Fang Xisheng, Zheng Shaopeng, Fang Zekui, Wu Xiping, Schenk Erin L, Belluomini Lorenzo, Fan Huizhen
Department of Oncology, Guangzhou First People's Hospital, South China University of Technology, Guangzhou, China.
Department of Thoracic Surgery, Cancer Hospital of Shantou University Medical College, Shantou, China.
Transl Lung Cancer Res. 2025 Jun 30;14(6):2125-2144. doi: 10.21037/tlcr-24-297. Epub 2025 Jun 26.
With the swift advancements in immunotherapy for solid tumors, exploring immune characteristics of tumors has become increasingly important. The tumor microenvironment (TME) is closely related to the prognosis and treatment of tumor patients. This study aims to explore the expression characteristics and model construction of TME-related genes in lung adenocarcinoma (LUAD) patients, and provide help for clinical diagnosis and treatment.
Through the Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) algorithm, we analyzed the transcriptomic data of 559 samples from The Cancer Genome Atlas (TCGA) data set to estimate the stromal cells and immune cells, and screened the immune-related differentially expressed genes (DEGs), namely, the TME-DEGs. Essential TME genes were then selected from the TME-DEGs by multivariate Cox and least absolute shrinkage and selection operator (LASSO) regression, and a prediction model of prognostic risk score (RS) was established.
We identified 5 crucial TME genes: , and . Analysis of the genes' associations with prognosis and clinical features showed that was significantly associated with poorer prognosis and decreased immune signatures, whereas the other 4 associated with improved prognosis and immune signatures. Further, a prognostic RS prediction model was constructed based on these 5 genes, and the results showed that patients with low RS had significantly higher overall survival (OS; P<0.001), relapse-free survival (RFS; P=0.009) and disease-free survival (DFS; P=0.005) than the high RS group, and it had a certain predictive accuracy [area under the curve (AUC)] of 5 years OS =0.70). Those were consistent in the GSE50081 cohort.
Five crucial TME genes, , and , are significantly correlated with the prognosis and tumor immune microenvironment (TIME) characteristic of LUAD patients, and the prognostic model has good prediction efficiency, which may improve clinical prognostic models and therapy selection.
随着实体瘤免疫治疗的迅速发展，探索肿瘤的免疫特征变得越来越重要。肿瘤微环境（TME）与肿瘤患者的预后和治疗密切相关。本研究旨在探讨肺腺癌（LUAD）患者中TME相关基因的表达特征及模型构建，为临床诊断和治疗提供帮助。
通过使用表达数据估计恶性肿瘤组织中的基质和免疫细胞（ESTIMATE）算法，我们分析了来自癌症基因组图谱（TCGA）数据集的559个样本的转录组数据，以估计基质细胞和免疫细胞，并筛选出免疫相关差异表达基因（DEG），即TME-DEG。然后通过多变量Cox和最小绝对收缩和选择算子（LASSO）回归从TME-DEG中选择关键的TME基因，并建立预后风险评分（RS）预测模型。
我们鉴定出5个关键的TME基因： ， 和 。对这些基因与预后和临床特征的关联分析表明， 与较差的预后和免疫特征降低显著相关，而其他4个与改善的预后和免疫特征相关。此外，基于这5个基因构建了预后RS预测模型，结果显示低RS患者的总生存期（OS；P<0.001）、无复发生存期（RFS；P=0.009）和无病生存期（DFS；P=0.005）显著高于高RS组，并且其具有一定的预测准确性[5年OS曲线下面积（AUC）=0.70]。这些结果在GSE50081队列中是一致的。
5个关键的TME基因， ， 和 ，与LUAD患者的预后和肿瘤免疫微环境（TIME）特征显著相关，并且该预后模型具有良好的预测效率，这可能改善临床预后模型和治疗选择。
