Department of Oncology, the Second Xiangya Hospital of Central South University, Molecular Biology Research Center and Center for Medical Genetics, School of Life Sciences, Central South University, Changsha, 410000, Hunan, China.
School of Medical Technology, Shao Yang University, Shaoyang, 422000, Hunan, China.
Sci Rep. 2022 Aug 11;12(1):13646. doi: 10.1038/s41598-022-17735-6.
Lung squamous cell carcinoma (LUSC) comprises 20-30% of all lung cancers. Immunotherapy has significantly improved the prognosis of LUSC patients; however, only a small subset of patients responds to the treatment. Therefore, we aimed to develop a novel multi-gene signature associated with the immune phenotype of the tumor microenvironment for LUSC prognosis prediction. We stratified the LUSC patients from The Cancer Genome Atlas dataset into hot and cold tumor according to a combination of infiltration status of immune cells and PD-L1 expression level. Kaplan-Meier analysis showed that hot tumors were associated with shorter overall survival (OS). Enrichment analyses of differentially expressed genes (DEGs) between the hot and cold tumors suggested that hot tumors potentially have a higher immune response ratio to immunotherapy than cold tumors. Subsequently, hub genes based on the DEGs were identified and protein-protein interactions were constructed. Finally, we established an immune-related 13-gene signature based on the hub genes using the least absolute shrinkage and selection operator feature selection and multivariate cox regression analysis. This gene signature divided LUSC patients into high-risk and low-risk groups and the former inclined worse OS than the latter. Multivariate cox proportional hazard regression analysis showed that the risk model constructed by the 13 prognostic genes was an independent risk factor for prognosis. Receiver operating characteristic curve analysis showed a moderate predictive accuracy for 1-, 3- and 5-year OS. The 13-gene signature also performed well in four external cohorts (three LUSC and one melanoma cohorts) from Gene Expression Omnibus. Overall, in this study, we established a reliable immune-related 13-gene signature that can stratify and predict the prognosis of LUSC patients, which might serve clinical use of immunotherapy.
肺鳞状细胞癌（LUSC）约占所有肺癌的 20-30%。免疫疗法显著改善了 LUSC 患者的预后；然而，只有一小部分患者对治疗有反应。因此，我们旨在开发一种与肿瘤微环境免疫表型相关的新型多基因标志物，用于预测 LUSC 患者的预后。我们根据免疫细胞浸润状态和 PD-L1 表达水平的组合，将来自癌症基因组图谱数据集的 LUSC 患者分为热肿瘤和冷肿瘤。Kaplan-Meier 分析显示，热肿瘤与总生存期（OS）较短相关。对热肿瘤和冷肿瘤之间差异表达基因（DEGs）的富集分析表明，热肿瘤可能对免疫治疗有更高的免疫反应比例，而冷肿瘤则较低。随后，基于 DEGs 鉴定了枢纽基因，并构建了蛋白质-蛋白质相互作用网络。最后，我们使用最小绝对收缩和选择算子特征选择和多变量 cox 回归分析，基于枢纽基因建立了一个免疫相关的 13 基因标志物。该基因标志物将 LUSC 患者分为高危和低危组，前者的 OS 明显差于后者。多变量 cox 比例风险回归分析表明，由 13 个预后基因构建的风险模型是预后的独立危险因素。受试者工作特征曲线分析显示，对 1 年、3 年和 5 年 OS 的预测准确率适中。该 13 基因标志物在来自基因表达综合数据库的四个外部队列（三个 LUSC 和一个黑色素瘤队列）中也表现良好。总体而言，在这项研究中，我们建立了一个可靠的免疫相关的 13 基因标志物，可以对 LUSC 患者进行分层和预测预后，这可能为免疫治疗的临床应用提供参考。
