Zhang Yakun, Zhou Jiajun, Jin Yitong, Liu Chenyu, Zhou Hanxiao, Sun Yue, Jiang Han, Gan Jing, Zhang Caiyu, Lu Qianyi, Chang Yetong, Zhang Yunpeng, Li Xia, Ning Shangwei
College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
The Second Affiliated Hospital of Harbin Medical University, Harbin 150081, China.
Cancers (Basel). 2024 Dec 30;17(1):85. doi: 10.3390/cancers17010085.
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.
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.
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.
Our study performed a comprehensive framework for assessing immunosenescence levels and provided insights into the role of immunosenescence in cancer prognosis and biomarker discovery.
免疫衰老即免疫系统的老化，与肺癌的发生发展及预后密切相关。针对免疫衰老进行干预被认为是一种有前景的治疗方法。
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正常组织中对其进行检测。我们探究了两个肺癌数据集的46,993个单细胞。通过网络分析、免疫浸润分析和套索回归在TCGA肺腺癌（LUAD）中构建免疫衰老风险模型（ISRM），并在四个肺癌队列中通过生存分析、cox回归和列线图进行验证。通过肿瘤预测算法和XGBoost模型检测ISRM对药物反应和免疫治疗的预测能力。
我们发现衰老的肺组织在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描绘了老年和年轻肺癌之间不同的免疫微环境。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定关联。ISRM由五个风险基因（CD40LG、IL7、CX3CR1、TLR3和TLR2）组成，改善了多个数据集中肺癌的预后分层。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率较差，免疫检查点表达较低，对免疫治疗耐药。
我们的研究为评估免疫衰老水平构建了一个综合框架，并为免疫衰老在癌症预后和生物标志物发现中的作用提供了见解。
