Table_1_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.

衰老乃一种不可避免的时间依赖性过程，其特征为众多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）的发展密切相关。然而，迄今为止，尚无研究描述基于衰老相关基因（ARG）的LUAD预后特征。鉴于此，本研究对来自癌症基因组图谱（TCGA）和基因表达综合数据库（GEO）的ARG表达数据进行分析。通过LASSO和Cox回归分析，构建了一个包含六个ARG（APOC3、EPOR、H2AFX、MXD1、PLCG2和YWHAZ）的预后特征，该特征在TCGA数据集中显著地将病例分为高风险组和低风险组，从总体生存率（OS）的角度来看。Cox回归分析表明，该ARG特征是LUAD的独立预后因素。基于ARG特征和临床病理因素的评分系统在TCGA队列中建立，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，评分系统的预测与实际观察结果之间具有良好的一致性。接收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，可能促进个体化治疗，并为免疫治疗提供有希望的新的分子标志物。