Development and validation of a hypoxia-immune-based microenvironment gene signature for predicting survival in non-small cell lung cancer.

Background: This study aims to establish a hypoxia-immune-related gene signature within the tumor microenvironment (TME) to reliably predict prognosis in non-small cell lung cancer (NSCLC).

Methods: Transcriptomic profiles and clinical data of lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases (GSE74777, GSE68465). Hypoxia- and immune-related genes were curated from MSigDB, ImmPort, and INATDB. Prognostic genes were identified via Cox and LASSO regression analyses, and a risk model was constructed. Model validity was assessed through Kaplan-Meier survival analysis, receiver operating characteristic (ROC) curves, and external validation.

Results: An eight-gene prognostic signature (AKAP12, MT2A, SERPINE1, CD1E, CD79A, CXCL13, XCL2, ANGPTL4) was established. The model demonstrated significant predictive accuracy for NSCLC survival (AUC: 0.643/0.649/0.620 at 1/3/5 years in TCGA cohort). Patients with high immune activity exhibited superior survival outcomes compared to those with low-immune counterparts (log-rank P < 0.001). Multivariate Cox regression confirmed the risk score as an independent prognostic factor (HR = 1.82, 95% CI: 1.44-2.30, P < 0.001).

Conclusions: The hypoxia-immune microenvironment signature serves as a robust prognostic classifier for NSCLC, providing a quantitative framework for personalized risk stratification and clinical decision support.