Sun Yameng, Ding Shenghao, Shen Fei, Yang Xiaolan, Sun Wenhua, Wan Jieqing
Cerebrovascular Disease Center, Renji Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai 200127, China.
Department of Neurosurgery, Renji Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai 200127, China.
Exp Gerontol. 2024 Oct 15;196:112584. doi: 10.1016/j.exger.2024.112584. Epub 2024 Sep 19.
Ischemic stroke (IS) is a severe condition regulated by complex molecular alterations. This study aimed to identify potential nicotinamide adenine dinucleotide (NAD+) metabolism-associated diagnostic markers of IS and explore their associations with immune dynamics. Weighted Gene Co-expression Network Analysis and single-sample gene set enrichment analysis (ssGSEA) were employed to identify key gene modules on the GEO dataset (GSE16561). LASSO regression was used to identify diagnostic genes. A diagnostic model was then developed using the training dataset, and its performance was assessed using a validation dataset (GSE22255 dataset). Associations between hub genes and immune cells, immune response genes, and human leukocyte antigen (HLA) genes were assessed by ssGSEA. A regulatory network was constructed using mirBase and TRRUST databases. A total of 20 NAD+ metabolic genes exhibited noteworthy expression variations. Within the module notably associated with NAD+ metabolism, 19 specific genes were included in the diagnostic model, which was validated on the GSE22255 dataset (AUC: 0.733). There were significant disparities in immune cell populations, immune response genes, and HLA gene expression, all of which were associated with the hub genes. A regulatory network composed of 153 edges and 103 nodes was constructed. This study advances our understanding of IS by providing insights into NAD+ metabolism and gene interactions, contributing to potential diagnostic innovations in IS.
缺血性中风（IS）是一种由复杂分子改变所调控的严重病症。本研究旨在识别缺血性中风潜在的烟酰胺腺嘌呤二核苷酸（NAD+）代谢相关诊断标志物，并探索它们与免疫动态的关联。采用加权基因共表达网络分析和单样本基因集富集分析（ssGSEA）来识别GEO数据集（GSE16561）上的关键基因模块。使用LASSO回归来识别诊断基因。然后利用训练数据集开发诊断模型，并使用验证数据集（GSE22255数据集）评估其性能。通过ssGSEA评估枢纽基因与免疫细胞、免疫反应基因和人类白细胞抗原（HLA）基因之间的关联。使用mirBase和TRRUST数据库构建调控网络。共有20个NAD+代谢基因表现出显著的表达变化。在与NAD+代谢显著相关的模块中，19个特定基因被纳入诊断模型，该模型在GSE22255数据集上得到验证（AUC：0.733）。免疫细胞群体、免疫反应基因和HLA基因表达存在显著差异，所有这些都与枢纽基因相关。构建了一个由153条边和103个节点组成的调控网络。本研究通过深入了解NAD+代谢和基因相互作用，增进了我们对缺血性中风的认识，为缺血性中风潜在的诊断创新做出了贡献。
