Zhou Yuting, Xu Min, Zhao Kai, Liu Baodong
Department of Thoracic Surgery, Central Hospital of Zibo, Affiliated with Shandong University, Zibo, China.
Department of Interventional Oncology, Central Hospital of Zibo, Affiliated with Shandong University, Zibo, China.
J Thorac Dis. 2023 Mar 31;15(3):1319-1334. doi: 10.21037/jtd-23-103. Epub 2023 Mar 29.
Lung squamous cell carcinoma (LUSC) is a highly malignant tumor with an extremely poor prognosis. Immune checkpoint inhibitors (ICIs) improve survival in some patients with LUSC. Tumor mutation burden (TMB) is a useful biomarker to predict the efficacy of ICIs. However, predictive and prognostic factors related to TMB in LUSC remain elusive. This study aimed to find effective biomarkers based on TMB and immune response and establish a prognostic model of LUSC.
We downloaded Mutation Annotation Format (MAF) files from The Cancer Genome Atlas (TCGA) database and identified immune-related differentially expressed genes (DEGs) between high- and low-TMB groups. The prognostic model was established using cox regression. The primary outcome was overall survival (OS). Receiver operating characteristic (ROC) curves and calibration curves were used to verify the accuracy of the model. GSE37745 acted as external validation set. The expression and prognosis of hub genes as well as their correlation with immune cells and somatic copy number variation (sCNA) were analyzed.
The TMB of patients with LUSC was correlated with prognosis and stage. High TMB group had higher survival rate (P<0.001). Five TMB-related hub immune genes ( and ) were identified and the prognostic model was constructed. The survival time of high-risk group was significantly shorter than that of low-risk group (P<0.001). The validation results of the model were quite stable in different data sets, and the area under curve (AUC) of training set and validation set were 0.658 and 0.644, respectively. Calibration chart, risk curve, and nomogram revealed that the prognostic model was reliable in predicting the prognostic risk of LUSC, and the risk score of the model could be used as an independent prognostic factor for LUSC patients (P<0.001).
Our results show that high TMB is associated with poor prognosis in patients with LUSC. The prognostic model related to TMB and immunity can effectively predict the prognosis of LUSC, and risk score is one of the independent prognostic factors of LUSC. However, this study still has some limitations, which need to be further verified in large-scale and prospective studies.
肺鳞状细胞癌（LUSC）是一种恶性程度高、预后极差的肿瘤。免疫检查点抑制剂（ICIs）可提高部分LUSC患者的生存率。肿瘤突变负荷（TMB）是预测ICIs疗效的有用生物标志物。然而，LUSC中与TMB相关的预测和预后因素仍不明确。本研究旨在基于TMB和免疫反应寻找有效的生物标志物，并建立LUSC的预后模型。
我们从癌症基因组图谱（TCGA）数据库下载了突变注释格式（MAF）文件，并鉴定了高TMB组和低TMB组之间的免疫相关差异表达基因（DEGs）。使用cox回归建立预后模型。主要结局为总生存期（OS）。采用受试者工作特征（ROC）曲线和校准曲线验证模型的准确性。GSE37745作为外部验证集。分析了枢纽基因的表达和预后及其与免疫细胞和体细胞拷贝数变异（sCNA）的相关性。
LUSC患者的TMB与预后和分期相关。高TMB组生存率更高（P<0.001）。鉴定出5个与TMB相关的枢纽免疫基因，并构建了预后模型。高危组的生存时间明显短于低危组（P<0.001）。该模型在不同数据集中的验证结果相当稳定，训练集和验证集的曲线下面积（AUC）分别为0.658和0.644。校准图、风险曲线和列线图显示，该预后模型在预测LUSC的预后风险方面可靠，模型的风险评分可作为LUSC患者的独立预后因素（P<0.
