Chen Peng, Yang Xian, Chen Weijie, Wei Wenwei, Chen Yujie, Wang Peiyuan, He Hao, Liu Shuoyan, Zheng Yuzhen, Wang Feng
Department of Thoracic Oncology Surgery, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou 350011 Fujian Province, PR China; Fujian Key Laboratory of Translational Cancer Medicine, Fuzhou 350011 Fujian Province, PR China; Fujian Provincial Key Laboratory of Tumor Biotherapy, Fuzhou 350011 Fujian Province, PR China.
Department of Nephrology, Fujian Provincial Hospital South Branch, Fuzhou 350007 Fujian Province, PR China.
Gene. 2025 Mar 15;941:149233. doi: 10.1016/j.gene.2025.149233. Epub 2025 Jan 10.
T cell senescence affects non-small cell lung cancer (NSCLC) by compromising the anti-tumor immune response. However, the prognostic significance of T cell senescence-related genes in NSCLC remains unclear.
The scRNA-seq data from normal lung and NSCLC tissues, along with co-incubation experiments involving NSCLC cells and T cells, were utilized to identify T cell senescence characteristics. The TCGA-NSCLC dataset was used for training, and 8 independent NSCLC cohorts from GEO were combined for validation. Various machine learning algorithms were employed for feature selection, with multivariate Cox regression used to construct the risk model. Two NSCLC cohorts receiving anti-PD1/PDL1 treatment from GEO were employed to validate the risk model's predictive capability for immunotherapeutic response. Additionally, 10 pairs of paracarcinoma and NSCLC tissues from a local hospital and transfection assays on T cells were used for validation.
T cells in the NSCLC microenvironment displayed increased senescent features (all P < 0.05). SLC2A1, TNS4, and GGTLC1 were integrated into the risk model, which proved to be a significant prognostic predictor in both training (P < 0.001) and validation (P < 0.05) cohorts. The risk signature also demonstrated strong predictive power for immunotherapeutic sensitivity (both AUC > 0.8). Higher CD3SLC2A1 and CD3TNS4 T cell infiltration, along with lower CD3GGTLC1 T cell levels, were observed in NSCLC (all P < 0.05). Moreover, GGTLC1 overexpression suppressed T cell senescence (all P < 0.05).
A T cell senescence-related gene signature has been established to predict prognosis and immunotherapeutic response in NSCLC.
T细胞衰老通过损害抗肿瘤免疫反应影响非小细胞肺癌（NSCLC）。然而，T细胞衰老相关基因在NSCLC中的预后意义仍不清楚。
利用来自正常肺组织和NSCLC组织的单细胞RNA测序（scRNA-seq）数据，以及涉及NSCLC细胞和T细胞的共孵育实验，来确定T细胞衰老特征。TCGA-NSCLC数据集用于训练，来自基因表达综合数据库（GEO）的8个独立NSCLC队列用于验证。采用多种机器学习算法进行特征选择，用多变量Cox回归构建风险模型。利用来自GEO的两个接受抗PD1/PDL1治疗的NSCLC队列，验证风险模型对免疫治疗反应的预测能力。此外，使用当地一家医院的10对癌旁组织和NSCLC组织以及T细胞转染实验进行验证。
NSCLC微环境中的T细胞显示衰老特征增加（所有P<0.05）。溶质载体家族2成员1（SLC2A1）、张力蛋白4（TNS4）和γ-谷氨酰转肽酶样蛋白1（GGTLC1）被纳入风险模型，该模型在训练队列（P<0.001）和验证队列（P<0.05）中均被证明是一个显著的预后预测指标。风险特征对免疫治疗敏感性也显示出强大的预测能力（曲线下面积均>0.8）。在NSCLC中观察到较高的CD3⁺SLC2A1和CD3⁺TNS4 T细胞浸润，以及较低的CD3⁺GGTLC1 T细胞水平（所有P<0.05）。此外，GGTLC1过表达抑制T细胞衰老（所有P<0.05）。
已建立一种T细胞衰老相关基因特征，以预测NSCLC的预后和免疫治疗反应。
