Background: 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./nMethods: 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./nResults: 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./nConclusions: 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.

中文翻译：

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T细胞耗竭相关基因的鉴定及其在肺腺癌中的免疫治疗作用的预测

背景：肺腺癌是全球第二大最常见的癌症，死亡率很高。多项研究表明，T 细胞耗竭与肿瘤免疫治疗有关。因此，了解 T 细胞耗竭对肿瘤微环境可能产生的影响至关重要。本研究的目的是创建一个基于 TEX 的模型，该模型将使用单细胞 RNA 序列 (scRNA-seq) 和批量 RNA 测序来探索评估 LUAD 患者预后和免疫治疗反应的新可能性。/n方法：从癌症基因组图谱 (TCGA) 数据库和国家生物技术信息中心 (GEO) 下载 LUAD 患者的 RNA-seq 数据。Bischoff P 等人报道的 10X scRNA 测序数据用于使用 TSNE 进行下采样聚类和亚组识别。通过基因集方差分析（GSVA）和加权基因相关网络分析（WGCNA）鉴定了 TEX 相关基因。我们利用 LASSO-Cox 分析来建立预测的 TEX 特征。在 GSE31210 和 GSE30219 队列中进行了外部验证。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群体之间突变谱和免疫微环境的差异。然后我们利用 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 验证了关键调控基因的差异表达。/n结果： 9 个 TEX 基因被确定为 LUAD 预后的高度预测因子，并与疾病结果密切相关。单变量和多变量分析显示，与高风险组患者相比，低风险组患者的总生存率明显更高，凸显了该模型独立预测 LUAD 预后的能力。我们的分析揭示了高风险组和低风险组肿瘤微环境中的生物学功能、突变景观和免疫细胞浸润的显着差异。此外，发现免疫治疗对两组都有显着影响，表明该模型具有很强的预测功效。/n结论： TEX模型表现出良好的预测性能，为评估预免疫的疗效提供了新的视角，从而提供了新的策略为未来肺腺癌的治疗提供依据。