ObjectiveUbiquitination-mediated disruption of protein homeostasis can affect neuronal excitability and synaptic function, processes that are closely linked to the pathogenesis of epilepsy. The study aims to identify ubiquitination-related genes and explore their potential molecular mechanisms and diagnostic value. MethodsPeripheral blood transcriptome data of epilepsy patients and healthy controls were obtained from the Gene Expression Omnibus (GEO). The limma package was used to identify differentially expressed genes (DEGs). These DEGs were then intersected with a set of ubiquitination-related genes (URGs) to obtain differentially expressed ubiquitination-related genes (DE-URGs). Enrichment analyses of these genes were performed to explore their potential biological functions and underlying molecular mechanisms. The Least Absolute Shrinkage and Selection Operator (LASSO) regression and Receiver Operating Characteristic (ROC) curve analysis were used to identify key genes with diagnostic potential. A combined diagnostic index model was constructed based on the selected genes, and its cross-tissue generalizability was validated in a brain tissue transcriptome dataset. The hTFtarget database was used to predict transcription factors that may interact with DE-URGs, and a visualized regulatory network was constructed. ResultsDifferential expression analysis identified 930 DEGs, and after intersecting with URGs, 57 DE-URGs were obtained. Enrichment analysis showed significant enrichment of these genes in protein ubiquitination modification, immune inflammatory signaling pathways, and pathways related to neurodegenerative diseases. The LASSO regression identified 15 ubiquitination-related genes with diagnostic potential, and the combined diagnostic model based on these genes achieved an AUC of 0.965 in the training set. In the brain tissue samples, the combined index model achieved an AUC of 0.929 in the validation set, demonstrating good cross-tissue generalizability. A total of 125 transcription factors were predicted to potentially interact with the DE-URGs. ConclusionAbnormal expression of ubiquitination-related genes is observed in the peripheral blood of patients with epilepsy. The imbalance of ubiquitination function may play a key role in the pathogenesis of epilepsy. The combined index model based on 15 ubiquitination-related genes exhibits high diagnostic efficacy and cross-tissue stability, which provides new candidate molecules for the screening of potential biomarkers and the early diagnosis of epilepsy
ObjectiveTo systematically evaluate the risk factors associated with the incidence of status epilepticus and provide evidence-based medical evidence for early identification and prevention. MethodsSystematic searches were performed in databases including PubMed, Embase, Web of Science, Cochrane Library, CNKI, WanFang, VIP, and CBM for studies investigating risk factors for status epilepticus. The search covered the period from database inception to December 2025. Two researchers independently performed literature screening, data extraction, and quality assessment. Meta-analysis was performed using RevMan 5.4 and Stata 18.0 software. ResultsA total of 10 articles were ultimately included (8 case-control studies and 2 cohort studies). The meta-analysis results indicated that a history of epilepsy [OR= 7.64, 95%CI (3.44, 17.00), P<0.000 01], poor medication adherence [OR=3.74, 95%CI(2.52,5.56), P<0.000 01], cerebrovascular disease [OR=6.01, 95%CI(3.60, 10.03), P<0.000 01], central nervous system infection [OR=4.60, 95%CI(2.58, 8.19), P<0.000 01], craniocerebral injury [OR=2.57, 95%CI(1.19, 5.55), P=0.02], abnormal background electroencephalogram activity [OR=2.98, 95%CI(1.90, 4.66), P<0.000 01], and neuroimaging (MRI) abnormalities [OR=1.98, 95%CI(1.39,2.83), P=0.000 2] were potential risk factors for status epilepticus. ConclusionHistory of epilepsy, poor medication adherence, cerebrovascular disease, central nervous system infection, craniocerebral injury, abnormal background electroencephalogram activity, and abnormal neuroimaging findings may be risk factors for the onset of status epilepticus. Therefore, early identification of and intervention based on these risk factors are crucial for preventing status epilepticus.