Lian Chaoqun, Li Feifan, Xie Yiluo, Zhang Linxiang, Chen Huili, Wang Ziqiang, Pan Xinyu, Wang Xiaojing, Zhang Jing
Research Center of Clinical Laboratory Science, Bengbu Medical University, Bengbu 233030, China.
Anhui Province Key Laboratory of Clinical and Preclinical Research in Respiratory Disease, Molecular Diagnosis Center, Department of Pulmonary and Critical Care Medicine, First Affiliated Hospital of Bengbu Medical University, Bengbu 233030, China.
J Cancer. 2024 Feb 25;15(8):2160-2178. doi: 10.7150/jca.92839. eCollection 2024.
Lung adenocarcinoma ranks as the second most widespread form of cancer globally, accompanied by a significant mortality rate. Several studies have shown that T cell exhaustion is associated with immunotherapy of tumours. Consequently, it is essential to comprehend the possible impact of T cell exhaustion on the tumor microenvironment. The purpose of this research was to create a TEX-based model that would use single-cell RNA-seq (scRNA-seq) and bulk-RNA sequencing to explore new possibilities for assessing the prognosis and immunotherapeutic response of LUAD patients. RNA-seq data from LUAD patients was downloaded from the Cancer Genome Atlas (TCGA) database and the National Center for Biotechnology Information (GEO). 10X scRNA sequencing data, as reported by Bischoff P et al., was utilized for down-sampling clustering and subgroup identification using TSNE. TEX-associated genes were identified through gene set variance analysis (GSVA) and weighted gene correlation network analysis (WGCNA). We utilized LASSO-Cox analysis to establish predicted TEX features. External validation was conducted in GSE31210 and GSE30219 cohorts. Immunotherapeutic response was assessed in IMvigor210, GSE78220, GSE35640 and GSE100797 cohorts. Furthermore, we investigated differences in mutational profiles and immune microenvironment between various risk groups. We then screened TEXRS key regulatory genes using ROC diagnostic curves and KM survival curves. Finally, we verified the differential expression of key regulatory genes through RT-qPCR. Nine TEX genes were identified as highly predictive of LUAD prognosis and strongly correlated with disease outcome. Univariate and multivariate analysis revealed that patients in the low-risk group had significantly better overall survival rates compared with those in the high-risk group, highlighting the model's ability to independently predict LUAD prognosis. Our analysis revealed significant variation in the biological function, mutational landscape, and immune cell infiltration within the tumor microenvironment of both high-risk and low-risk groups. Additionally, immunotherapy was found to have a significant impact on both groups, indicating strong predictive efficacy of the model. The TEX model showed good predictive performance and provided a new perspective for evaluating the efficacy of preimmunization, which provides a new strategy for the future treatment of lung adenocarcinoma.
肺腺癌是全球第二大常见癌症，死亡率颇高。多项研究表明，T细胞耗竭与肿瘤免疫治疗相关。因此，了解T细胞耗竭对肿瘤微环境的潜在影响至关重要。本研究旨在构建一个基于T细胞耗竭（TEX）的模型，利用单细胞RNA测序（scRNA-seq）和批量RNA测序来探索评估肺腺癌（LUAD）患者预后和免疫治疗反应的新可能性。从癌症基因组图谱（TCGA）数据库和美国国立生物技术信息中心（GEO）下载了LUAD患者的RNA-seq数据。如Bischoff P等人报道的10X scRNA测序数据，用于通过t-SNE进行下采样聚类和亚组鉴定。通过基因集变异分析（GSVA）和加权基因共表达网络分析（WGCNA）鉴定TEX相关基因。我们利用LASSO-Cox分析建立预测的TEX特征。在GSE31210和GSE30219队列中进行外部验证。在IMvigor210、GSE78220、GSE35640和GSE100797队列中评估免疫治疗反应。此外，我们研究了不同风险组之间突变谱和免疫微环境的差异。然后，我们使用ROC诊断曲线和KM生存曲线筛选TEXRS关键调控基因。最后，我们通过RT-qPCR验证关键调控基因的差异表达。九个TEX基因被确定为对LUAD预后具有高度预测性，且与疾病结局密切相关。单因素和多因素分析显示，低风险组患者的总生存率显著高于高风险组患者，突出了该模型独立预测LUAD预后的能力。我们的分析揭示了高风险和低风险组肿瘤微环境中生物学功能、突变格局和免疫细胞浸润的显著差异。此外，发现免疫治疗对两组均有显著影响，表明该模型具有强大的预测效力。TEX模型显示出良好的预测性能，为评估免疫治疗疗效提供了新视角，为未来肺腺癌治疗提供了新策略。
