Department of Clinical Laboratory, Second Affiliated Hospital, 117799Xi'an Jiaotong University, Xi'an, Shaanxi, People's Republic of China.
School of Medicine, 117799Xi'an Jiaotong University, Xi'an, Shaanxi, People's Republic of China.
Technol Cancer Res Treat. 2020 Jan-Dec;19:1533033820977504. doi: 10.1177/1533033820977504.
Lung cancer is one of the leading causes of cancer-related death. In recent years, there has been an increasing interest in the fields of tumor and immunity. This study focused on the possible prognostic value of immune genes in non-small cell lung cancer patients. We used The Cancer Genome Atlas (TCGA) to download gene expression data and clinical information of lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC). The immune gene list was downloaded from the Immport database. We then constructed immune gene prognostic models on the basis of Cox regression analysis. We further evaluated the clinical significance of the models via survival analysis, receiver operating characteristic (ROC) curves, and independent prognostic factor analysis. Moreover, we analyzed the associations of prognostic models with both mutation burdens and neoantigens. Using the Gene Expression Omnibus (GEO) and Kaplan-Meier plotter databases, we evaluated the validity of the prognostic models. The prognostic model of LUAD included 13 immune genes, and the prognostic model of LUSC contained 10 immune genes. High-risk patients based on prognostic models had a lower 5-year survival rate than did low-risk patients. The ROC curve analysis demonstrated the prediction accuracy of the prognostic models, as the area under the curve (AUC) was 0.742, 0.707, and 0.711 for LUAD, and 0.668, 0.703, and 0.668 for LUSC, when the predicted survival times were 1, 3, and 5 years, respectively. The mutation burden analysis showed that mutation level was associated with the risk score in patients with LUAD. The analysis based on GEO and Kaplan-Meier plotter demonstrated the prognostic validity of the models. Therefore, immune gene-related models of LUAD and LUSC can predict prognosis. Further study of these genes may enable us to better distinguish between LUAD and LUSC and lead to improvement in immunotherapy for lung cancer.
肺癌是癌症相关死亡的主要原因之一。近年来，肿瘤和免疫领域越来越受到关注。本研究重点探讨了免疫基因在非小细胞肺癌患者中的可能预后价值。我们使用癌症基因组图谱（TCGA）下载了肺腺癌（LUAD）和肺鳞癌（LUSC）的基因表达数据和临床信息。免疫基因列表从 Immport 数据库下载。然后，我们基于 Cox 回归分析构建了免疫基因预后模型。我们进一步通过生存分析、接收者操作特征（ROC）曲线和独立预后因素分析评估了模型的临床意义。此外，我们分析了预后模型与突变负担和新抗原的相关性。我们使用基因表达综合数据库（GEO）和 Kaplan-Meier 绘图器数据库评估了预后模型的有效性。LUAD 的预后模型包括 13 个免疫基因，LUSC 的预后模型包含 10 个免疫基因。基于预后模型的高危患者的 5 年生存率低于低危患者。ROC 曲线分析表明了预后模型的预测准确性，当预测生存时间分别为 1、3 和 5 年时，曲线下面积（AUC）分别为 0.742、0.707 和 0.711，用于 LUAD；分别为 0.668、0.703 和 0.668，用于 LUSC。突变负担分析表明，在 LUAD 患者中，突变水平与风险评分相关。基于 GEO 和 Kaplan-Meier 绘图器的分析证明了模型的预后有效性。因此，LUAD 和 LUSC 的免疫基因相关模型可以预测预后。对这些基因的进一步研究可能使我们能够更好地区分 LUAD 和 LUSC，并改善肺癌的免疫治疗。
