DataSheet_1_Identification of cell death-related biomarkers and immune infiltration in ischemic stroke between male and female patients.xlsx

BackgroundStroke is the second leading cause of death and the third leading cause of disability worldwide, with ischemic stroke (IS) being the most prevalent. A substantial number of irreversible brain cell death occur in the short term, leading to impairment or death in IS. Limiting the loss of brain cells is the primary therapy target and a significant clinical issue for IS therapy. Our study aims to establish the gender specificity pattern from immune cell infiltration and four kinds of cell-death perspectives to improve IS diagnosis and therapy.
MethodsCombining and standardizing two IS datasets (GSE16561 and GSE22255) from the GEO database, we used the CIBERSORT algorithm to investigate and compare the immune cell infiltration in different groups and genders. Then, ferroptosis-related differently expressed genes (FRDEGs), pyroptosis-related DEGs (PRDEGs), anoikis-related DEGs (ARDEGs), and cuproptosis-related DEGs (CRDEGs) between the IS patient group and the healthy control group were identified in men and women, respectively. Machine learning (ML) was finally used to generate the disease prediction model for cell death-related DEGs (CDRDEGs) and to screen biomarkers related to cell death involved in IS.
ResultsSignificant changes were observed in 4 types of immune cells in male IS patients and 10 types in female IS patients compared with healthy controls. In total, 10 FRDEGs, 11 PRDEGs, 3 ARDEGs, and 1 CRDEG were present in male IS patients, while 6 FRDEGs, 16 PRDEGs, 4 ARDEGs, and 1 CRDEG existed in female IS patients. ML techniques indicated that the best diagnostic model for both male and female patients was the support vector machine (SVM) for CDRDEG genes. SVM’s feature importance analysis demonstrated that SLC2A3, MMP9, C5AR1, ACSL1, and NLRP3 were the top five feature-important CDRDEGs in male IS patients. Meanwhile, the PDK4, SCL40A1, FAR1, CD163, and CD96 displayed their overwhelming influence on female IS patients.
ConclusionThese findings contribute to a better knowledge of immune cell infiltration and their corresponding molecular mechanisms of cell death and offer distinct clinically relevant biological targets for IS patients of different genders.

脑卒中是全球第二大死亡原因、第三大致残原因，其中缺血性脑卒中（ischemic stroke, IS）最为常见。短时间内即可发生大量不可逆的脑细胞死亡，进而导致缺血性脑卒中患者出现神经功能缺损甚至死亡。减少脑细胞丢失是缺血性脑卒中治疗的首要靶点，也是临床面临的重大难题。本研究旨在从免疫细胞浸润与四种细胞死亡类型的视角，构建性别特异性特征谱，以优化缺血性脑卒中的诊断与治疗方案。
方法：本研究整合并标准化了来自基因表达综合数据库（Gene Expression Omnibus, GEO）的两项缺血性脑卒中数据集（GSE16561与GSE22255），采用CIBERSORT算法分析并对比了不同组别及不同性别受试者的免疫细胞浸润情况。随后，本研究分别在男性与女性群体中，筛选出缺血性脑卒中患者组与健康对照组之间的铁死亡相关差异表达基因（ferroptosis-related differently expressed genes, FRDEGs）、焦亡相关差异表达基因（pyroptosis-related DEGs, PRDEGs）、失巢凋亡相关差异表达基因（anoikis-related DEGs, ARDEGs）与铜死亡相关差异表达基因（cuproptosis-related DEGs, CRDEGs）。最终，本研究采用机器学习（machine learning, ML）方法构建基于细胞死亡相关差异表达基因（cell death-related DEGs, CDRDEGs）的疾病预测模型，并筛选出参与缺血性脑卒中发生的细胞死亡相关生物标志物。
结果：与健康对照组相比，男性缺血性脑卒中患者体内有4种免疫细胞发生显著变化，而女性患者体内则有10种免疫细胞出现显著改变。男性缺血性脑卒中患者中共筛选出10个铁死亡相关差异表达基因、11个焦亡相关差异表达基因、3个失巢凋亡相关差异表达基因以及1个铜死亡相关差异表达基因；而女性患者中则分别存在6个、16个、4个与1个上述四类差异表达基因。机器学习分析结果显示，针对男性与女性患者，基于细胞死亡相关差异表达基因构建的最优诊断模型均为支持向量机（support vector machine, SVM）。支持向量机的特征重要性分析表明，SLC2A3、MMP9、C5AR1、ACSL1以及NLRP3是影响男性缺血性脑卒中患者的前5位核心细胞死亡相关差异表达基因。与此同时，PDK4、SLC40A1、FAR1、CD163与CD96则对女性缺血性脑卒中患者具有最为显著的影响。
结论：本研究结果有助于深化对缺血性脑卒中免疫细胞浸润模式及其对应细胞死亡分子机制的认识，并为不同性别的缺血性脑卒中患者提供具有临床转化价值的特异性生物学靶点。