BackgroundGastric cancer (GC) is a prevalent malignant tumor of the gastrointestinal (GI) system. However, the lack of reliable biomarkers has made its diagnosis, prognosis, and treatment challenging. Immunogenic cell death (ICD) is a type of programmed cell death that is strongly related to the immune system. However, its function in GC requires further investigation.MethodWe used multi‐omics and multi‐angle approaches to comprehensively explore the prognostic features of ICD in patients with stomach adenocarcinoma (STAD). At the single‐cell level, we screened genes associated with ICD at the transcriptome level, selected prognostic genes related to ICD using weighted gene co‐expression network analysis (WGCNA) and machine learning, and constructed a prognostic model. In addition, we constructed nomograms that incorporated pertinent clinical features and provided effective tools for prognostic prediction in clinical settings. We also investigated the sensitivity of the risk subgroups to both immunotherapy and drugs. Finally, in addition to quantitative real‐time polymerase chain reaction, immunofluorescence was used to validate the expression of ICD‐linked genes.ResultsBased on single‐cell and transcriptome WGCNA analyses, we identified 34 ICD‐related genes, of which 11 were related to prognosis. We established a prognostic model using the least absolute shrinkage and selection operator (LASSO) algorithm and identified dissimilarities in overall survival (OS) and progression‐free survival (PFS) in risk subgroups. The nomograms associated with the ICD‐related signature (ICDRS) demonstrated a good predictive value for clinical applications. Moreover, we detected changes in the tumor microenvironment (TME), including biological functions, mutation landscapes, and immune cell infiltration, between the high‐ and low‐risk groups.ConclusionWe constructed an ICD‐related prognostic model that incorporated features related to cell death. This model can serve as a useful tool for predicting the prognosis of GC, targeted prevention, and personalized medicine.

中文翻译：

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揭示胃腺癌中的免疫原性细胞死亡途径：一项多组学研究

研究背景胃癌（GC）是胃肠道（GI）系统常见的恶性肿瘤。然而，缺乏可靠的生物标志物使其诊断、预后和治疗具有挑战性。免疫原性细胞死亡（ICD）是一种与免疫系统密切相关的程序性细胞死亡。但其在GC中的作用还需要进一步研究。方法采用多组学、多角度的方法全面探讨ICD对胃腺癌（STAD）患者的预后特征。在单细胞水平上，我们在转录组水平筛选与ICD相关的基因，利用加权基因共表达网络分析（WGCNA）和机器学习筛选与ICD相关的预后基因，并构建预后模型。此外，我们构建了包含相关临床特征的列线图，并为临床环境中的预后预测提供了有效的工具。我们还研究了风险亚组对免疫疗法和药物的敏感性。最后，除了实时定量聚合酶链式反应外，还使用免疫荧光来验证ICD相关基因的表达。结果基于单细胞和转录组WGCNA分析，我们鉴定了34个ICD相关基因，其中11个与ICD相关基因相关。预后。我们使用最小绝对收缩和选择算子（LASSO）算法建立了一个预后模型，并确定了风险亚组中总生存期（OS）和无进展生存期（PFS）的差异。与 ICD 相关特征 (ICDRS) 相关的列线图对临床应用表现出良好的预测价值。此外，我们检测了高风​​险组和低风险组之间肿瘤微环境（TME）的变化，包括生物学功能、突变景观和免疫细胞浸润。结论我们构建了一个 ICD 相关预后模型，其中纳入了与细胞死亡相关的特征。该模型可以作为预测GC预后、针对性预防和个性化医疗的有用工具。