Table2_Pyroptosis-Related Gene Signature Predicts Prognosis and Indicates Immune Microenvironment Infiltration in Glioma.XLSX

Objective: 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.
Methods: 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.
Results: 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.
Conclusion: 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.

研究目标：胶质瘤（Gliomas）是中枢神经系统最常见的原发性肿瘤，预后不良。细胞焦亡（Pyroptosis）是一种炎性程序性细胞死亡形式，在肿瘤的发生发展中发挥关键作用，但目前对焦亡相关基因（PRGs）在胶质瘤中的作用仍知之甚少。本研究旨在构建焦亡相关基因特征风险模型，并探讨其与临床特征、预后、肿瘤微环境（TME）及免疫检查点的相关性。
研究方法：本研究从中国胶质瘤基因组图谱（CGGA）、癌症基因组图谱（TCGA）、脑肿瘤分子数据仓库（REMBRANDT）及基因型-组织表达项目（GTEx-Brain）中获取RNA测序表达谱及相关临床数据。随后鉴定差异表达的焦亡相关基因，基于TCGA训练集构建最小绝对收缩和选择算子（LASSO）回归与多因素Cox回归模型。通过实时定量聚合酶链式反应（qPCR）与人类蛋白质图谱（HPA）检测该焦亡基因特征的mRNA与蛋白表达水平。进一步利用TCGA测试集、CGGA及REMBRANDT数据集，对该焦亡基因特征的预测能力、预后分析及分层分析进行验证。随后，结合该基因特征与其他关键临床特征构建列线图（nomogram）。此外，通过基因集富集分析（GSEA）、基因本体（GO）富集分析、京都基因与基因组百科全书（KEGG）富集分析、肿瘤免疫功能异常与排斥分析（TIDE）、单样本基因集富集分析（ssGSEA）及免疫评分（IPS），明确焦亡相关基因与肿瘤微环境、免疫浸润的关联，并预测胶质瘤的免疫治疗响应。
研究结果：本研究鉴定出包含CASP4、CASP9、GSDMC、IL1A的4个焦亡相关基因特征，可将胶质瘤患者分为低风险组与高风险组。生存分析、ROC曲线及分层分析结果显示，高风险组患者的预后显著差于低风险组。相关性分析表明，风险评分与不良疾病特征相关。此外，基因集富集分析、免疫浸润及免疫评分分析显示，该焦亡基因特征可有效预测胶质瘤的肿瘤微环境、免疫浸润状态及免疫响应。
研究结论：本研究新鉴定的4基因焦亡相关基因特征可有效用于胶质瘤的诊断，并能稳健预测患者预后，其对肿瘤微环境及免疫细胞浸润的影响可为胶质瘤免疫治疗提供新的指导方向。