Huang Chenyi, Wu Dengxuan, Yang Guifen, Huang Chuchu, Li Li
Rehabilitation Department, Tongde Hospital of Zhejiang Province, Hangzhou, 310012, Zhejiang, China.
Eur J Med Res. 2025 Mar 22;30(1):195. doi: 10.1186/s40001-025-02454-1.
Ischemic stroke (IS) is one of the most common causes of disability in adults worldwide. This study aimed to identify key genes related to the inflammatory response to provide insights into the mechanisms and management of IS.
Transcriptomic data for IS were downloaded from the Gene Expression Omnibus (GEO) database. Weighted gene co-expression network analysis (WGCNA) and differential expression analysis were used to identify inflammation-related genes (IRGs) associated with IS. Hub IRGs were screened using Lasso, SVM-RFE, and random forest algorithms, and a nomogram diagnostic model was constructed. The diagnostic performance of the model was assessed using receiver operating characteristic (ROC) curves and calibration plots. Additionally, immune cell infiltration and potential small molecule drugs targeting IRGs were analyzed. The expression of IRG was verified by qRT-PCR in healthy controls and IS patients.
Nine differentially expressed IRGs were identified in IS, including NMUR1, AHR, CD68, OSM, CDKN1A, RGS1, BTG2, ATP2C1, and TLR3. Machine learning algorithms selected three hub IRGs (AHR, OSM, and NMUR1). A diagnostic model based on these three genes showed excellent diagnostic performance for IS, with an area under the curve (AUC) greater than 0.9 in both the training and validation sets. Immune infiltration analysis revealed higher levels of neutrophils and activated CD4 + T cells, and lower levels of CD8 + T cells, activated NK cells, and naive B cells in IS patients. The hub IRGs exhibited significant correlations with immune cell infiltration. Furthermore, small molecule drugs targeting hub IRGs were identified, including chrysin, piperine, genistein, and resveratrol, which have potential therapeutic effects for IS. qRT-PCR evaluation demonstrated that the levels of blood biomarkers (AHR, OSM, and NMUR1) in IS patients could serve as distinguishing indicators between IS patients and healthy controls (P < 0.05).
This study confirmed the significant impact of IRGs on the progression of IS and provided new diagnostic and therapeutic targets for personalized treatment of IS.
缺血性中风（IS）是全球成年人残疾的最常见原因之一。本研究旨在识别与炎症反应相关的关键基因，以深入了解IS的发病机制和治疗方法。
从基因表达综合数据库（GEO）下载IS的转录组数据。采用加权基因共表达网络分析（WGCNA）和差异表达分析来识别与IS相关的炎症相关基因（IRGs）。使用套索回归、支持向量机递归特征消除（SVM-RFE）和随机森林算法筛选核心IRGs，并构建列线图诊断模型。使用受试者工作特征（ROC）曲线和校准图评估模型的诊断性能。此外，分析了免疫细胞浸润情况以及针对IRGs的潜在小分子药物。通过qRT-PCR在健康对照和IS患者中验证IRG的表达。
在IS中鉴定出9个差异表达的IRGs，包括NMUR1、AHR、CD68、OSM、CDKN1A、RGS1、BTG2、ATP2C1和TLR3。机器学习算法选择了3个核心IRGs（AHR、OSM和NMUR1）。基于这三个基因的诊断模型对IS显示出优异的诊断性能，训练集和验证集的曲线下面积（AUC）均大于0.9。免疫浸润分析显示，IS患者的中性粒细胞和活化CD4 + T细胞水平较高，而CD8 + T细胞、活化NK细胞和幼稚B细胞水平较低。核心IRGs与免疫细胞浸润显著相关。此外，还鉴定出针对核心IRGs的小分子药物，包括白杨素、胡椒碱、染料木黄酮和白藜芦醇，它们对IS具有潜在治疗作用。qRT-PCR评估表明，IS患者血液生物标志物（AHR、OSM和NMUR1）水平可作为IS患者与健康对照之间的鉴别指标（P < 0.05）。
本研究证实了IRGs对IS进展的显著影响，并为IS的个性化治疗提供了新的诊断和治疗靶点。
