Han Pengkai, Guda Chittibabu, Liu Qiping
Department of Pulmonary and Critical Care Medicine, Chongqing University Three Gorges Hospital, School of Medicine, Chongqing University, Chongqing, China.
Department of Genetics, Cell Biology, and Anatomy, University of Nebraska Medical Center, Omaha, NE, USA.
Transl Cancer Res. 2025 Aug 31;14(8):4989-5001. doi: 10.21037/tcr-2025-1455. Epub 2025 Aug 28.
Epithelial-mesenchymal transition (EMT) plays an important role in the pathogenesis of lung adenocarcinoma (LUAD). In this study, we aimed to construct a prognostic signature based on EMT that could predict the prognosis of patients with LUAD.
The messenger RNA (mRNA) expression profiles and the clinical data were downloaded from The Cancer Genome Atlas (TCGA) as the training set while data from the Gene Expression Omnibus (GEO) served as the validation set. Differentially expressed EMT-related genes (EMTGs) were identified from the training dataset. Univariate and multivariate Cox regression analyses were employed to develop a gene signature from the EMTGs to predict overall survival (OS) time. The prediction performance of the signature was tested using the time-dependent receiver operating characteristic (ROC) curve. The signature was verified in the TCGA dataset and the external dataset, GSE30219. A corresponding nomogram was also constructed to predict the prognosis of patients with LUAD. The expression of the prognostic genes at the protein level was investigated in the Clinical Proteomic Tumor Analysis Consortium (CPTAC) dataset. Gene set enrichment analysis was conducted to reveal the biological pathways associated with the high-risk group and the low-risk group.
A set of 79 differentially expressed EMTGs were identified. An EMT-related signature was constructed which classified patients with LUAD into two subgroups based on the median risk score. In the ROC curve analysis, the prognostic signature had a moderate discrimination accuracy for the 1-, 3-, and 5-year survival rate with areas under the curve (AUCs) of 0.732, 0.675, 0.702 in TCGA training set, respectively and 0.813, 0.672, 0.706 in the GSE30219 validation set, respectively. The established nomogram effectively predicted the OS of patients with LUAD.
The novel EMT-related signature established in this study is a robust and independent prognostic indicator for patients with LUAD. This signature is expected to improve the personalized management of patients with LUAD.
上皮-间质转化（EMT）在肺腺癌（LUAD）的发病机制中起重要作用。在本研究中，我们旨在构建基于EMT的预后特征，以预测LUAD患者的预后。
从癌症基因组图谱（TCGA）下载信使核糖核酸（mRNA）表达谱和临床数据作为训练集，而来自基因表达综合数据库（GEO）的数据用作验证集。从训练数据集中鉴定出差异表达的EMT相关基因（EMTGs）。采用单变量和多变量Cox回归分析从EMTGs中开发一个基因特征，以预测总生存（OS）时间。使用时间依赖的受试者工作特征（ROC）曲线测试该特征的预测性能。在TCGA数据集和外部数据集GSE30219中验证该特征。还构建了相应的列线图以预测LUAD患者的预后。在临床蛋白质组肿瘤分析联盟（CPTAC）数据集中研究了预后基因在蛋白质水平的表达。进行基因集富集分析以揭示与高风险组和低风险组相关的生物学途径。
鉴定出一组79个差异表达的EMTGs。构建了一个与EMT相关的特征，该特征根据中位风险评分将LUAD患者分为两个亚组。在ROC曲线分析中，该预后特征对1年、3年和5年生存率具有中等的判别准确性，在TCGA训练集中曲线下面积（AUCs）分别为0.732、0.675、0.702，在GSE30219验证集中分别为0.813、0.672、0.706。所建立的列线图有效地预测了LUAD患者的OS。
本研究中建立的新型EMT相关特征是LUAD患者强大且独立的预后指标。该特征有望改善LUAD患者的个性化管理。
