Table_2_An Aging-Related Gene Signature-Based Model for Risk Stratification and Prognosis Prediction in Lung Adenocarcinoma.DOCX

Aging is an inevitable time-dependent process associated with a gradual decline in many physiological functions. Importantly, some studies have supported that aging may be involved in the development of lung adenocarcinoma (LUAD). However, no studies have described an aging-related gene (ARG)-based prognosis signature for LUAD. Accordingly, in this study, we analyzed ARG expression data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). After LASSO and Cox regression analyses, a six ARG-based signature (APOC3, EPOR, H2AFX, MXD1, PLCG2, and YWHAZ) was constructed using TCGA dataset that significantly stratified cases into high- and low-risk groups in terms of overall survival (OS). Cox regression analysis indicated that the ARG signature was an independent prognostic factor in LUAD. A nomogram based on the ARG signature and clinicopathological factors was developed in TCGA cohort and validated in the GEO dataset. Moreover, to visualize the prediction results, we established a web-based calculator yurong.shinyapps.io/ARGs_LUAD/. Calibration plots showed good consistency between the prediction of the nomogram and actual observations. Receiver operating characteristic curve and decision curve analyses indicated that the ARG nomogram had better OS prediction and clinical net benefit than the staging system. Taken together, these results established a genetic signature for LUAD based on ARGs, which may promote individualized treatment and provide promising novel molecular markers for immunotherapy.

衰老是一种不可避免的时间依赖性过程，其特征在于多种生理功能的逐渐衰退。值得注意的是，一些研究表明，衰老可能与肺腺癌（LUAD）的发生发展密切相关。然而，迄今为止，尚无研究描述过针对LUAD的基于衰老相关基因（ARG）的预后特征。因此，在本研究中，我们分析了来自癌症基因组图谱（TCGA）和基因表达综合数据库（GEO）的ARG表达数据。经过LASSO和Cox回归分析，我们构建了一个包含六个ARG（APOC3、EPOR、H2AFX、MXD1、PLCG2和YWHAZ）的预后特征，该特征利用TCGA数据集将病例显著分为高风险组和低风险组，以总体生存率（OS）为标准。Cox回归分析表明，该ARG特征是LUAD的一个独立预后因素。基于ARG特征和临床病理因素的评分系统在TCGA队列中得到开发，并在GEO数据集中得到验证。此外，为了可视化预测结果，我们建立了一个基于网络的计算器yurong.shinyapps.io/ARGs_LUAD/。校准图显示，评分系统预测与实际观察之间具有良好的一致性。接受者操作特征曲线和决策曲线分析表明，该ARG评分系统在OS预测和临床净收益方面优于分期系统。综上所述，这些结果建立了一个基于ARGs的LUAD遗传特征，这可能会促进个体化治疗，并为免疫治疗提供有前景的新型分子标记物。