Wang Zhengyan, Wang Ying, Yan Jing, Wei Yuchi, Zhang Yinzhen, Wang Xukai, Leng Xiangyang
Changchun University of Chinese Medicine, Changchun, China.
The Affiliated Hospital of Changchun University of Chinese Medicine, Changchun, China.
Front Med (Lausanne). 2023 Jul 17;10:1115500. doi: 10.3389/fmed.2023.1115500. eCollection 2023.
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.
铜死亡是一种新型细胞死亡形式，由蛋白质脂酰化介导，与线粒体代谢高度相关，且在细胞内受到调控。溃疡性结肠炎（UC）是一种慢性炎症性肠病，易反复发作，其发病率在全球范围内逐年上升。目前，越来越多的研究表明，铜死亡相关基因（CRG）在多种肿瘤的发生发展过程中起着关键作用。然而，CRG在UC中的调控作用尚未完全阐明。首先，我们鉴定了UC中的差异表达基因，同样，评估了CRG表达谱和免疫谱。使用75个UC样本，我们基于CRG的表达谱对UC进行分型，随后进行相关免疫细胞浸润分析。使用加权基因共表达网络分析（WGCNA）方法，生成聚类差异表达基因（DEG）。然后，构建并预测了极端梯度提升模型（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、受试者工作特征曲线（ROC）、ROC曲线下面积（AUC）、校准曲线、列线图和决策曲线分析（DCA）评估最佳机器学习模型的有效性。共鉴定出13个CRG在UC和对照样本中有显著差异。基于CRG表达谱在UC中鉴定出两个亚型。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结果显示共有8个模块在亚型之间存在显著差异，其中绿松石模块最为特异。机器学习结果显示XGB模型性能令人满意（AUC = 0.981）。最后，构建了基于5个基因的最终XGB模型，并通过校准曲线、列线图、决策曲线分析以及五个外部数据集（GSE11223：AUC = 0.987；GSE38713：AUC = 0.815；GSE53306：AUC = 0.946；GSE94648：AUC = 0.809；GSE87466：AUC = 0.981）进行验证，也证明能够准确预测UC的亚型。我们的研究提出了一个可靠的模型，该模型可以预测患UC的可能性，并系统地概述了CRG与UC之间的复杂关系。
