Integrated bioinformatics analysis identifies immune-related epithelial-mesenchymal transition prognostic biomarkers and immune infiltrates in patients with lung adenocarcinoma

Abstract

Abstract Background Lung cancer, particularly lung adenocarcinoma (LUAD), is highly lethal. Understanding the critical interaction between epithelial-mesenchymal transition (EMT) and the immune status of patients is imperative for clinical assessment. Methods We conducted bioinformatics analysis to identify potential immune-related EMT (iEMT) prognostic genes and explored the immune status in LUAD. Using data from The Cancer Genome Atlas and GSE68465, differentially expressed genes, were identified, and a risk model was constructed. Cluster analysis was conducted using the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathways. Results Our findings revealed 69 differentially expressed iEMT genes, with risk values demonstrating independent prognostic significance for both The Cancer Genome Atlas and GSE68465 samples. The risk value was positively correlated with tumor stage. Immune cell infiltration analysis showed a significant decrease in resting dendritic cells and an increase in CD4 memory T cells in high-risk groups with poor survival prognoses. The immunotherapy analysis revealed weak immunotherapeutic effects in the high-risk group. Conclusions This study provides insights into potential aberrant differential iEMT genes and risk models and explores immune landscapes that inform personalized immunotherapy in patients with LUAD.