发现和验证缺血性脑卒中患者外周血中的分子模式和免疫特征。

Discovery and validation of molecular patterns and immune characteristics in the peripheral blood of ischemic stroke patients.

机构信息

Department of Neurology, The Second Affiliated Hospital of Harbin Medical University, Harbin Medical University, Harbin, China.

出版信息

PeerJ. 2024 Apr 19;12:e17208. doi: 10.7717/peerj.17208. eCollection 2024.

DOI:10.7717/peerj.17208

PMID:38650649

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11034498/

Abstract

BACKGROUND

Stroke is a disease with high morbidity, disability, and mortality. Immune factors play a crucial role in the occurrence of ischemic stroke (IS), but their exact mechanism is not clear. This study aims to identify possible immunological mechanisms by recognizing immune-related biomarkers and evaluating the infiltration pattern of immune cells.

METHODS

We downloaded datasets of IS patients from GEO, applied R language to discover differentially expressed genes, and elucidated their biological functions using GO, KEGG analysis, and GSEA analysis. The hub genes were then obtained using two machine learning algorithms (least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE)) and the immune cell infiltration pattern was revealed by CIBERSORT. Gene-drug target networks and mRNA-miRNA-lncRNA regulatory networks were constructed using Cytoscape. Finally, we used RT-qPCR to validate the hub genes and applied logistic regression methods to build diagnostic models validated with ROC curves.

RESULTS

We screened 188 differentially expressed genes whose functional analysis was enriched to multiple immune-related pathways. Six hub genes (ANTXR2, BAZ2B, C5AR1, PDK4, PPIH, and STK3) were identified using LASSO and SVM-RFE. ANTXR2, BAZ2B, C5AR1, PDK4, and STK3 were positively correlated with neutrophils and gamma delta T cells, and negatively correlated with T follicular helper cells and CD8, while PPIH showed the exact opposite trend. Immune infiltration indicated increased activity of monocytes, macrophages M0, neutrophils, and mast cells, and decreased infiltration of T follicular helper cells and CD8 in the IS group. The ceRNA network consisted of 306 miRNA-mRNA interacting pairs and 285 miRNA-lncRNA interacting pairs. RT-qPCR results indicated that the expression levels of BAZ2B, C5AR1, PDK4, and STK3 were significantly increased in patients with IS. Finally, we developed a diagnostic model based on these four genes. The AUC value of the model was verified to be 0.999 in the training set and 0.940 in the validation set.

CONCLUSION

Our research explored the immune-related gene expression modules and provided a specific basis for further study of immunomodulatory therapy of IS.

摘要

背景

中风是一种发病率、致残率和死亡率都很高的疾病。免疫因素在缺血性中风（IS）的发生中起着至关重要的作用，但它们的确切机制尚不清楚。本研究旨在通过识别免疫相关生物标志物并评估免疫细胞的浸润模式来确定可能的免疫学机制。

方法

我们从 GEO 下载了中风患者的数据集，应用 R 语言发现差异表达基因，并通过 GO、KEGG 分析和 GSEA 分析阐明其生物学功能。然后使用两种机器学习算法（最小绝对收缩和选择算子（LASSO）和支持向量机递归特征消除（SVM-RFE））获得枢纽基因，并通过 CIBERSORT 揭示免疫细胞浸润模式。使用 Cytoscape 构建基因-药物靶标网络和 mRNA-miRNA-lncRNA 调控网络。最后，我们使用 RT-qPCR 验证枢纽基因，并应用逻辑回归方法构建通过 ROC 曲线验证的诊断模型。

结果

我们筛选出 188 个差异表达基因，其功能分析富集到多个免疫相关途径。使用 LASSO 和 SVM-RFE 鉴定出 6 个枢纽基因（ANTXR2、BAZ2B、C5AR1、PDK4、PPIH 和 STK3）。ANTXR2、BAZ2B、C5AR1、PDK4 和 STK3 与中性粒细胞和γδ T 细胞呈正相关，与滤泡辅助 T 细胞和 CD8 呈负相关，而 PPIH 则呈现相反的趋势。免疫浸润表明单核细胞、巨噬细胞 M0、中性粒细胞和肥大细胞的活性增加，而滤泡辅助 T 细胞和 CD8 的浸润减少。ceRNA 网络包含 306 个 miRNA-mRNA 相互作用对和 285 个 miRNA-lncRNA 相互作用对。RT-qPCR 结果表明，中风患者 BAZ2B、C5AR1、PDK4 和 STK3 的表达水平显著升高。最后，我们基于这四个基因建立了一个诊断模型。该模型在训练集中的 AUC 值验证为 0.999，在验证集中为 0.940。