Sun Zhiyue, Ding Zhiqiang, Guo Xiaoyang, Li Jiangtao, Ding Daoyuan, Wen Bo
Shenzhen Clinical College of Integrated Chinese and Western Medicine, Guangzhou University of Chinese Medicine, Shenzhen, 518104, Guang Dong, China.
Shenzhen Hospital of Integrated Traditional Chinese and Western Medicine, Shenzhen, 518104, Guang Dong, China.
Sci Rep. 2025 Jul 14;15(1):25397. doi: 10.1038/s41598-025-09950-8.
Uremia is a serious complication of end-stage chronic kidney disease, closely associated with immune imbalance and chronic inflammation. However, its molecular mechanisms remain largely unclear. In this study, we analyzed transcriptomic data from the GSE37171 dataset to identify genes associated with uremia. Differential expression and WGCNA analyses were used to screen core genes, followed by machine learning (LASSO, Random Forest, SVM-RFE) to identify key feature genes. GSEA and immune infiltration analyses were conducted to explore functional pathways and immune relevance. ROC curves were used to evaluate the discriminatory power of the selected genes. Four feature genes-NAF1, SNORD4A, CGB3, and CD3E-were identified. These genes were enriched in pathways related to apoptosis, immune regulation, and oxidative stress. Their expression levels correlated with multiple immune cell types, and ROC analysis demonstrated good discriminatory performance between uremia and healthy samples. Our findings provide potential molecular candidates for further investigation into the immune-related mechanisms of uremia.
尿毒症是终末期慢性肾脏病的一种严重并发症，与免疫失衡和慢性炎症密切相关。然而，其分子机制在很大程度上仍不清楚。在本研究中，我们分析了来自GSE37171数据集的转录组数据，以确定与尿毒症相关的基因。采用差异表达分析和加权基因共表达网络分析（WGCNA）筛选核心基因，随后通过机器学习（套索回归、随机森林、支持向量机递归特征消除法）确定关键特征基因。进行基因集富集分析（GSEA）和免疫浸润分析，以探索功能通路和免疫相关性。使用受试者工作特征曲线（ROC曲线）评估所选基因的判别能力。确定了四个特征基因——NAF1、SNORD4A、CGB3和CD3E。这些基因在与细胞凋亡、免疫调节和氧化应激相关的通路中富集。它们的表达水平与多种免疫细胞类型相关，ROC分析表明尿毒症样本与健康样本之间具有良好的判别性能。我们的研究结果为进一步研究尿毒症的免疫相关机制提供了潜在的分子候选物。
