Zhang Haipeng, Wu Ti, Li Xinghua, Liu Shuangqing, Wang Yuanyuan, Cao Yang
Department of Clinical Laboratory, The Second Hospital of Tianjin Medical University, Tianjin, China.
Department of Neurology, Tianjin Medical University General Hospital, Tianjin, China.
Front Neurol. 2025 Aug 29;16:1611776. doi: 10.3389/fneur.2025.1611776. eCollection 2025.
This study aimed to investigate the diagnostic potential of neutrophil extracellular traps (NETs)-related genes in acute ischemic stroke (AIS) through comprehensive bioinformatics analysis.
Two GEO datasets (GSE37587 and GSE16561) were integrated to identify differentially expressed genes (DEGs) between AIS patients and healthy controls. Gene Set Enrichment Analysis (GSEA) was performed to explore functional pathways, while single-sample GSEA (ssGSEA) was used to evaluate immune cell infiltration patterns. NETs-related DEGs (NDEGs) were identified by intersecting the DEGs with previously reported NETS-related genes. Functional enrichment of NDEGs was performed using Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. Key genes were identified via machine learning algorithms, including least absolute shrinkage and selection operator (LASSO) and random forest (RF). A diagnostic model was constructed based on the identified hub genes and validated using an independent dataset (GSE58294). Potential regulatory miRNAs and candidate therapeutic compounds were predicted using the TargetScan and DSigDB databases, respectively.
The discovery dataset included 73 AIS patients and 24 healthy controls, revealed 551 DEGs (225 upregulated, 326 downregulated). The analysis of ssGSEA revealed notable immune dysregulation in AIS patients, characterized by increased neutrophil infiltration and decreased level of Th17, Th1, and TFH cells. GSEA indicated that DEGs were enriched in neutrophil degranulation and innate immune system. NDEGs were significantly enriched in immune regulation and leukocyte apoptosis (GO) and NETs formation pathway (KEGG). Four hub genes-, and -were identified using LASSO and RF algorithms. A diagnostic model based on these genes yielded area under the curve (AUC) values of 0.880 in the training dataset and 0.936 in the validation dataset. Furthermore, three regulatory miRNAs (miR-146a-5p, miR-155-5p, and miR-21-5p) and 23 candidate therapeutic drugs were predicted.
To our knowledge, this represents the first comprehensive investigation of NETs-related gene signatures in AIS patients compared with healthy controls. These findings deepen our understanding of immune cell infiltration and the underlying molecular mechanisms involved in stroke, offering novel insights that may enhance diagnostic accuracy and therapeutic strategies for AIS.
本研究旨在通过全面的生物信息学分析，探讨中性粒细胞胞外诱捕网（NETs）相关基因在急性缺血性卒中（AIS）中的诊断潜力。
整合两个基因表达综合数据库（GEO）数据集（GSE37587和GSE16561），以鉴定AIS患者与健康对照之间的差异表达基因（DEGs）。进行基因集富集分析（GSEA）以探索功能通路，同时使用单样本GSEA（ssGSEA）评估免疫细胞浸润模式。通过将DEGs与先前报道的NETs相关基因进行交叉，鉴定出NETs相关的DEGs（NDEGs）。使用基因本体论（GO）和京都基因与基因组百科全书（KEGG）分析对NDEGs进行功能富集。通过机器学习算法，包括最小绝对收缩和选择算子（LASSO）和随机森林（RF），鉴定关键基因。基于鉴定出的枢纽基因构建诊断模型，并使用独立数据集（GSE58294）进行验证。分别使用TargetScan和DSigDB数据库预测潜在的调控性微小RNA（miRNA）和候选治疗化合物。
发现数据集包括73例AIS患者和24例健康对照，共鉴定出551个DEGs（225个上调，326个下调）。ssGSEA分析显示AIS患者存在明显的免疫失调，其特征为中性粒细胞浸润增加以及Th17、Th1和滤泡辅助性T细胞（TFH）水平降低。GSEA表明DEGs在中性粒细胞脱颗粒和固有免疫系统中富集。NDEGs在免疫调节和白细胞凋亡（GO）以及NETs形成途径（KEGG）中显著富集。使用LASSO和RF算法鉴定出四个枢纽基因。基于这些基因的诊断模型在训练数据集中的曲线下面积（AUC）值为0.880，在验证数据集中为0.936。此外，预测出三种调控性miRNA（miR-146a-5p、miR-155-5p和miR-21-5p）以及23种候选治疗药物。
据我们所知，这是首次将AIS患者与健康对照进行比较，全面研究NETs相关基因特征。这些发现加深了我们对免疫细胞浸润以及卒中潜在分子机制的理解，为提高AIS的诊断准确性和治疗策略提供了新的见解。
