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find Author "HE Chengcheng" 3 results
  • Identification of differentially expressed genes in peripheral blood of patients with idiopathic epilepsy by bioinformatics analysis

    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.

    Release date:2021-01-07 02:57 Export PDF Favorites Scan
  • Construction of a prediction model and analysis of risk factors for seizures after stroke

    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.

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  • Clustering analysis of risk factors in high-incidence areas of esophageal cancer in Yanting county

    Objective To investigate the dietary patterns of rural residents in the high-incidence areas of esophageal cancer (EC), and to explore the clustering and influencing factors of risk factors associated with high-incidence characteristics. Methods A special structured questionnaire was applied to conduct a face-to-face survey on the dietary patterns of rural residents in Yanting county of Sichuan Province from July to August 2021. Univariate and multivariate logistic regression models were used to analyze the influencing factors of risk factor clustering for EC. Results There were 838 valid questionnaires in this study. A total of 90.8% of rural residents used clean water such as tap water. In the past one year, the people who ate fruits and vegetables, soybean products, onions and garlic in high frequency accounted for 69.5%, 32.8% and 74.5%, respectively; the people who ate kimchi, pickled vegetables, sauerkraut, barbecue, hot food and mildew food in low frequency accounted for 59.2%, 79.6%, 68.2%, 90.3%, 80.9% and 90.3%, respectively. The clustering of risk factors for EC was found in 73.3% of residents, and the aggregation of two risk factors was the most common mode (28.2%), among which tumor history and preserved food was the main clustering pattern (4.6%). The logistic regression model revealed that the gender, age, marital status and occupation were independent influencing factors for the risk factors clustering of EC (P<0.05). Conclusion A majority of rural residents in high-incidence areas of EC in Yanting county have good eating habits, but the clustering of some risk factors is still at a high level. Gender, age, marital status, and occupation are influencing factors of the risk factors clustering of EC.

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