AUTHOR=Xu Dan , Chu ManMan , Chen YaoYao , Fang Yang , Wang JingGuang , Zhang XiaoLi , Xu FaLin 

TITLE=Identification and verification of ferroptosis-related genes in the pathology of epilepsy: insights from CIBERSORT algorithm analysis

JOURNAL=Frontiers in Neurology

VOLUME=Volume 14 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2023.1275606

DOI=10.3389/fneur.2023.1275606

ISSN=1664-2295

ABSTRACT=Objective: This study aims to provide a comprehensive understanding of the relationship between ferroptosis and epilepsy through bioinformatic analysis. By identifying key genes, pathways, and potential therapeutic targets, we aim to shed light on the underlying mechanisms involved in the pathogenesis of epilepsy.Materials and Methods: We conducted a comprehensive analysis by screening gene expression data from the Gene Expression Omnibus (GEO) database and identified differentially expressed genes (DEGs) related to ferroptosis. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to gain insights into the biological processes and pathways involved. Moreover, we constructed a protein-protein interaction (PPI) network to identify hub genes, which were further validated using Receiver Operator Characteristic (ROC) curve analysis. To explore the relationship between immune infiltration and genes, we employed the CIBERSORT algorithm. Results: In this study, we identified a total of 33 differentially expressed genes (FDEGs) associated with epilepsy and presented them using a Venn diagram. Enrichment analysis revealed significant enrichment in pathways related to reactive oxygen species, secondary lysosomes, and ubiquitin protein ligase binding. Further GSVA enrichment analysis highlighted significant differences between epilepsy and control groups in terms of the generation of precursor metabolites and energy, chaperone complex, and antioxidant activity in Gene Ontology (GO) analysis. Furthermore, in the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, we observed differential expression in pathways associated with amyotrophic lateral sclerosis (ALS) and acute myeloid leukemia (AML) between the two groups. To identify hub genes, we constructed a protein-protein interaction (PPI) network using 30 FDEGs and utilized algorithms. This analysis led to the identification of three hub genes, namely HIF1A, TLR4, and CASP8. The application of the CIBERSORT algorithm allowed us to explore the immune infiltration patterns between epilepsy and control groups. We found that T cells CD4_naïve, T cells gamma delta, Macrophages M1, and Neutrophils exhibited higher expression in the control group compared to the epilepsy group.The present study identified three FDEGs and analyzed the immune cells in epilepsy. These findings pave the way for future research and the development of innovative therapeutic strategies for epilepsy.