Jia Juan, Niu Liang, Feng Peng, Liu Shangyu, Han Hongxi, Zhang Bo, Wang Yingbin, Wang Manxia
Lanzhou University Second Hospital, The Second Medical College of Lanzhou University, Cuiyingmen No.82, Chengguan District, Lanzhou, 730030, China.
Department of Anesthesiology, Second Hospital of Lanzhou University, Lanzhou, 730030, China.
J Mol Neurosci. 2025 Jan 25;75(1):13. doi: 10.1007/s12031-025-02309-8.
Ischemic stroke leads to permanent damage to the affected brain tissue, with strict time constraints for effective treatment. Predictive biomarkers demonstrate great potential in the clinical diagnosis of ischemic stroke, significantly enhancing the accuracy of early identification, thereby enabling clinicians to intervene promptly and reduce patient disability and mortality rates. Furthermore, the application of predictive biomarkers facilitates the development of personalized treatment plans tailored to the specific conditions of individual patients, optimizing treatment outcomes and improving prognoses. Bioinformatics technologies based on high-throughput data provide a crucial foundation for comprehensively understanding the biological characteristics of ischemic stroke and discovering effective predictive targets. In this study, we evaluated gene expression data from ischemic stroke patients retrieved from the Gene Expression Omnibus (GEO) database, conducting differential expression analysis and functional analysis. Through weighted gene co-expression network analysis (WGCNA), we characterized gene modules associated with ischemic stroke. To screen candidate core genes, three machine learning algorithms were applied, including Least Absolute Shrinkage and Selection Operator (LASSO), random forest (RF), and support vector machine-recursive feature elimination (SVM-RFE), ultimately identifying five candidate core genes: MBOAT2, CKAP4, FAF1, CLEC4D, and VIM. Subsequent validation was performed using an external dataset. Additionally, the immune infiltration landscape of ischemic stroke was mapped using the CIBERSORT method, investigating the relationship between candidate core genes and immune cells in the pathogenesis of ischemic stroke, as well as the key pathways associated with the core genes. Finally, the key gene VIM was further identified and preliminarily validated through four machine learning algorithms, including generalized linear model (GLM), Extreme Gradient Boosting (XGBoost), RF, and SVM-RFE. This study contributes to advancing our understanding of biomarkers for ischemic stroke and provides a reference for the prediction and diagnosis of ischemic stroke.
缺血性中风会导致受影响的脑组织永久性损伤，有效治疗有严格的时间限制。预测性生物标志物在缺血性中风的临床诊断中显示出巨大潜力，显著提高早期识别的准确性，从而使临床医生能够及时进行干预，降低患者的残疾率和死亡率。此外，预测性生物标志物的应用有助于制定针对个体患者具体情况的个性化治疗方案，优化治疗效果并改善预后。基于高通量数据的生物信息学技术为全面了解缺血性中风的生物学特征和发现有效的预测靶点提供了关键基础。在本研究中，我们评估了从基因表达综合数据库（GEO）检索到的缺血性中风患者的基因表达数据，进行了差异表达分析和功能分析。通过加权基因共表达网络分析（WGCNA），我们对与缺血性中风相关的基因模块进行了特征描述。为了筛选候选核心基因，应用了三种机器学习算法，包括最小绝对收缩和选择算子（LASSO）、随机森林（RF）和支持向量机递归特征消除（SVM-RFE），最终确定了五个候选核心基因：MBOAT2、CKAP4、FAF1、CLEC4D和VIM。随后使用外部数据集进行了验证。此外，使用CIBERSORT方法绘制了缺血性中风的免疫浸润图谱，研究了候选核心基因与缺血性中风发病机制中免疫细胞之间的关系，以及与核心基因相关的关键途径。最后，通过广义线性模型（GLM）、极端梯度提升（XGBoost）、RF和SVM-RFE这四种机器学习算法进一步鉴定并初步验证了关键基因VIM。本研究有助于推进我们对缺血性中风生物标志物的理解，并为缺血性中风的预测和诊断提供参考。
