Screening, Analysis, and Validation of Endoplasmic Reticulum Stress-Related DEGs in Epilepsy

Abstract

To investigate the relationship between Endoplasmic Reticulum Stress (ERS) and epilepsy, as well as their biological functions. We downloaded the GSE143272 dataset from the GEO database, identified differentially expressed genes (DEGs), and cross-analyzed them with ERS-related genes from GeneCards and the Molecular Signatures Database (MSigDB). Protein-protein interaction (PPI) networks were constructed, and Hub genes were screened. ROC curve analysis was conducted to assess the diagnostic utility of these genes, followed by qRT-PCR validation. This study identified a total of 83 ERS-related DEGs in epilepsy. PPI network analysis revealed eight feature genes: C-X-C motif chemokine ligand 8 (CXCL8), Toll-like receptor 4 (TLR4), Matrix metalloproteinase 9 (MMP9), Tumor necrosis factor receptor superfamily member 1A (TNFRSF1A), Prostaglandin-endoperoxide synthase 2 (PTGS2), Signal transducer and activator of transcription 1 (STAT1), B-cell lymphoma 2 (BCL2), and RELA proto-oncogene, NF-κB subunit (RELA). ROC curve analysis demonstrated that the combination of these eight feature genes exhibited the highest diagnostic potential. Among them, CXCL8 was the most valuable gene. qRT-PCR analysis showed that CXCL8 mRNA expression was significantly lower in the case group compared to the control group (P < 0.01). The results suggest that ERS is involved in physiological processes such as inflammation and neuronal apoptosis in epilepsy. This provides a bioinformatics evidence for exploring the biological functions and pathology of ERS in epilepsy, as well as serving as a reference for clinical diagnosis and potential therapeutic targets.

Keywords: Bioinformatics; DEGs; Endoplasmic reticulum stress; Epilepsy.

Fig. 1

Flow chart of the study

Fig. 2

Screening of ERS-related DEGs in epilepsy, A Volcano plot of DEGs, blue indicates down-regulation and red indicates up-regulation; B Venn plot of ERS-associated genes versus DEGs; C Heatmap of correlation of ERS-associated DEGs in epilepsy, blue indicates negative correlation and red positive correlation

Fig. 3

Heatmap of clustering of DEGs and ERS-related DEGs in epilepsy, A Heatmap of clustering of DEGs, blue indicates down-regulation of expression, and red indicates up-regulation; B Heatmap of clustering of ERS-associated DEGs in epilepsy, blue indicates down-regulation, and red indicates up-regulation

Fig. 4

GO and KEGG analysis of ERS-related DEGs in epilepsy, A BP enrichment analysis of ERS-related DEGs in epilepsy; B CC enrichment analysis of ERS-related DEGs in epilepsy; C MF enrichment analysis of ERS-related DEGs in epilepsy; D KEGG analysis of ERS-related DEGs in epilepsy

Fig. 5

Construction of PPI network and screening of Hub genes, A PPI network of ERS-related DEGs in epilepsy; B Venn diagrams between the five algorithms; C histogram of Hub genes for the MCC algorithm; D Multiple associations between Hub genes as shown by the MMC algorithm, the darker the color of the dot, the higher the Hub gene ranking

Fig. 6

mRNA expression of Hub genes in GSE143272 between samples of epilepsy study participants and samples of healthy study participants ( * P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001)

Fig. 7

ROC curves for Hub genes in GSE143272

Fig. 8

Verification of CXCL8 mRNA expression levels by qRT-PCR (**P < 0.01; n = 50)