Zhang Yulian, Zhang Chuanpeng, Yang Yanbo, Wang Guohui, Wang Zai, Liu Jiang, Zhang Li, Yu Yanbing
Department of Neurosurgery, China-Japan Friendship Hospital, Beijing, China.
Department of Neurosurgery, Peking University China-Japan Friendship School of Clinical Medicine, Beijing, China.
Front Cell Dev Biol. 2022 Apr 25;10:862493. doi: 10.3389/fcell.2022.862493. eCollection 2022.
Gliomas are the most common primary tumors in the central nervous system with a bad prognosis. Pyroptosis, an inflammatory form of regulated cell death, plays a vital role in the progression and occurrence of tumors. However, the value of pyroptosis related genes (PRGs) in glioma remains poorly understood. This study aims to construct a PRGs signature risk model and explore the correlation with clinical characteristics, prognosis, tumor microenviroment (TME), and immune checkpoints. RNA sequencing profiles and the relevant clinical data were obtained from the Chinese Glioma Genome Atlas (CGGA), the Cancer Genome Atlas (TCGA), the Repository of Molecular Brain Neoplasia Data (REMBRANDT), and the Genotype-Tissue Expression Project (GTEx-Brain). Then, the differentially expressed pyroptosis related genes (PRGs) were identified, and the least absolute shrinkage and selection operator (LASSO) and mutiCox regression model was generated using the TCGA-train dataset. Then the expression of mRNA and protein levels of PRGs signature was detected through qPCR and human protein atlas (HPA). Further, the predictive ability of the PRGs-signature, prognostic analysis, and stratification analysis were utilized and validated using TCGA-test, CGGA, and REMBRANDT datasets. Subsequently, we constructed the nomogram by combining the PRGs signature and other key clinical features. Moreover, we used gene set enrichment analysis (GSEA), GO, KEGG, the tumor immune dysfunction and exclusion (TIDE) single-sample GSEA (ssGSEA), and Immunophenoscore (IPS) to determine the relationship between PRGs and TME, immune infiltration, and predict the response of immune therapy in glioma. A four-gene PRGs signature (CASP4, CASP9, GSDMC, IL1A) was identified and stratified patients into low- or high-risk group. Survival analysis, ROC curves, and stratified analysis revealed worse outcomes in the high-risk group than in the low-risk group. Correlation analysis showed that the risk score was correlated with poor disease features. Furthermore, GSEA and immune infiltrating and IPS analysis showed that the PRGs signature could potentially predict the TME, immune infiltration, and immune response in glioma. The newly identified four-gene PRGs signature is effective in diagnosis and could robustly predict the prognosis of glioma, and its impact on the TME and immune cell infiltrations may provide further guidance for immunotherapy.
胶质瘤是中枢神经系统中最常见的原发性肿瘤，预后较差。焦亡是一种炎症形式的程序性细胞死亡，在肿瘤的发生和发展中起着至关重要的作用。然而，焦亡相关基因（PRGs）在胶质瘤中的价值仍知之甚少。本研究旨在构建一个PRGs特征风险模型，并探讨其与临床特征、预后、肿瘤微环境（TME）和免疫检查点的相关性。从中国胶质瘤基因组图谱（CGGA）、癌症基因组图谱（TCGA）、分子脑肿瘤数据储存库（REMBRANDT）和基因型-组织表达项目（GTEx-脑）中获取RNA测序谱和相关临床数据。然后，鉴定差异表达的焦亡相关基因（PRGs），并使用TCGA训练数据集生成最小绝对收缩和选择算子（LASSO）和多Cox回归模型。然后通过qPCR和人类蛋白质图谱（HPA）检测PRGs特征的mRNA和蛋白质水平表达。此外，利用TCGA测试、CGGA和REMBRANDT数据集对PRGs特征的预测能力、预后分析和分层分析进行了验证。随后，我们通过结合PRGs特征和其他关键临床特征构建了列线图。此外，我们使用基因集富集分析（GSEA）、GO、KEGG、肿瘤免疫功能障碍和排除（TIDE）单样本GSEA（ssGSEA）和免疫表型评分（IPS）来确定PRGs与TME、免疫浸润之间的关系，并预测胶质瘤免疫治疗的反应。鉴定出一个四基因PRGs特征（CASP4、CASP9、GSDMC、IL1A），并将患者分为低风险或高风险组。生存分析、ROC曲线和分层分析显示，高风险组的预后比低风险组更差。相关性分析表明，风险评分与不良疾病特征相关。此外，GSEA以及免疫浸润和IPS分析表明，PRGs特征可能预测胶质瘤中的TME、免疫浸润和免疫反应。新鉴定的四基因PRGs特征在诊断中有效，并且可以可靠地预测胶质瘤的预后，其对TME和免疫细胞浸润的影响可能为免疫治疗提供进一步的指导。
