基于缺氧免疫的微环境基因特征用于预测非小细胞肺癌生存的开发与验证

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

作者信息

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

机构信息

Department of Thoracic Surgery, The Third Affiliated Hospital of Kunming Medical University (Yunnan Cancer Hospital, Yunnan Cancer Center), 519 Kunzhou Road, Kunming, 650118, China.

School of Rehabilitation Medicine, Yunnan Institute of Economics and Management, 296 Haitun Road, Kunming, 650106, China.

出版信息

Discov Oncol. 2025 Aug 4;16(1):1464. doi: 10.1007/s12672-025-03319-z.

DOI:10.1007/s12672-025-03319-z

PMID:40759816

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12321723/

Abstract

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.

摘要

背景

本研究旨在建立肿瘤微环境（TME）中与缺氧免疫相关的基因特征，以可靠地预测非小细胞肺癌（NSCLC）的预后。

方法

从癌症基因组图谱（TCGA）和基因表达综合数据库（GEO）（GSE74777、GSE68465）中获取肺腺癌（LUAD）和肺鳞状细胞癌（LUSC）的转录组图谱和临床数据。从MSigDB、ImmPort和INATDB中筛选出与缺氧和免疫相关的基因。通过Cox和LASSO回归分析确定预后基因，并构建风险模型。通过Kaplan-Meier生存分析、受试者工作特征（ROC）曲线和外部验证来评估模型的有效性。

结果

建立了一个由八个基因组成的预后特征（AKAP12、MT2A、SERPINE1、CD1E、CD79A、CXCL13、XCL2、ANGPTL4）。该模型对NSCLC生存具有显著的预测准确性（TCGA队列中1/3/5年时的AUC分别为0.643/0.649/0.620）。与免疫活性低的患者相比，免疫活性高的患者生存结果更好（对数秩P＜0.001）。多变量Cox回归证实风险评分是一个独立的预后因素（HR = 1.82，95%CI：1.44 - 2.30，P＜0.001）。