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通过数据挖掘,网络分析,丰富和对接分析来确定中风的潜在治疗目标

Mahdi Hatamipour¹, Hossein Saremi¹, Prashant Kesharwani²

¹Department of Medicinal Chemistry, School of Pharmacy, Mashhad University of Medical Sciences, Mashhad, Iran.

Computational biology and chemistry

|March 24, 2025

概括

研究人员通过数据挖掘和网络分析确定了与中风有关的关键基因和途径. 这项研究揭示了开发新型中风治疗方法的潜在治疗点和候选药物.

科学领域:

基因组学就是基因组学.
生物信息学是一种生物信息学.
计算生物学计算生物学

背景情况:

脑卒中对全球健康造成重大负担,导致广泛的残疾和死亡率.
确定有效的治疗目标对于改善中风结果至关重要.

研究的目的:

用多分析方法确定中风的新型治疗点.
通过分子对接来探索潜在的中风治疗候选药物.

主要方法:

从GeneCards和DisGeNET数据库中挖掘与中风相关的基因的数据.
使用STRING. 构建和分析蛋白质与蛋白质相互作用 (PPI) 网络.
用Metascape进行基因本体学 (GO) 和KEGG通路丰富分析.
分子对接分析以选潜在的候选药物.

主要成果:

1991年,与中风相关的基因被发现.
构建了一个PPI网络,拥有1301个节点和5413个边缘.
确定了10个枢纽基因 (例如TNF,IL6,AKT1) 和5个丰富的KEGG通路 (例如TNF信号传递,细胞因子-细胞因子受体相互作用).
针对这些枢纽基因的几种有前途的候选药物通过分子对接进行了选.

结论:

这项研究成功地确定了关键的分子参与者和涉及中风病变的途径.
已识别的枢纽基因和途径为新的中风疗法提供了潜在的目标.
选的候选药物需要进一步研究,以便在中风治疗中临床应用.

关键词:

通过KEGG路径.一次性中风,中风.数据挖掘是数据挖掘的一个方法.发现药物的发现.分子对接的分子对接.网络药理学网络药理学蛋白质与蛋白质的相互作用

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