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: ferroptosis, epilepsy, bioinformatic analysis, differentially
   expressed genes, immune landscape

1. Introduction

   Epilepsy is a chronic neurological disorder characterized by recurrent
   and unpredictable seizures. Seizures are caused by abnormal electrical
   activity in the brain, which can result in sudden and uncontrolled
   bursts of abnormal brain cell firing, leading to various symptoms
   ([35]1, [36]2). These symptoms can be mild, such as brief periods of
   impaired consciousness, confusion, or altered sensations. They can also
   be severe, including muscle convulsions, fainting, loss of
   consciousness, generalized convulsions, and foaming at the mouth
   ([37]3). In recent years, our understanding of the pathogenesis of
   epilepsy has deepened, although the exact mechanisms behind its onset
   are still unclear ([38]4). To provide more precise guidance for
   preventing and treating epilepsy, it is necessary to unravel its
   pathogenesis at the molecular level. In this study, we utilized a
   dataset of epilepsy from the Gene Expression Omnibus (GEO) database and
   conducted bioinformatics analysis on the genes associated with
   epilepsy.

   Ferroptosis is a similar form of cell death with distinctive biological
   features to other common cell death mechanisms. A prominent feature of
   ferroptosis is the accumulation of free iron within cells resulting in
   an irreversible lipid peroxidation reaction ([39]5). Excessive
   accumulation of intracellular ions can lead to lipid oxidation,
   membrane damage, and functional impairment, ultimately resulting in
   cell death. Factors involved in the regulation of ferroptosis include
   lipid peroxidation products, antioxidants, and iron-related proteins,
   as well as molecules and pathways associated with cell survival and
   death ([40]6). Significant progress has been made in the study of
   ferroptosis, particularly in understanding its regulatory mechanisms,
   related signaling pathways, and its association with the development of
   epilepsy ([41]7, [42]8). These studies contribute to elucidating the
   deeper mechanisms underlying cell death and provide theoretical and
   practical foundations for developing novel therapeutic approaches for
   epilepsy.

   Gene set variation analysis (GSVA) is a non-parametric unsupervised
   analysis method primarily used to transform gene expression matrices
   into gene set expression matrices for the evaluation of transcriptional
   enrichment of different metabolic pathways among different samples
   ([43]9, [44]10). To investigate the biological process variations
   between epilepsy patients and normal controls, the R package “GSVA” can
   be employed for gene set variation analysis based on gene expression
   profiling datasets from different epilepsy patients and normal
   controls.

   Ferroptosis plays an important role in epilepsy, and this research aims
   to identify key genes associated with epilepsy and ferroptosis, which
   may serve as novel biomarkers or possible therapeutic targets for
   epilepsy. In this study, the CIBERSORT algorithm was performed to
   evaluate the immune infiltration characteristics of epilepsy and normal
   samples in the integrated dataset ([45]GSE32534 and [46]GSE143272),
   assessing the composition of 22 immune cell types in each group. The
   selected hub genes were then associated with the levels of immune cell
   infiltration using Pearson correlation coefficients and significance
   levels. Finally, a compound network, drug network, mRNA–miRNA network,
   and transcription factor network of the three hub genes were
   constructed. This study provides a research foundation for exploring
   potential regulatory targets and possible mechanisms of epilepsy,
   offering new insights into the treatment of this disease.

2. Materials and methods

2.1. Data source and preprocessing

   Ferroptosis genes were selected from the GeneCards database ([47]11).
   Gene expression data of epilepsy including [48]GSE32534 and
   [49]GSE143272 were obtained from the Gene Expression Omnibus (GEO)
   database ([50]12, [51]13). [52]GSE32534 is based on the [53]GPL570
   platform, which includes 10 tissue samples, with five being Epilepsy
   patients and five being normal controls. The other dataset
   [54]GSE143272 is based on the [55]GPL10558 platform and includes 34
   epilepsy patients and 50 normal controls. The detailed information on
   both datasets is shown in [56]Table 1.

Table 1.

   Data set information.
   ID GPL Sample source Sample size References Species