Table 6_Revealing the significance of tissue-resident memory T cells in lung adenocarcinoma through bioinformatic analysis and experimental validation.xlsx

PurposeTo investigate the functions of lung TRM cells in the development and treatment of lung adenocarcinoma (LUAD).
MethodsR-language bioinformatics analysis was applied to obtain differentially expressed (DE) lung TRM cell-specific genes and a related prognostic signature, which were further validated using external datasets, immunohistochemical staining images, and biological experiments.
ResultsA total of 130 DE lung TRM cell-specific genes were identified, 14 of which were involved in the prognostic signature, including SLC16A3, ARHGAP11A, PTTG1, DTL, GPRIN1, EXO1, GAPDH, TYMS, DAPK2, CCL20, HLA-DQA1, ADAM12, ALOX5AP and OASL. The signature was efficient and robust in predicting the overall survival and anti-PD-1/PD-L1 immunotherapeutic outcomes of patients with LUAD. The AUCs for predicting the 1-, 3-, and 5-year survival rates were 0.688, 0.698, and 0.648, respectively, in the training cohort, and were 0.867, 0.662, and 0.672, respectively, in the validation cohort. The signature also had predictive value for the sensitivity of patients to chemical drugs. TYMS was a hub gene in the prognostic signature, and was strongly associated with LUAD progression and cell proliferation in the experimental validation.
ConclusionsThe lung TRM cell-related prognostic signature is an effective tool for predicting the prognosis and therapeutic outcomes of patients with LUAD.

研究目的：本研究旨在探讨肺组织驻留记忆性T细胞（lung TRM cells）在肺腺癌（LUAD）发生发展与治疗中的作用。
研究方法：本研究采用R语言生物信息学分析方法，筛选得到差异表达（differentially expressed, DE）的肺TRM细胞特异性基因，并构建相关预后特征模型；随后通过外部数据集、免疫组化染色图像及生物学实验对该模型进行验证。
研究结果：本研究共筛选鉴定出130个差异表达的肺TRM细胞特异性基因，其中14个基因被纳入预后特征模型，包括SLC16A3、ARHGAP11A、PTTG1、DTL、GPRIN1、EXO1、GAPDH、TYMS、DAPK2、CCL20、HLA-DQA1、ADAM12、ALOX5AP及OASL。该预后特征模型在预测肺腺癌患者总生存期及抗PD-1/PD-L1免疫治疗疗效方面展现出良好的效能与稳定性。训练队列中，该模型预测1年、3年、5年生存率的曲线下面积（Area Under the Curve, AUC）分别为0.688、0.698及0.648；验证队列中则分别为0.867、0.662及0.672。此外，该特征模型对肺腺癌患者的化疗药物敏感性亦具有预测价值。TYMS为该预后特征模型中的核心枢纽基因，实验验证结果显示其与肺腺癌进展及细胞增殖密切相关。
研究结论：肺TRM细胞相关预后特征模型可作为有效工具，用于预测肺腺癌患者的预后及治疗结局。