目的：探究肺腺癌中与免疫治疗相关氧化应激基因（IROSG）及其与肿瘤组织免疫浸润和患者预后的关系。方法：从TCGA数据库和GEO数据库下载肺腺癌患者IROSG表达数据及相关临床信息。对非小细胞肺癌免疫治疗队列进行差异基因表达分析以获取免疫治疗相关基因，然后与从GeneCards 数据库筛选的氧化应激相关基因取交集得到IROSG。基于得到的IROSG 对肺腺癌患者进行分型，对亚型的差异表达基因进行单因素COX、LASSO 和多因素COX回归分析以构建预后模型。使用模型公式计算每个患者的风险评分，并将患者划分为高、低风险组。从多个层面验证模型的预测效能，并进行肿瘤微环境（TME）分析、免疫治疗反应预测和药物敏感性分析。结果：通过数据库分析获取82 个IROSG ，IROSG 高表达的肺腺癌患者预后较好（P<0.05）。基于IROSG 表达水平分型和风险评分构建的肺腺癌患者预后模型预测能力好，基于风险评分和病例特征等预后因子构建的列线图和校正曲线能较好地预测肺腺癌患者的总生存率。低风险组主要富集于同种异体移植物排斥和自身免疫性疾病等通路，而高风险组主要富集在细胞周期和DNA复制等通路上，且低风险组肺腺癌组织中免疫细胞浸润水平较高。高、低风险评分结合肿瘤突变负荷（TMB）、TME、肿瘤免疫功能障碍和排斥（TIDE）评分和免疫检查点分子表达水平能较好地预测肺腺癌患者预后、免疫治疗反应和对化疗药物的敏感性。结论：本研究构建了一个可以预测肺腺癌患者预后和免疫治疗反应的模型，可为肺腺癌患者个体化治疗提供了理论依据。

Objective: To explore the immunotherapy-related oxidative stress genes (IROSGs) in lung adenocarcinoma (LUAD) and their relationship with immune infiltration and patient prognosis. Methods: The expression profile IROSG and corresponding clinical information of LUAD patients were downloaded from the TCGA and GEO databases. To identify immunotherapy-related genes, differential gene expression analysis was conducted on a cohort of non-small cell lung cancer (NSCLC) patients undergoing immunotherapy. By intersecting the results with oxidative stress-related genes screened from the GeneCards database, the IROSG set was obtained. The LUAD patients were clustered based on the obtained IROSG, and a prognostic model was constructed by performing univariate COX regression analysis, Lasso and multivariate COX regression analysis on the differentially expressed genes of the subtypes. The risk score was calculated for each patient based on the model, and patients were categorized into high-risk and low-risk groups. The predictive efficacy of the model was validated using multiple external validation sets, and further analyses were performed including tumor microenvironment (TME) analysis, drug sensitivity analysis, and prediction of immunotherapy response. Results: A total of 82 IROSGs were obtained through comprehensive database analysis, and LUAD patients with high IROSG expression had a better prognosis (P<0.05). The prognostic model for LUAD patients constructed on the basis of expression of IROSG and risk scores showed good predictive performance. A nomogram was constructed based on the prognostic factors such as risk score and patient characteristics, and its calibration curve demonstrated good performance to predict the overall survival rate in LUAD patients. The low-risk group was primarily enriched in pathways such as allograft rejection and autoimmune diseases, while the high-risk group was primarily enriched in pathways such as cell cycle and DNA replication. Additionally, the levels of immune cell infiltration were higher in the LUAD tissues of low-risk group. Combining high- and low-risk scores with tumor mutation burden (TMB), TME, tumor immune dysfunction and exclusion (TIDE) scores, as well as immune checkpoint molecule expression levels, can effectively predict the prognosis of LUAD patients, immune therapy response, and sensitivity to chemotherapy drugs. Conclusion: In this study, we developed a model for predicting the prognosis and immunotherapy response of LUAD patients, providing a theoretical basis for personalized treatment of LUAD patients.

云南省基础研究计划科研项目（No. 202301AY070001-099）