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

Abstract

Background: Epilepsy is a neurological disorder characterized by recurrent seizures. A mechanism of cell death regulation, known as ferroptosis, which involves iron-dependent lipid peroxidation, has been implicated in various diseases, including epilepsy.

Objective: This study aimed to provide a comprehensive understanding of the relationship between ferroptosis and epilepsy through bioinformatics analysis. By identifying key genes, pathways, and potential therapeutic targets, we aimed 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 the 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 was further validated using the receiver operating characteristic (ROC) curve analysis. To explore the relationship between immune infiltration and genes, we employed the CIBERSORT algorithm. Furthermore, we visualized four distinct interaction networks-mRNA-miRNA, mRNA-transcription factor, mRNA-drug, and mRNA-compound-to investigate potential regulatory mechanisms.

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 the pathways related to reactive oxygen species, secondary lysosomes, and ubiquitin protein ligase binding. Furthermore, 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, during 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 CD4-naïve T cells, gamma delta T cells, M1 macrophages, and neutrophils exhibited higher expression in the control group than in the epilepsy group.

Conclusion: This 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.

Keywords: bioinformatic analysis; differentially expressed genes; epilepsy; ferroptosis; immune landscape.