Exploration of autophagy-related molecular mechanisms underlying epilepsy using multiple datasets
Objective To elucidate the molecular mechanisms underlying epilepsy, we investigated autophagy-related differentially expressed genes in epilepsy patients. Methods We analyzed GSE143272 and GSE4290 microarray datasets from the NCBI Gene Expression Omnibus database, which is established based on eval...
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| Format: | Article |
| Language: | English |
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SAGE Publishing
2025-08-01
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| Series: | Journal of International Medical Research |
| Online Access: | https://doi.org/10.1177/03000605251364784 |
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| author | Yongfei Wang Haoxuan Zeng Chongxu Liu Jianjun Chen Yihong Huang Xianju Zhou |
| author_facet | Yongfei Wang Haoxuan Zeng Chongxu Liu Jianjun Chen Yihong Huang Xianju Zhou |
| author_sort | Yongfei Wang |
| collection | DOAJ |
| description | Objective To elucidate the molecular mechanisms underlying epilepsy, we investigated autophagy-related differentially expressed genes in epilepsy patients. Methods We analyzed GSE143272 and GSE4290 microarray datasets from the NCBI Gene Expression Omnibus database, which is established based on evaluations of peripheral blood samples. Using a bioinformatics approach, autophagy-related differentially expressed genes between epilepsy patients and healthy controls were identified. Further analyses including Least Absolute Shrinkage and Selection Operator regression, immune cell infiltration, and pathway enrichment were conducted. Experimental validation was performed using quantitative reverse transcription–polymerase chain reaction in a mouse epileptic model. Additionally, the Connectivity Map database was employed to predict potential drugs. Results In total, 49 autophagy-related differentially expressed genes were identified. A Least Absolute Shrinkage and Selection Operator logistic model revealed four autophagy-related differentially expressed genes, namely, CAPN2 , ERN1 , RELA , and SAR1A . Furthermore, a novel diagnostic model with robust validation metrics was established. Immune cell infiltration analysis underscored the significance of immune response in epilepsy, revealing distinct profiles in patients. Additionally, pathway enrichment analysis using gene set enrichment analysis and gene set variation analysis revealed that critical genes were implicated in diverse pathways, including metabolic and neurodegenerative diseases. The expression levels of these key genes were experimentally corroborated using quantitative reverse transcription–polymerase chain reaction in the hippocampus tissues of status epileptic mice. Finally, Connectivity Map analysis suggested three antiseizure drugs (cabergoline, capsazepine, and zolantidine). Conclusions Our results provide insights into potential biomarker candidates, thus contributing to clinical diagnosis and the development of new antiseizure drugs. |
| format | Article |
| id | doaj-art-90b77d7f390b4e6da2cdbd68f9e3eaa1 |
| institution | DOAJ |
| issn | 1473-2300 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Journal of International Medical Research |
| spelling | doaj-art-90b77d7f390b4e6da2cdbd68f9e3eaa12025-08-20T03:06:13ZengSAGE PublishingJournal of International Medical Research1473-23002025-08-015310.1177/03000605251364784Exploration of autophagy-related molecular mechanisms underlying epilepsy using multiple datasetsYongfei WangHaoxuan ZengChongxu LiuJianjun ChenYihong HuangXianju ZhouObjective To elucidate the molecular mechanisms underlying epilepsy, we investigated autophagy-related differentially expressed genes in epilepsy patients. Methods We analyzed GSE143272 and GSE4290 microarray datasets from the NCBI Gene Expression Omnibus database, which is established based on evaluations of peripheral blood samples. Using a bioinformatics approach, autophagy-related differentially expressed genes between epilepsy patients and healthy controls were identified. Further analyses including Least Absolute Shrinkage and Selection Operator regression, immune cell infiltration, and pathway enrichment were conducted. Experimental validation was performed using quantitative reverse transcription–polymerase chain reaction in a mouse epileptic model. Additionally, the Connectivity Map database was employed to predict potential drugs. Results In total, 49 autophagy-related differentially expressed genes were identified. A Least Absolute Shrinkage and Selection Operator logistic model revealed four autophagy-related differentially expressed genes, namely, CAPN2 , ERN1 , RELA , and SAR1A . Furthermore, a novel diagnostic model with robust validation metrics was established. Immune cell infiltration analysis underscored the significance of immune response in epilepsy, revealing distinct profiles in patients. Additionally, pathway enrichment analysis using gene set enrichment analysis and gene set variation analysis revealed that critical genes were implicated in diverse pathways, including metabolic and neurodegenerative diseases. The expression levels of these key genes were experimentally corroborated using quantitative reverse transcription–polymerase chain reaction in the hippocampus tissues of status epileptic mice. Finally, Connectivity Map analysis suggested three antiseizure drugs (cabergoline, capsazepine, and zolantidine). Conclusions Our results provide insights into potential biomarker candidates, thus contributing to clinical diagnosis and the development of new antiseizure drugs.https://doi.org/10.1177/03000605251364784 |
| spellingShingle | Yongfei Wang Haoxuan Zeng Chongxu Liu Jianjun Chen Yihong Huang Xianju Zhou Exploration of autophagy-related molecular mechanisms underlying epilepsy using multiple datasets Journal of International Medical Research |
| title | Exploration of autophagy-related molecular mechanisms underlying epilepsy using multiple datasets |
| title_full | Exploration of autophagy-related molecular mechanisms underlying epilepsy using multiple datasets |
| title_fullStr | Exploration of autophagy-related molecular mechanisms underlying epilepsy using multiple datasets |
| title_full_unstemmed | Exploration of autophagy-related molecular mechanisms underlying epilepsy using multiple datasets |
| title_short | Exploration of autophagy-related molecular mechanisms underlying epilepsy using multiple datasets |
| title_sort | exploration of autophagy related molecular mechanisms underlying epilepsy using multiple datasets |
| url | https://doi.org/10.1177/03000605251364784 |
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