Background: Immunosenescence is the aging of the immune system, which is closely related to the development and prognosis of lung cancer. Targeting immunosenescence is considered a promising therapeutic approach. Methods: We defined an immunosenescence gene set (ISGS) and examined it across 33 TCGA tumor types and 29 GTEx normal tissues. We explored the 46,993 single cells of two lung cancer datasets. The immunosenescence risk model (ISRM) was constructed in TCGA LUAD by network analysis, immune infiltration analysis, and lasso regression and validated by survival analysis, cox regression, and nomogram in four lung cancer cohorts. The predictive ability of ISRM for drug response and immunotherapy was detected by the oncopredict algorithm and XGBoost model. Results: We found that senescent lung tissues were significantly enriched in ISGS and revealed the heterogeneity of immunosenescence in pan-cancer. Single-cell and bulk transcriptomics characterized the distinct immune microenvironment between old and young lung cancer. The ISGS network revealed the crucial function modules and transcription factors. Multiplatform analysis revealed specific associations between immunosenescence and the tumor progression of lung cancer. The ISRM consisted of five risk genes (CD40LG, IL7, CX3CR1, TLR3, and TLR2), which improved the prognostic stratification of lung cancer across multiple datasets. The ISRM showed robustness in immunotherapy and anti-tumor therapy. We found that lung cancer patients with a high-risk score showed worse survival and lower expression of immune checkpoints, which were resistant to immunotherapy. Conclusions: Our study performed a comprehensive framework for assessing immunosenescence levels and provided insights into the role of immunosenescence in cancer prognosis and biomarker discovery.
背景：免疫衰老是指免疫系统的老化，与肺癌的发生发展及预后密切相关。靶向免疫衰老被认为是一种有前景的治疗策略。方法：我们定义了一个免疫衰老基因集（ISGS），并在33种TCGA肿瘤类型和29种GTEx正常组织中进行了检测。我们分析了两个肺癌数据集的46,993个单细胞数据。通过网络分析、免疫浸润分析和LASSO回归，在TCGA LUAD中构建了免疫衰老风险模型（ISRM），并在四个肺癌队列中通过生存分析、Cox回归和列线图进行了验证。使用OncoPredict算法和XGBoost模型检测了ISRM对药物反应和免疫治疗的预测能力。结果：我们发现衰老肺组织在ISGS中显著富集，并揭示了泛癌中免疫衰老的异质性。单细胞和批量转录组学分析刻画了老年与年轻肺癌患者之间不同的免疫微环境特征。ISGS网络揭示了关键的功能模块和转录因子。多平台分析揭示了免疫衰老与肺癌肿瘤进展之间的特异性关联。ISRM由五个风险基因（CD40LG、IL7、CX3CR1、TLR3和TLR2）构成，在多个数据集中改善了肺癌的预后分层。ISRM在免疫治疗和抗肿瘤治疗中表现出稳健性。我们发现高风险评分的肺癌患者生存期更差，免疫检查点表达更低，且对免疫治疗产生抵抗。结论：本研究建立了一个评估免疫衰老水平的综合框架，为理解免疫衰老在癌症预后和生物标志物发现中的作用提供了新见解。
肿瘤(癌症)患者之家