ObjectiveTo investigate key differentially expressed genes (DEGs) in peripheral blood of idiopathic epilepsy patients, as well as their biological functions, cellular localization, involved signaling pathways, through bioinformatics analysis. So to provide new insights for the pathogenesis and prevention of idiopathic epilepsy.MethodsFirstly, we screened and downloaded microarray data including 6 peripheral blood samples of drug-naive patients with idiopathic epilepsy, 8 peripheral blood samples of responders of idiopathic epilepsy treated with Valproate (VPA), and 10 peripheral blood samples of non-responders of idiopathic epilepsy treated with VPA from Gene Expression Omnibus (GEO) data series GSE143272, which Public in January 2020. Secondly, we identified DEGs via the limma package and others in R software. Then we had gotten 74 DEGs, and subsequently conducted gene ontology and pathway enrichment analysis, PPI network analysis and hub gene analysis, using multiple methods containing DAVID, STRING, and Cytohubba in Cytoscape.ResultsWe had identified significant hub DEGs, including TREML3P, KCNJ15, ORM1, RNA28S5, ELANE, RETN, ARG1, LCN2, SLPI, HP, PGLYRP1, BPI, DEFA4, TCN1, MPO, MMP9, CTSG, CXCL8, RNASE3, RNASE2, S100A12, DEFA1B, DEFA1, DEFA3, CEACAM8, MS4A3, PTGS2, PI3, CCL3. The biological processes involved in these DEGs include immune response, inflammatory response, chemotaxis, etc. While, the molecular function is focused on peroxidase activity, chemokine activity, etc. Moreover, KEGG pathway enrichment analysis shows that DEGs were mainly involved in cytokine-cytokine receptor interaction, Toll-like receptor signaling pathway, chemokine signaling pathway and so on.ConclusionThese important key DEGs may be involved in the onset and development of idiopathic epilepsy through a variety of signaling pathways and complex mechanisms.
ObjectiveConstructing a prediction model for seizures after stroke, and exploring the risk factors that lead to seizures after stroke. MethodsA retrospective analysis was conducted on 1 741 patients with stroke admitted to People's Hospital of Zhongjiang from July 2020 to September 2022 who met the inclusion and exclusion criteria. These patients were followed up for one year after the occurrence of stroke to observe whether they experienced seizures. Patient data such as gender, age, diagnosis, National Institute of Health Stroke Scale (NIHSS) score, Activity of daily living (ADL) score, laboratory tests, and imaging examination data were recorded. Taking the occurrence of seizures as the outcome, an analysis was conducted on the above data. The Least absolute shrinkage and selection operator (LASSO) regression analysis was used to screen predictive variables, and multivariate Logistic regression analysis was performed. Subsequently, the data were randomly divided into a training set and a validation set in a 7:3 ratio. Construct prediction model, calculate the C-index, draw nomogram, calibration plot, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA) to evaluate the model's performance and clinical application value. ResultsThrough LASSO regression, nine non-zero coefficient predictive variables were identified: NIHSS score, homocysteine (Hcy), aspartate aminotransferase (AST), platelet count, hyperuricemia, hyponatremia, frontal lobe lesions, temporal lobe lesions, and pons lesions. Multivariate logistic regression analysis revealed that NIHSS score, Hcy, hyperuricemia, hyponatremia, and pons lesions were positively correlated with seizures after stroke, while AST and platelet count were negatively correlated with seizures after stroke. A nomogram for predicting seizures after stroke was established. The C-index of the training set and validation set were 0.854 [95%CI (0.841, 0.947)] and 0.838 [95%CI (0.800, 0.988)], respectively. The areas under the ROC curves were 0.842 [95%CI (0.777, 0.899)] and 0.829 [95%CI (0.694, 0.936)] respectively. Conclusion These nine variables can be used to predict seizures after stroke, and they provide new insights into its risk factors.