School of Chinese Medicine, Nanjing University of Chinese Medicine, Nanjing, China.
Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China.
Environ Toxicol. 2024 Oct;39(10):4712-4728. doi: 10.1002/tox.24338. Epub 2024 May 8.
Gastric 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.
We 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.
Based 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.
We 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.
胃癌（GC）是一种常见的胃肠道（GI）系统恶性肿瘤。然而，缺乏可靠的生物标志物使得其诊断、预后和治疗变得具有挑战性。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类型。然而，其在 GC 中的功能需要进一步研究。
我们使用多组学和多角度方法全面探讨胃腺癌（STAD）患者中 ICD 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，使用加权基因共表达网络分析（WGCNA）和机器学习选择与 ICD 相关的预后基因，并构建预后模型。此外，我们构建了纳入相关临床特征的列线图，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治疗和药物的敏感性。最后，除了定量实时聚合酶链反应外，还使用免疫荧光验证了与 ICD 相关的基因的表达。
基于单细胞和转录组 WGCNA 分析，我们确定了 34 个与 ICD 相关的基因，其中 11 个与预后相关。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，并确定了风险亚组之间总生存期（OS）和无进展生存期（PFS）的差异。与 ICD 相关特征（ICDRS）相关的列线图在临床应用中具有良好的预测价值。此外，我们检测到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。
我们构建了一个包含与细胞死亡相关特征的 ICD 相关预后模型。该模型可作为预测 GC 预后、靶向预防和个体化医学的有用工具。
