AUTHOR=Chen Wenli , Chen Yuanfang , Wu Liting , Gao Yue , Zhu Hangju , Li Ye , Ji Xinyu , Wang Ziyi , Wang Wen , Han Lei , Zhu Baoli , Wang Hongxing , Xu Ming 

TITLE=Identification of cell death-related biomarkers and immune infiltration in ischemic stroke between male and female patients

JOURNAL=Frontiers in Immunology

VOLUME=Volume 14 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2023.1164742

DOI=10.3389/fimmu.2023.1164742

ISSN=1664-3224

ABSTRACT=Background: Stroke is the second cause of death and the third cause of disability worldwide, with ischemic strokes (IS) being the most prevalent. A substantial number of irreversible brain cell death occur short-termly, 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 is to establish the gender-specificity pattern from immune cell infiltration and four kinds of cell-death perspectives to improve IS diagnosis and therapy.
Methods: Combining and standardizing two IS datasets (GSE16561 and GSE22255) from the GEO database, we used the CIBERSORTA 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 IS patient and healthy control groups were identified in males and females 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.
Results: Significant changes were observed in four types of immune cells in IS males and ten in IS females compared with healthy controls. Totally, ten, eleven, three, one FRDEGs, PRDEGs, ARDEGs, and CRDEGs were present in male IS patients, while six, sixteen, four, one FRDEGs, PRDEGs, ARDEGs, and CRDEGs existed in the female ones. Machine learning (ML) techniques indicated that the best diagnostic model for both males and females was the support vector machine (SVM) for CDRDEGs genes. SVM's feature importance analysis demonstrated that SLC2A3, MMP9, C5AR1, ACSL1, and NLRP3 were the top 5 feature-important CDRDEGs in IS males. Meanwhile, the PDK4, SCL40A1, FAR1, CD163, and CD96 displayed their overwhelming influence on IS female patients. 
Conclusion: These 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.