肺腺癌（LUAD）是肺癌最常见的组织学亚型，表现出结果的异质性和对治疗的不同反应。 CD8 T 细胞始终存在于肿瘤发展的各个阶段，并在肿瘤微环境 (TME) 中发挥着关键作用。我们的目的是研究 LUAD 中 CD8 T 细胞标记基因的表达谱，建立基于这些基因的预后风险模型，并探讨其与免疫治疗反应的关系。通过利用癌症基因组图谱 (TCGA) 的表达数据和临床记录）和基因表达综合（GEO）队列，我们​​确定了 23 个一致的预后基因。我们采用了 10 种机器学习算法，生成了 101 种组合，最终选择了最佳算法来构建人工智能衍生的预测签名，名为riskScore。此选择基于三个测试队列的平均一致性指数 (C 指数)。RiskScore 成为总生存期 (OS)、无进展间隔 (PFI)、无病间隔 (DFI) 和LUAD 中的疾病特异性生存率 (DSS)。值得注意的是，与传统临床变量相比，riskScore 表现出明显优越的预测准确性。此外，我们观察到高风险riskScore组与肿瘤促进生物学功能、较低的肿瘤突变负荷（TMB）、较低的新抗原（NEO）负荷、较低的微卫星不稳定性（MSI）评分以及免疫功能降低之间呈正相关。 TME 内细胞浸润和免疫逃避的可能性增加。值得注意的是，免疫表型评分 (IPS) 评分在风险亚组之间显示出显着差异，并且riskScore根据患者的生存结果对 IMvigor210 和 GSE135222 免疫治疗队列中的患者进行了有效分层。此外，我们还确定了可以针对特定风险亚组的潜在药物。总之，riskScore 展示了其作为一种强大且有前途的工具的潜力，用于指导 LUAD 患者的临床管理和定制个体化治疗。2024 转化癌症研究。版权所有。

Lung adenocarcinoma (LUAD) stands as the most prevalent histological subtype of lung cancer, exhibiting heterogeneity in outcomes and diverse responses to therapy. CD8 T cells are consistently present throughout all stages of tumor development and play a pivotal role within the tumor microenvironment (TME). Our objective was to investigate the expression profiles of CD8 T cell marker genes, establish a prognostic risk model based on these genes in LUAD, and explore its relationship with immunotherapy response.By leveraging the expression data and clinical records from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) cohorts, we identified 23 consensus prognostic genes. Employing ten machine-learning algorithms, we generated 101 combinations, ultimately selecting the optimal algorithm to construct an artificial intelligence-derived prognostic signature named riskScore. This selection was based on the average concordance index (C-index) across three testing cohorts.RiskScore emerged as an independent risk factor for overall survival (OS), progression-free interval (PFI), disease-free interval (DFI), and disease-specific survival (DSS) in LUAD. Notably, riskScore exhibited notably superior predictive accuracy compared to traditional clinical variables. Furthermore, we observed a positive correlation between the high-risk riskScore group and tumor-promoting biological functions, lower tumor mutational burden (TMB), lower neoantigen (NEO) load, and lower microsatellite instability (MSI) scores, as well as reduced immune cell infiltration and an increased probability of immune evasion within the TME. Of significance, the immunophenoscore (IPS) score displayed significant differences among risk subgroups, and riskScore effectively stratified patients in the IMvigor210 and GSE135222 immunotherapy cohort based on their survival outcomes. Additionally, we identified potential drugs that could target specific risk subgroups.In summary, riskScore demonstrates its potential as a robust and promising tool for guiding clinical management and tailoring individualized treatments for LUAD patients.2024 Translational Cancer Research. All rights reserved.