Screening for biomarkers of tuberous sclerosis complex-associated epilepsy: a bioinformatics analysis.

IF 1.5 4区 医学 Q2 PEDIATRICS Translational pediatrics Pub Date : 2024-07-31 Epub Date: 2024-07-29 DOI:10.21037/tp-24-211
Yongsheng Sun, Haonan Ji, Liqin Xu, Ruiyin Gu, Pasquale Striano, Gavin P Winston, Bin Li, Hui Zhou
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Abstract

Background: The optimal biomarkers for early diagnosis, treatment, and prognosis of tuberous sclerosis complex (TSC)-associated epilepsy are not yet clear. This study identifies the crucial genes involved in the pathophysiology of TSC-associated epilepsy via a bioinformatics analysis. These genes may serve as novel therapeutic targets.

Methods: Gene chip data sets (GSE62019 and GSE16969) comprising the data of patients with TSC-associated epilepsy and healthy control participants were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) in the GEO database were identified using the GEO2R gene expression analysis tool. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, Gene Ontology function, and protein-protein interaction (PPI) network analyses were then conducted. The results were analyzed using R language, and are presented in volcano plots, Venn diagrams, heatmaps, and enrichment pathway bubble charts. A gene set enrichment analysis (GSEA), was conducted to examine the KEGG pathways and crucial genes linked to TSC-associated epilepsy. The potential genes were compared with the genes listed in the Online Mendelian Inheritance in Man (OMIM) database and analyzed against the literature to determine their clinical significance. Finally, the expression of the key genes in the TSC-associated epilepsy mice cerebral cortex was examined through immunohistochemical staining.

Results: The intersection of the GSE62019 and GSE16969 data sets revealed 151 commonly upregulated DEGs. The KEGG enrichment analysis indicated that these DEGs affected the occurrence and development of TSC-associated epilepsy by modulating complement and coagulation cascades, glycosaminoglycans in cancer, and extracellular matrix-receptor interactions. Four high-scoring clusters emerged, and podoplanin (PDPN) was identified as a key gene through the construction of a PPI network of the common DEGs using the Cytoscape software. A GSEA of the DEGs revealed that the common DEG PDPN was enriched in both data sets in pathways related to platelet activation, aggregation, and the glycoprotein VI (GPVI)-mediated activation cascade. Immunohistochemical staining revealed a significant elevation in PDPN expression in the cerebral cortex of mice with TSC-associated epilepsy. Conversely, the control group mice did not display any significantly positive neurons.

Conclusions: The discovery of these crucial genes and signaling pathways extends understanding of the molecular processes underlying the development of TSC-associated epilepsy. Additionally, our findings may provide a theoretical basis for research into targeted clinical treatments.

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筛选结节性硬化症复合体相关癫痫的生物标记物:生物信息学分析。
背景:结节性硬化综合征(TSC)相关癫痫的早期诊断、治疗和预后的最佳生物标志物尚不明确。本研究通过生物信息学分析确定了参与TSC相关癫痫病理生理学的关键基因。这些基因可作为新的治疗靶点:方法:从基因表达总库(GEO)数据库中获取基因芯片数据集(GSE62019和GSE16969),包括TSC相关癫痫患者和健康对照参与者的数据。使用 GEO2R 基因表达分析工具鉴定了 GEO 数据库中的差异表达基因(DEGs)。然后进行了京都基因组百科全书(KEGG)通路、基因本体功能和蛋白质相互作用(PPI)网络分析。结果使用 R 语言进行分析,并以火山图、维恩图、热图和富集通路气泡图的形式呈现。通过基因组富集分析(GSEA),研究了与 TSC 相关癫痫有关的 KEGG 通路和关键基因。将潜在基因与在线人类孟德尔遗传数据库(OMIM)中列出的基因进行比较,并对照文献进行分析,以确定其临床意义。最后,通过免疫组化染色检测了关键基因在TSC相关癫痫小鼠大脑皮层中的表达情况:结果:GSE62019 和 GSE16969 数据集的交叉发现了 151 个常见的上调 DEGs。KEGG富集分析表明,这些DEGs通过调节补体和凝血级联、癌症中的糖胺聚糖以及细胞外基质与受体的相互作用,影响了TSC相关癫痫的发生和发展。通过使用Cytoscape软件构建常见DEGs的PPI网络,发现了四个高分簇,并将podoplanin(PDPN)确定为关键基因。DEGs的GSEA显示,在两个数据集中,共同的DEG PDPN都富集在与血小板活化、聚集和糖蛋白VI(GPVI)介导的活化级联有关的通路中。免疫组化染色显示,TSC 相关性癫痫小鼠大脑皮层中的 PDPN 表达明显升高。相反,对照组小鼠的神经元没有出现明显的阳性表达:结论:这些关键基因和信号通路的发现扩展了人们对TSC相关癫痫发病的分子过程的认识。此外,我们的发现还为临床靶向治疗研究提供了理论依据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Translational pediatrics
Translational pediatrics Medicine-Pediatrics, Perinatology and Child Health
CiteScore
4.50
自引率
5.00%
发文量
108
期刊介绍: Information not localized
期刊最新文献
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