积累的研究表明，肿瘤免疫与热原性细胞死亡、凋亡和坏死之间存在密切关系。然而，PANoptosis在胃癌（GC）中的作用尚未完全理解。本研究试图通过整合GSE54129和GSE65801数据集，确定GC中PANoptosis调节因子的表达模式以及免疫景观。我们分析了GC标本，并建立了与PANoptosis相关基因（PRGs）和对应免疫特性相关的分子簇。我们使用WGCNA方法确定差异表达基因。随后，我们采用了四种机器学习算法（随机森林、支持向量机、广义线性模型和极限梯度提升）来选择最优模型，并使用判定表、校准曲线、决策曲线分析（DCA）和两个验证队列对其进行验证。此外，本研究还讨论了选定模型中浸润免疫细胞与变量之间的关系。本研究确定了GC样本和正常样本之间的PRGs异常表达和免疫活性之间的差异，并进一步确定了GC中的两个PANoptosis相关分子簇。这些分子簇表现出显著的免疫异质性，其中簇1表现出丰富的免疫浸润。支持向量机签名被发现具有最佳的差异能力，建立了一个基于5个基因的支持向量机签名。该模型在外部验证队列中表现出优异的性能，并且判定表、校准曲线和DCA表明它在预测GC模式方面的可靠性。进一步分析证实，选择的5个变量与浸润免疫细胞和免疫相关通路密切相关。综上所述，本研究显示了PANoptosis模式作为患者风险评估的分层工具和GC免疫微环境的反映的潜力。Copyright © 2023 Qing, Jiang, Yuan, Xie and Wang.

Accumulative studies have demonstrated the close relationship between tumor immunity and pyroptosis, apoptosis, and necroptosis. However, the role of PANoptosis in gastric cancer (GC) is yet to be fully understood.This research attempted to identify the expression patterns of PANoptosis regulators and the immune landscape in GC by integrating the GSE54129 and GSE65801 datasets. We analyzed GC specimens and established molecular clusters associated with PANoptosis-related genes (PRGs) and corresponding immune characteristics. The differentially expressed genes were determined with the WGCNA method. Afterward, we employed four machine learning algorithms (Random Forest, Support Vector Machine, Generalized linear Model, and eXtreme Gradient Boosting) to select the optimal model, which was validated using nomogram, calibration curve, decision curve analysis (DCA), and two validation cohorts. Additionally, this study discussed the relationship between infiltrating immune cells and variables in the selected model.This study identified dysregulated PRGs and differential immune activities between GC and normal samples, and further identified two PANoptosis-related molecular clusters in GC. These clusters demonstrated remarkable immunological heterogeneity, with Cluster1 exhibiting abundant immune infiltration. The Support Vector Machine signature was found to have the best discriminative ability, and a 5-gene-based SVM signature was established. This model showed excellent performance in the external validation cohorts, and the nomogram, calibration curve, and DCA indicated its reliability in predicting GC patterns. Further analysis confirmed that the 5 selected variables were remarkably related to infiltrating immune cells and immune-related pathways.Taken together, this work demonstrates that the PANoptosis pattern has the potential as a stratification tool for patient risk assessment and a reflection of the immune microenvironment in GC.Copyright © 2023 Qing, Jiang, Yuan, Xie and Wang.