor development and regulate tumor immune responses, making them of significant value in Lung Adenocarcinoma (LUAD) management. In-depth exploration of exosome-related genes in LUAD is of great significance for expanding LUAD clinical treatment options. Methods Data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) were analyzed. Differential expression analysis (limma package), consensus clustering (ConsensusClusterPlus), and Least Absolute Shrinkage and Selection Operator (LASSO) regression (glmnet package) were used to build a prognostic model. Immune infiltration was assessed with TIMER, MCPcounter, and single-sample gene set enrichment analysis (ssGSEA). Tumor Immune Dysfunction and Exclusion (TIDE) algorithm evaluated immunotherapy response. Single-cell RNA sequencing (scRNA-seq) data were processed using Seurat. LUAD cell lines (A549, NCI-H838) were used for quantitative real-time PCR (qRT-PCR), Cell Counting Kit-8 (CCK-8), Transwell, and wound-healing assays. Results Four genes associated with exosomes were identified as key genes significantly influencing LUAD prognosis, namely CLIC6, ANLN, FAM83A, and RHOV. A LUAD prognosis model was constructed based on these genes, and the ROC curve confirmed the model’s excellent predictive performance. Immune infiltration analysis revealed immune cell infiltration differences between low- and high-RiskScore groups in LUAD, with significant differences in infiltration observed between groups for cells including eosinophils, T cells, myeloid dendritic cells, and B lineage ( P < 0.05). CD274, PDCD1 and LAG3 were highly expressed in the high-risk group of LUAD ( P < 0.01). Exosome-related pathways were significantly enriched in epithelial cells and monocyte/macrophage cells. Single-cell analysis reveals that CLIC6 exhibits higher expression levels in epithelial cells. ANLN regulates the malignant phenotype of LUAD cell lines. Conclusion A four-gene exosome-related signature was identified that can effectively separate LUAD patients into risk subgroups having distinct immune characteristics and immunotherapeutic benefits. Which occurred when ANLN was validated as an oncogene, coupled to the model being significantly associated with immune evasion mechanisms, suggests these biomarkers could enhance prognostic assessment and allow clinicians to identify patients who may be less likely to respond to immunotherapy, thereby informing personalized treatment strategies.