Machine learning identifies neutrophil extracellular traps-related biomarkers for acute ischemic stroke diagnosis.

ABSTRACT:

Purpose

This study aimed to investigate the diagnostic potential of neutrophil extracellular traps (NETs)-related genes in acute ischemic stroke (AIS) through comprehensive bioinformatics analysis.

Methods

Two GEO datasets (GSE37587 and GSE16561) were integrated to identify differentially expressed genes (DEGs) between AIS patients and healthy controls. Gene Set Enrichment Analysis (GSEA) was performed to explore functional pathways, while single-sample GSEA (ssGSEA) was used to evaluate immune cell infiltration patterns. NETs-related DEGs (NDEGs) were identified by intersecting the DEGs with previously reported NETS-related genes. Functional enrichment of NDEGs was performed using Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. Key genes were

SUBMITTER: Zhang H 

PROVIDER: S-EPMC12425765 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature