铜死亡是一种新型细胞死亡形式，通过蛋白质脂酰化介导，与线粒体代谢密切相关。它在细胞中受到调节。溃疡性结肠炎（UC）是一种经常复发的慢性炎症性肠病，每年在全球范围内发病率都在增加。目前，越来越多的研究表明，与铜死亡相关基因（CRGs）在多种肿瘤的发展和进展中起着关键作用。然而，CRGs在UC中的调控作用尚未完全阐明。首先，我们鉴定了UC中差异表达的基因。同样地，评估了CRGs的表达谱和免疫谱。我们使用75个UC样本，基于CRGs的表达谱对UC进行分型，然后进行相关的免疫细胞浸润分析。使用加权基因共表达网络分析（WGCNA）方法，生成了聚类的差异表达基因（DEGs）。然后，构建并预测了极限梯度提升模型（XGB），支持向量机模型（SVM），随机森林模型（RF）和广义线性模型（GLM）的性能。最后，使用五个外部数据集，接收器操作特征曲线（ROC），ROC曲线下面积（AUC），校准曲线，评分卡和决策曲线分析（DCA），评估了最佳机器学习模型的有效性。共鉴定了13个CRGs在UC和对照样本中差异显著。基于CRGs表达谱鉴定了两个UC亚型。亚型的免疫细胞浸润分析显示不同亚型的免疫细胞之间存在显著差异。WGCNA结果显示了共8个模块，这些模块在亚型之间存在显著差异，其中青色模块最为特异。机器学习结果显示XGB模型的性能满意（AUC=0.981）。最后，通过校准曲线，评分卡，决策曲线分析和五个外部数据集（GSE11223: AUC=0.987; GSE38713: AUC=0.815; GSE53306: AUC=0.946; GSE94648: AUC=0.809; GSE87466: AUC=0.981）验证了基于最终的5个基因的XGB模型的构建，证明了其准确预测UC亚型的能力。我们的研究提供了一个可靠的模型，可预测发展UC的可能性，并系统地概述了CRGs与UC之间的复杂关系。版权所有 © 2023 王，王，严，魏，张，王和冷。

Cuproptosis is a novel form of cell death, mediated by protein lipid acylation and highly associated with mitochondrial metabolism, which is regulated in the cell. Ulcerative colitis (UC) is a chronic inflammatory bowel disease that recurs frequently, and its incidence is increasing worldwide every year. Currently, a growing number of studies have shown that cuproptosis-related genes (CRGs) play a crucial role in the development and progression of a variety of tumors. However, the regulatory role of CRGs in UC has not been fully elucidated. Firstly, we identified differentially expressed genes in UC, Likewise, CRGs expression profiles and immunological profiles were evaluated. Using 75 UC samples, we typed UC based on the expression profiles of CRGs, followed by correlative immune cell infiltration analysis. Using the weighted gene co-expression network analysis (WGCNA) methodology, the cluster's differentially expressed genes (DEGs) were produced. Then, the performances of extreme gradient boosting models (XGB), support vector machine models (SVM), random forest models (RF), and generalized linear models (GLM) were constructed and predicted. Finally, the effectiveness of the best machine learning model was evaluated using five external datasets, receiver operating characteristic curve (ROC), the area under the curve of ROC (AUC), a calibration curve, a nomogram, and a decision curve analysis (DCA). A total of 13 CRGs were identified as significantly different in UC and control samples. Two subtypes were identified in UC based on CRGs expression profiles. Immune cell infiltration analysis of subtypes showed significant differences between immune cells of different subtypes. WGCNA results showed a total of 8 modules with significant differences between subtypes, with the turquoise module being the most specific. The machine learning results showed satisfactory performance of the XGB model (AUC = 0.981). Finally, the construction of the final 5-gene-based XGB model, validated by the calibration curve, nomogram, decision curve analysis, and five external datasets (GSE11223: AUC = 0.987; GSE38713: AUC = 0.815; GSE53306: AUC = 0.946; GSE94648: AUC = 0.809; GSE87466: AUC = 0.981), also proved to predict subtypes of UC with accuracy. Our research presents a trustworthy model that can predict the likelihood of developing UC and methodically outlines the complex relationship between CRGs and UC.Copyright © 2023 Wang, Wang, Yan, Wei, Zhang, Wang and Leng.