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Background <p>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).</p> Methods <p>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.</p> Results <p>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 <i>P</i> &lt; 0.001). Multivariate Cox regression confirmed the risk score as an independent prognostic factor (HR = 1.82, 95% CI: 1.44–2.30, <i>P</i> &lt; 0.001).</p> Conclusions <p>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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月4日

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  • Xiangwu Zhang,
  • Rongxian Zhou,
  • Guangqiang Zhao,
  • Wanling Chen,
  • Ping Zhao,
  • Yunchao Huang,
  • Qiubo Huang,
  • Lianhua Ye

摘要

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.