Neutrophil-Related Gene Signatures for Ischemic Stroke Diagnosis

Ischemic stroke (IS) is a major cause of death and disability worldwide. The transcriptional mechanism of neutrophil extracellular trap-related genes (NRGs) and their diagnostic potential remain unknown. This study aims to explore the mechanism of NRGs in IS through machine learning and Single-cell RNA sequencing (scRNA-seq).

We conducted differential analysis and functional enrichment analysis on the GEO dataset. Machine learning algorithms were used to identify NRGs related to IS. ScRNA-seq analysis was employed to verify the expression of NRGs in different cell types, and cellchat was used to explore the interactions between cell types in the IS. The expression of Eno1 was also verified in the mouse model of middle cerebral artery occlusion (MCAO).

We identified 26 differentially expressed NRGs (DE-NRGs). The diagnostic models constructed from five DE-NRGs (ENO1, HMGB1, ILK, ORAI1, SUCNR1) demonstrated high predictive ability. Single-cell analysis revealed that NRGs were highly expressed in the IS group. The experiment verified the significant upregulation of Eno1.

This study employed machine learning and scRNA-seq to identify the DENRGs- related diagnostic model, providing a certain theoretical basis for IS risk stratification. More experiments are needed to verify the role of DE-NRGs in IS in the future.

This study identified DE-NRGs with diagnostic capabilities in IS and verified their high expression through scRNA and experimental methods. DE-NRGs may be potential therapeutic targets for IS.