Department of Physiology, Shanxi Medical University, Taiyuan, China.
Key Laboratory of Cellular Physiology, (Shanxi Medical University), Ministry of Education, Taiyuan, China.
Bioengineered. 2021 Dec;12(1):7417-7431. doi: 10.1080/21655979.2021.1974779.
Lung adenocarcinoma (LUAD) represents the major histological type of lung cancer with high mortality globally. Due to the heterogeneous nature, the same treatment strategy to various patients may result in different therapeutic responses. Hence, we aimed to elaborate an effective signature for predicting patient survival outcomes. The TCGA-LUAD cohort from the TCGA portal was used as a training dataset. The GSE26939 and GSE68465 cohorts from the GEO database were taken as validation datasets. All immunologically relevant genes were extracted from the ImmPort. The ESTIMATE algorithm was employed to explore LUAD microenvironment in the training dataset. Further, the DEGs were picked out based on the immune-associated genes reflecting different statuses in the immune context of TME. Univariate/multivariate Cox regression was performed to determine six prognosis- specific genes (PIK3CG, BTK, VEGFD, INHA, INSL4, and PTPRC) and established a risk predictive signature. The time-dependent ROC indicated that AUC values were all greater than 0.70 at 1-, 3-, and 5- year intervals. Corresponding RiskScore of each LUAD patient was calculated from the signature, and they were stratified into the high- and low-risk groups by the median value of RiskScore. K-M curves and Log-rank test demonstrated significant survival differences between the two groups ( < 0.05). Similar results were exhibited in the validation datasets. The RiskScore was incredibly relevant to clinicopathological factors like gender, AJCC stage, and T stage. Also, it can mirror the distribution state of 15 kinds of TIICs and have some predictive value for the sensitivity of therapeutic drugs.
肺腺癌 (LUAD) 是全球死亡率较高的主要肺癌组织学类型。由于其异质性，对不同患者采用相同的治疗策略可能会导致不同的治疗反应。因此，我们旨在阐述一种有效的预测患者生存结局的标志物。我们使用 TCGA 数据库中的 TCGA-LUAD 队列作为训练数据集。从 GEO 数据库中选择 GSE26939 和 GSE68465 队列作为验证数据集。从 ImmPort 中提取所有与免疫相关的基因。使用 ESTIMATE 算法在训练数据集中探索 LUAD 微环境。进一步，根据免疫相关基因反映 TME 免疫背景中不同状态的方法，筛选出差异基因。使用单变量/多变量 Cox 回归确定了 6 个与预后相关的基因 (PIK3CG、BTK、VEGFD、INHA、INSL4 和 PTPRC)，并建立了风险预测模型。时间依赖性 ROC 表明，在 1、3 和 5 年的时间间隔内，AUC 值均大于 0.70。根据该标志物计算每个 LUAD 患者的 RiskScore，并通过 RiskScore 的中位数将其分为高风险组和低风险组。K-M 曲线和 Log-rank 检验表明两组之间的生存差异有统计学意义（<0.05）。在验证数据集中也得到了类似的结果。RiskScore 与性别、AJCC 分期和 T 分期等临床病理因素密切相关。此外，它可以反映 15 种 TIIC 的分布状态，对治疗药物的敏感性具有一定的预测价值。
