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Abstract：
The prognostic roles of apoptosis-related genes (ARGs) in lung adenocarcinoma (LUAD) have not been fully elucidated. In this study, differentially expressed genes (DEGs) associated with apoptosis and the hub genes were further identified. The prognostic values of the ARGs were evaluated using the LASSO Cox regression method. Prognostic values were determined using Kaplan-Meier (K-M) curves and receiver operating characteristic (ROC) curves in the TCGA and GEO datasets. The correlations, mutation data, and protein expression of the 10 ARGs predictive models were also analyzed. We identified 130 differentially expressed ARGs. DEGs were used to split LUAD cases into two subtypes whose overall survival (OS) were significantly different (P = 0.025). We developed a novel 10-gene signature using LASSO Cox regression. In both TCGA and GEO datasets, the results of the K-M curve and log-rank test showed significant difference in the survival rate of patients in the high-risk group and low-risk group (P < 0.0001). According to the GO and KEGG analyses, ARGs were enriched in cancer-related terms. In both cohorts, the immune status of the high-risk group was significantly lower than that of the low-risk group. Based on the differential expression of the ARGs, we established a new risk model to predict the prognosis of patients with LUAD.
摘要：
凋亡相关基因（ARGs）在肺腺癌（LUAD）中的预后作用尚未完全阐明。在这项研究中,进一步鉴定了与凋亡相关的差异表达基因（DEGs）和hub基因。使用LASSOCox回归方法评估ARGs的预后价值。在TCGA和GEO数据集中使用Kaplan-Meier(K-M)曲线和受试者工作特征(ROC)曲线确定预后值。相关性，突变数据,并对10个ARGs预测模型的蛋白表达进行了分析。我们鉴定了130种差异表达的ARGs。使用DEGs将LUAD病例分为两种亚型，其总生存期（OS）显着不同（P=0.025）。我们使用LASSOCox回归开发了一种新的10基因签名。在TCGA和GEO数据集中，K-M曲线和log-rank检验结果显示，高危组和低危组患者的生存率差异有统计学意义（P<0.0001）。根据GO和KEGG的分析，ARGs在癌症相关术语中富集。在这两个队列中，高危组的免疫状态明显低于低危组。基于ARGs的差异表达，我们建立了一个新的风险模型来预测LUAD患者的预后。
