鉴定胃癌中与铜死亡相关的亚型，构建预后模型和肿瘤微环境景观。

INTRODUCTION: Cuproptosis is a novel identified regulated cell death (RCD), which is correlated with the development, treatment response and prognosis of cancer. However, the potential role of cuproptosis-related genes (CRGs) in the tumor microenvironment (TME) of gastric cancer (GC) remains unknown. METHODS: Transcriptome profiling, somatic mutation, somatic copy number alteration and clinical data of GC samples were downloaded from the Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) database to describe the alterations of CRGs from genetic and transcriptional fields. Differential, survival and univariate cox regression analyses of CRGs were carried out to investigate the role of CRGs in GC. Cuproptosis molecular subtypes were identified by using consensus unsupervised clustering analysis based on the expression profiles of CRGs, and further analyzed by GO and KEGG gene set variation analyses (GSVA). Genes in distinct molecular subtypes were also analyzed by GO and KEGG gene enrichment analyses (GSEA). Differentially expressed genes (DEGs) were screened out from distinct molecular subtypes and further analyzed by GO enrichment analysis and univariate cox regression analysis. Consensus clustering analysis of prognostic DEGs was performed to identify genomic subtypes. Next, patients were randomly categorized into the training and testing group at a ratio of 1:1. CRG Risk scoring system was constructed through logistic least absolute shrinkage and selection operator (LASSO) cox regression analysis, univariate and multivariate cox analyses in the training group and validated in the testing and combined groups. Real-time quantitative polymerase chain reaction (RT-qPCR) was used to evaluate the expression of key Risk scoring genes. Sensitivity and specificity of Risk scoring system were examined by using receiver operating characteristic (ROC) curves. pRRophetic package in R was used to investigate the therapeutic effects of drugs in high- and low- risk score group. Finally, the nomogram scoring system was developed to predict patients' survival through incorporating the clinicopathological features and CRG Risk score. RESULTS: Most CRGs were up-regulated in tumor tissues and showed a relatively high mutation frequency. Survival and univariate cox regression analysis revealed that LIAS and FDX1 were significantly associated with GC patients' survival. After consensus unsupervised clustering analysis, GC patients were classified into two cuproptosis molecular subtypes, which were significantly associated with clinical features (gender, age, grade and TNM stage), prognosis, metabolic related pathways and immune cell infiltration in TME of GC. GO enrichment analyses of 84 DEGs, obtained from distinct molecular subtypes, revealed that DEGs primarily enriched in the regulation of metabolism and intracellular/extracellular structure in GC. Univariate cox regression analysis of 84 DEGs further screened out 32 prognostic DEGs. According to the expression profiles of 32 prognostic DEGs, patients were re-classified into two gene subtypes, which were significantly associated with patients' age, grade, T and N stage, and survival of patients. Nest, the Risk score system was constructed with moderate sensitivity and specificity. A high CRG Risk score, characterized by decreased microsatellite instability-high (MSI-H), tumor mutation burden (TMB) and cancer stem cell (CSC) index, and high stromal and immune score in TME, indicated poor survival. Four of five key Risk scoring genes expression were dysregulated in tumor compared with normal samples. Moreover, CRG Risk score was greatly related with sensitivity of multiple drugs. Finally, we established a highly accurate nomogram for promoting the clinical applicability of the CRG Risk scoring system. DISCUSSION: Our comprehensive analysis of CRGs in GC demonstrated their potential roles in TME, clinicopathological features, and prognosis. These findings may improve our understanding of CRGs in GC and provide new perceptions for doctors to predict prognosis and develop more effective and personalized therapy strategies.

简介：铜死亡是一种新发现的调控细胞死亡（RCD），与癌症的发生、治疗反应和预后相关。然而，铜死亡相关基因（CRGs）在胃癌（GC）肿瘤微环境（TME）中的潜在作用尚不清楚。

方法：从癌症基因组图谱（TCGA）和基因表达综合数据库（GEO）下载 GC 样本的转录组谱、体细胞突变、体细胞拷贝数改变和临床数据，从遗传和转录水平描述 CRGs 的变化。对 CRGs 进行差异分析、生存和单因素 cox 回归分析，以探讨 CRGs 在 GC 中的作用。基于 CRGs 的表达谱，采用共识无监督聚类分析鉴定铜死亡分子亚型，并进一步进行 GO 和 KEGG 基因集变异分析（GSVA）。对不同分子亚型中的基因进行 GO 和 KEGG 基因富集分析（GSEA）。从不同分子亚型中筛选出差异表达基因（DEGs），并进一步进行 GO 富集分析和单因素 cox 回归分析。对预后 DEGs 进行一致性聚类分析，确定基因组亚型。接下来，患者以 1:1 的比例随机分为训练组和测试组。通过逻辑最小绝对收缩和选择算子（LASSO）cox 回归分析、单因素和多因素 cox 分析在训练组中构建 CRG 风险评分系统，并在测试组和联合组中进行验证。实时定量聚合酶链反应（RT-qPCR）用于评估关键风险评分基因的表达。通过使用接收器操作特征（ROC）曲线评估风险评分系统的敏感性和特异性。R 中的 pRRophetic 包用于研究高风险评分和低风险评分组中药物的治疗效果。最后，开发列线图评分系统，通过纳入临床病理特征和 CRG 风险评分来预测患者的生存情况。

结果：大多数 CRGs 在肿瘤组织中上调，且具有相对较高的突变频率。生存和单因素 cox 回归分析显示，LIAS 和 FDX1 与 GC 患者的生存显著相关。经过共识无监督聚类分析，GC 患者被分为两个铜死亡分子亚型，这两个亚型与 GC 的临床特征（性别、年龄、分级和 TNM 分期）、预后、代谢相关途径和 TME 中的免疫细胞浸润显著相关。从不同分子亚型中获得的 84 个 DEGs 的 GO 富集分析显示，DEGs 主要富集在 GC 中代谢和细胞内/细胞外结构的调控。84 个 DEGs 的单因素 cox 回归分析进一步筛选出 32 个预后 DEGs。根据 32 个预后 DEGs 的表达谱，患者被重新分为两个基因亚型，这两个亚型与患者的年龄、分级、T 和 N 分期以及患者的生存显著相关。巢，风险评分系统具有中等的敏感性和特异性。高 CRG 风险评分，表现为微卫星不稳定高（MSI-H）、肿瘤突变负荷（TMB）和癌症干细胞（CSC）指数降低，以及 TME 中基质和免疫评分升高，预示着不良的生存。五个关键风险评分基因中的四个在肿瘤与正常样本中的表达失调。此外，CRG 风险评分与多种药物的敏感性密切相关。最后，我们建立了一个高度准确的列线图，以提高 CRG 风险评分系统的临床适用性。

讨论：我们对 GC 中 CRGs 的综合分析表明，它们在 TME、临床病理特征和预后方面具有潜在作用。这些发现可能会提高我们对 GC 中 CRGs 的认识，并为医生预测预后和开发更有效和个性化的治疗策略提供新的见解。