ArticleFrontiers in neurology2025
Construction of epilepsy diagnosis model based on cell senescence-related genes and its potential mechanism.
Article in Frontiers in neurology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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6 authors.
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Abstract
Introduction: 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.
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