Department of Respiration, Chengdu First People's Hospital, Chengdu, 610041, China.
Department of Respiration, West China Hospital of Sichuan University, 37# Guo Xue Xiang, Chengdu, 610041, Sichuan, China.
Sci Rep. 2022 Jul 14;12(1):12059. doi: 10.1038/s41598-022-15971-4.
Because of immunotherapy failure in lung adenocarcinoma, we have tried to find new potential biomarkers for differentiating different tumor subtypes and predicting prognosis. We identified two subtypes based on tumor microenvironment-related genes in this study. We used seven methods to analyze the immune cell infiltration between subgroups. Further analysis of tumor mutation load and immune checkpoint expression among different subgroups was performed. The least absolute shrinkage and selection operator Cox regression was applied for further selection. The selected genes were used to construct a prognostic 14-gene signature for LUAD. Next, a survival analysis and time-dependent receiver operating characteristics were performed to verify and evaluate the model. Gene set enrichment analyses and immune analysis in risk groups was also performed. According to the expression of genes related to the tumor microenvironment, lung adenocarcinoma can be divided into cold tumors and hot tumors. The signature we built was able to predict prognosis more accurately than previously known models. The signature-based tumor microenvironment provides further insight into the prediction of lung adenocarcinoma prognosis and may guide individualized treatment.
由于肺腺癌的免疫治疗失败，我们试图寻找新的潜在生物标志物来区分不同的肿瘤亚型并预测预后。我们根据这项研究中与肿瘤微环境相关的基因确定了两种亚型。我们使用七种方法来分析亚组间的免疫细胞浸润。对不同亚组之间的肿瘤突变负荷和免疫检查点表达进行了进一步分析。最小绝对收缩和选择算子 Cox 回归用于进一步选择。选择的基因用于构建 LUAD 的预后 14 基因特征。接下来，进行生存分析和时间依赖的接收器工作特性以验证和评估模型。还对风险组中的基因集富集分析和免疫分析进行了研究。根据与肿瘤微环境相关的基因表达，肺腺癌可分为冷肿瘤和热肿瘤。我们构建的特征比以前已知的模型更能准确地预测预后。基于特征的肿瘤微环境为肺腺癌预后的预测提供了更深入的了解，并可能指导个体化治疗。
