构建了基于PD-L1相关特征的肺腺癌风险评分模型，该模型揭示了预后预测能力以及与肿瘤免疫微环境基因的相关性。
Risk scoring model for lung adenocarcinoma based on PD-L1 related signature reveals prognostic predictability and correlation with tumor immune microenvironment genes was constructed.
作者信息
Li-Fei Meng, Ren Si-Meng, Wang Jun, Zhao Wei-Jun, Chen Jian, Hu Wen-Tao
机构信息
Department of Thoracic Surgery, The First Affiliated Hospital of Ningbo University, Ningbo, China.
Department of Psychology, College of Liberal Arts, Wenzhou-Kean University, Wenzhou, China.
出版信息
Front Immunol. 2025 Jun 11;16:1601982. doi: 10.3389/fimmu.2025.1601982. eCollection 2025.
BACKGROUND
Immunotherapy has recently become a hot topic in the field of oncology, with PD-L1 playing a crucial role in this area. However, the research on PD-L1 correlation prediction models is not fully understood. The aim of our study was to investigate the role of PD-L1-related genes in lung adenocarcinoma immunity.
METHODS
The mRNA and clinical data were obtained from the Cancer Genome Atlas database. DESeq2, Glmnet, forestplot, clusterProfiler and enrichplot were used to analyze the mRNA and clinical data. Western blotting and real-time qRT-PCR were used to confirm the GPR115, MF12, GREB1L, SPRR1B and LIPK mRNA and protein expression.
RESULTS
Firstly, 562 cases of TCGA lung adenocarcinoma, including 503 of tumor tissue and 59 of normal tissue were collected. The dataset was analyzed using the DESeq2 package of R. 1,251 high- and 285 low-expression genes were obtained. The tumor samples were divided into CD274-high and CD274-low expression samples and 873 genes were up-regulated and 1,010 genes were down regulated between CD274-high and CD274-low samples. Subsequently, the intersection of 1,251 and 873 was taken to obtain 110 genes that were both highly expressed genes in tumors and CD274 high-expression samples. Survival analysis of 110 genes yielded 5 meaningful genes including GPR115, MF12, GREB1L, SPRR1B, and LIPK (p < 0.001). These five genes were used to construct PD-L1 risk predictors. Cytokine-cytokine receptor interaction and IL-17 signaling pathway were involved in the regulation of this risk model factors to lung adenocarcinoma. The level of effector memory CD4 T cells and Type 2 T helper cells were correlated with the risk model factor. Importantly, the PD-L1 risk prediction model could effectively predict the prognosis of patients.
CONCLUSION
The construction of PD-L1 risk model was of great significance for the treatment of lung adenocarcinoma.
背景
免疫疗法最近已成为肿瘤学领域的热门话题，程序性死亡受体配体1（PD-L1）在该领域发挥着关键作用。然而，关于PD-L1相关预测模型的研究尚未完全明晰。我们研究的目的是探究PD-L1相关基因在肺腺癌免疫中的作用。
方法
从癌症基因组图谱（Cancer Genome Atlas）数据库获取mRNA和临床数据。使用DESeq2、Glmnet、forestplot、clusterProfiler和enrichplot对mRNA和临床数据进行分析。采用蛋白质免疫印迹法和实时定量逆转录-聚合酶链反应（qRT-PCR）来确认GPR115、MF12、GREB1L、丝聚蛋白1B（SPRR1B）和脂质激酶（LIPK）的mRNA和蛋白质表达。
结果
首先，收集了562例TCGA肺腺癌病例，其中包括503例肿瘤组织和59例正常组织。使用R语言的DESeq2软件包对数据集进行分析。获得了1251个高表达基因和285个低表达基因。将肿瘤样本分为CD274高表达和CD274低表达样本，在CD274高表达和低表达样本之间，有873个基因上调，1010个基因下调。随后，取1251个基因与873个基因的交集，得到110个在肿瘤和CD274高表达样本中均为高表达的基因。对这110个基因进行生存分析，得到5个有意义的基因，包括GPR115、MF12、GREB1L、SPRR1B和LIPK（p < 0.001）。利用这五个基因构建了PD-L1风险预测模型。细胞因子-细胞因子受体相互作用和白细胞介素-17信号通路参与了该风险模型对肺腺癌的调控。效应记忆CD4 T细胞和2型辅助性T细胞的水平与风险模型因素相关。重要的是，PD-L1风险预测模型能够有效预测患者的预后。
结论
构建PD-L1风险模型对肺腺癌的治疗具有重要意义。
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