Table_1_Identification of cuproptosis-associated subtypes and signature genes for diagnosis and risk prediction of Ulcerative colitis based on machine learning.xlsx

BackgroundUlcerative colitis (UC) is a chronic and debilitating inflammatory bowel disease that impairs quality of life. Cuproptosis, a recently discovered form of cell death, has been linked to many inflammatory diseases, including UC. This study aimed to examine the biological and clinical significance of cuproptosis-related genes in UC.
MethodsThree gene expression profiles of UC were obtained from the Gene Expression Omnibus (GEO) database to form the combined dataset. Differential analysis was performed based on the combined dataset to identify differentially expressed genes, which were intersected with cuproptosis-related genes to obtain differentially expressed cuproptosis-related genes (DECRGs). Machine learning was conducted based on DECRGs to identify signature genes. The prediction model of UC was established using signature genes, and the molecular subtypes related to cuproptosis of UC were identified. Functional enrichment analysis and immune infiltration analysis were used to evaluate the biological characteristics and immune infiltration landscape of signature genes and molecular subtypes.
ResultsSeven signature genes (ABCB1, AQP1, BACE1, CA3, COX5A, DAPK2, and LDHD) were identified through the machine learning algorithms, and the nomogram built from these genes had excellent predictive performance. The 298 UC samples were divided into two subtypes through consensus cluster analysis. The results of the functional enrichment analysis and immune infiltration analysis revealed significant differences in gene expression patterns, biological functions, and enrichment pathways between the cuproptosis-related molecular subtypes of UC. The immune infiltration analysis also showed that the immune cell infiltration in cluster A was significantly higher than that of cluster B, and six of the characteristic genes (excluding BACE1) had higher expression levels in subtype B than in subtype A.
ConclusionsThis study identified several promising signature genes and developed a nomogram with strong predictive capabilities. The identification of distinct subtypes of UC enhances our current understanding of UC’s underlying pathogenesis and provides a foundation for personalized diagnosis and treatment in the future.

背景：溃疡性结肠炎（Ulcerative colitis, UC）是一种慢性致残性炎症性肠病，会严重损害患者的生活质量。铜死亡（Cuproptosis）是近年新发现的细胞死亡形式，已被证实与包括UC在内的多种炎症性疾病存在关联。本研究旨在探讨铜死亡相关基因在UC中的生物学及临床意义。
方法：从基因表达综合数据库（Gene Expression Omnibus, GEO）中获取3组UC基因表达谱以构建合并数据集。基于该合并数据集开展差异表达分析，筛选差异表达基因，并将其与铜死亡相关基因取交集，得到差异表达铜死亡相关基因（differentially expressed cuproptosis-related genes, DECRGs）。基于DECRGs进行机器学习分析以筛选特征基因。利用特征基因构建UC预测模型，并识别UC中与铜死亡相关的分子亚型。通过功能富集分析与免疫浸润分析，评估特征基因及分子亚型的生物学特征与免疫浸润格局。
结果：通过机器学习算法共筛选得到7个特征基因（ABCB1、AQP1、BACE1、CA3、COX5A、DAPK2、LDHD），基于这些基因构建的列线图（nomogram）展现出优异的预测性能。对298例UC样本进行一致性聚类分析，将其划分为2个亚型。功能富集分析与免疫浸润分析结果显示，UC的铜死亡相关分子亚型在基因表达模式、生物学功能及富集通路上均存在显著差异。免疫浸润分析还发现，簇A的免疫细胞浸润水平显著高于簇B，且除BACE1外的6个特征基因在亚型B中的表达水平均高于亚型A。
结论：本研究筛选得到多个具有应用前景的特征基因，并构建了预测能力优异的列线图。对UC不同亚型的识别加深了我们对UC潜在发病机制的理解，可为未来个体化诊疗提供理论基础。