Identification and Verification of Hub Mitochondrial Dysfunction Genes in Epilepsy Based on Bioinformatics Analysis

Background Epilepsy is a neurological disorder characterized by recurrent seizures, remaining a significant challenge in terms of understanding its underlying molecular mechanisms. The objective of this study was to investigate the role of mitochondrial energy metabolism-related differentially expressed genes (MRDEGs) in epilepsy, and to construct and validate a diagnostic model based on these genes. Methods Datasets were obtained from the Gene Expression Omnibus (GEO) database. Differential expression analysis was conducted to identify MRDEGs. Diagnostic models were developed using logistic regression, support vector machine (SVM), and random forest (RF) algorithms. LASSO regression was employed to mitigate overfitting. The diagnostic value of the models was assessed using Receiver Operating Characteristic (ROC) curves. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed on hub genes. Protein-protein interaction (PPI) networks were constructed and visualized using Cytoscape software. Additionally, mRNA-miRNA and mRNA-transcription factor (TF) interaction networks were established. Results In dataset GSE143272, logistic regression analysis highlighted 26 statistically significant MRDEGs. The SVM model achieved the highest accuracy with 22 MRDEGs. The RF algorithm identified 11 important MRDEGs based on IncNodePurity > 0.80. LASSO regression yielded a diagnostic model comprising five hub genes: ACAA1 , ALDH3B1 , DLST , GCDH , and NDUFB9 . ROC curves demonstrated high accuracy for DLST (AUC > 0.9). GO and KEGG analyses revealed significant enrichment in processes such as mitochondrial ATP synthesis coupled electron transport. PPI networks illustrated the interactions between hub genes. Conclusions In conclusion, our research elucidates the critical role of MRDEGs in the pathogenesis of epilepsy and develops a robust diagnostic model with potential clinical applications.