Zhang Longxiang, Li Jian, Zhang Qiqi, Gao Jianshu, Zhao Keke, Asai Yersen, Hu Ziying, Gao Hongliang
The First Affiliated Hospital of Xinjiang Medical University, Urumqi 830000, Xinjiang, China.
The First Affiliated Hospital of Xinjiang Medical University, Urumqi 830000, Xinjiang, China.
Int Immunopharmacol. 2025 Jan 3;145:113765. doi: 10.1016/j.intimp.2024.113765. Epub 2024 Dec 7.
Ulcerative colitis (UC) is a chronic and refractory inflammatory disease of the colon and rectum. This study utilized bioinformatics methods to explore the potential of Nicotinamide adenine dinucleotide (NAD) metabolism-related genes (NMRGs) as key genes in UC. Using the GSE87466 dataset, differentially expressed NMRGs were identified through differential expression analysis, weighted gene co-expression network analysis (WGCNA), and NMRG scoring. These NMRGs were used as exposure factors, with UC as the outcome, to identify causal candidate genes through Mendelian randomization (MR) analysis. Key genes were further validated as biomarkers using machine learning and expression validation in external datasets (GSE75214, GSE224758). A nomogram based on the expression levels of these biomarkers was constructed to predict UC risk, and the biomarkers' expression was validated through real-time quantitative polymerase chain reaction (RT-qPCR). Subsequently, signaling pathway analysis, enrichment analysis, immune infiltration analysis, and drug prediction were conducted to comprehensively understand the biological roles of the key genes in the human body. Single-cell (GSE116222) and spatial transcriptomic analyses (GSE189184) revealed the expression patterns of these key genes in specific cell types. NCF2, IL1B, S100A8, and SLC26A2 were identified as biomarkers, with NCF2 and IL1B serving as protective factors and S100A8 and SLC26A2 as risk factors for UC. The nomogram based on these biomarkers demonstrated strong predictive value. Functional analysis revealed significant IL1B, NCF2, and S100A8 enrichment in pathways such as IL-4 and IL-13 signaling, while SLC26A2 was strongly associated with respiratory electron transport. Significant differences in immune cells, such as macrophages and neutrophils, were also observed. Single-cell analysis showed high expression of NCF2, IL1B, and S100A8 in monocytes, while SLC26A2 was primarily expressed in epithelial cells, intestinal epithelial cells, and mast cells. Overall, these findings reveal the roles of NMRGs, providing valuable insights into the diagnosis and treatment of UC patients.
溃疡性结肠炎（UC）是一种结肠和直肠的慢性难治性炎症性疾病。本研究利用生物信息学方法探讨烟酰胺腺嘌呤二核苷酸（NAD）代谢相关基因（NMRGs）作为UC关键基因的潜力。使用GSE87466数据集，通过差异表达分析、加权基因共表达网络分析（WGCNA）和NMRG评分来鉴定差异表达的NMRGs。这些NMRGs被用作暴露因素，以UC为结局，通过孟德尔随机化（MR）分析来鉴定因果候选基因。使用机器学习和外部数据集（GSE75214、GSE224758）中的表达验证进一步将关键基因验证为生物标志物。基于这些生物标志物的表达水平构建了列线图以预测UC风险，并通过实时定量聚合酶链反应（RT-qPCR）验证了生物标志物的表达。随后，进行了信号通路分析、富集分析、免疫浸润分析和药物预测，以全面了解关键基因在人体中的生物学作用。单细胞（GSE116222）和空间转录组分析（GSE189184）揭示了这些关键基因在特定细胞类型中的表达模式。NCF2、IL1B、S100A8和SLC26A2被鉴定为生物标志物，其中NCF2和IL1B为保护因素，S100A8和SLC26A2为UC的风险因素。基于这些生物标志物的列线图显示出强大的预测价值。功能分析显示IL1B、NCF2和S100A8在IL-4和IL-13信号等通路中显著富集，而SLC26A2与呼吸电子传递密切相关。还观察到巨噬细胞和中性粒细胞等免疫细胞存在显著差异。单细胞分析显示NCF2、IL1B和S100A8在单核细胞中高表达，而SLC26A2主要在上皮细胞、肠道上皮细胞和肥大细胞中表达。总体而言，这些发现揭示了NMRGs的作用，为UC患者的诊断和治疗提供了有价值的见解。
