Construction of epilepsy diagnosis model based on cell senescence-related genes and its potential mechanism

Xiangyao Gong; Wei Lu; Qihua Xiao; Xiaopeng Wang; Chenchen Cui; Hai Tang

doi:10.3389/fneur.2025.1555586

Construction of epilepsy diagnosis model based on cell senescence-related genes and its potential mechanism

Front Neurol. 2025 May 30:16:1555586. doi: 10.3389/fneur.2025.1555586. eCollection 2025.

Authors

Xiangyao Gong^#^{1

2}, Wei Lu^#³, Qihua Xiao^{2

4}, Xiaopeng Wang^{1

2}, Chenchen Cui^{2

4}, Hai Tang^{1

2}

Affiliations

¹ Department of Neurology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, China.
² Epilepsy Center, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, China.
³ Department of General Practice, The Affiliated Suqian Hospital of Xuzhou Medical University, Suqian, China.
⁴ Department of Neurosurgery, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, China.

^# Contributed equally.

Abstract

Introduction: Epilepsy is a chronic brain disease with a certain degree of neurodegeneration and is caused by abnormal discharges of neurons. The mechanism of cell senescence has garnered increasing attention in neurodegenerative diseases. However, the role of cell senescence in the onset and progression of epilepsy is unclear. Therefore, this study constructed a diagnostic model of epilepsy based on cellular senescence-related genes (CSRGs) to analyze their role in disease pathogenesis.

Methods: The differentially expressed genes (DEGs) were screened from the epileptic sample dataset of the gene expression omnibus (GEO) database, and the cellular senescence-related DEGs (CSRDEGs) related to epilepsy were identified by CSRGs crossover. The functional enrichment characteristics of CSRDEGs were analyzed using gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) enrichment analyses. The differences in biological processes between high and low-risk groups were analyzed using gene set enrichment analysis (GSEA). For model construction, logistic regression, random forest, and least absolute shrinkage and selection operator (LASSO) regression were employed to identify key genes, including ribosomal protein S6 kinase alpha-3 (RPS6KA3), cathepsin D (CTSD), and zinc finger protein 101 (ZNF101). Subsequently, a multifactor logistic regression model was developed to evaluate the risk of epilepsy based on these screened genes.

Results: The model exhibited higher area under the curve (AUC) values in the GSE data sets 143272 and 32534, producing encouraging results. Finally, mRNA-miRNA and mRNA-transcription factors (TFs) networks revealed the potential regulatory mechanism of the selected critical genes in the disease.

Discussion: This study elucidated the possible process of cell senescence in epileptic diseases through bioinformatics analysis, offering a potential target for personalized diagnosis and precise treatment.

Keywords: bioinformatics analysis; cell senescence; diagnosis model; epilepsy; senescence-associated secretory phenotype.