Abstract
BACKGROUND: This research utilized a combination of gene databases associated with ferroptosis and online gene expression data from ischemic stroke samples to pinpoint ferroptosis-related genes (FRGs) in ischemic stroke cases. METHODS: By employing Random Forest (RF) and Support Vector Machine (SVM) models based on these genes, an overlap of genes from both models was identified as "Hub" genes. Through consensus clustering analysis using Hub genes, two distinct clusters of FRGs were revealed in ischemic stroke samples. Examination of the correlation between these molecular subtypes and the immune microenvironment highlighted a close link between gene expression levels and immune cell infiltration. Significantly different gene expression and functions within the FRG clusters underscored the pivotal role of Hub genes in the immune microenvironment. A gene diagnostic model related to ferroptosis was developed and validated to elucidate the significance of the identified genes. RESULTS: The results demonstrated that the Hub gene-based classification model effectively differentiated between ischemic stroke patients and normal samples, achieving an AUC of 0.900, signifying clinical relevance. CONCLUSION: This study successfully identified ferroptosis-related genes in ischemic stroke, offering insights that could contribute to the formulation of future comprehensive treatment approaches.