Identification of potential therapeutic targets for stroke using data mining, network analysis, enrichment, and docking analysis

IF 2.6 4区生物学 Q2 BIOLOGY

Computational Biology and Chemistry Pub Date : 2025-03-20 DOI:10.1016/j.compbiolchem.2025.108431

Mahdi Hatamipour , Hossein Saremi , Prashant Kesharwani , Amirhossein Sahebkar

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引用次数: 0

Abstract

Stroke is a leading cause of disability and death worldwide. In this study, we identified potential therapeutic targets for stroke using a data mining, network analysis, enrichment, and docking analysis approach. We first identified 1991 genes associated with stroke from two publicly available databases: GeneCards and DisGeNET. We then constructed a protein-protein interaction (PPI) network using the STRING database and identified 1301 nodes and 5413 edges. We used Metascape to perform GO enrichment analysis and KEGG pathway enrichment analysis. The results of these analyses identified ten hub genes (TNF, IL6, ACTB, AKT1, IL1B, TP53, VEGFA, STAT3, CASP3, and CTNNB1) and five KEGG pathways (cancer, lipid and atherosclerosis, cytokine–cytokine receptor interaction, AGE RAGE signaling pathway in complications, and TNF signaling pathway) that are enriched in stroke genes. We then performed molecular docking analysis to screen potential drug candidates for these targets. The results of this analysis identified several promising drug candidates that could be used to develop new therapeutic strategies for stroke.

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来源期刊

Computational Biology and Chemistry 生物-计算机：跨学科应用

CiteScore

6.10

自引率

3.20%

发文量

142

审稿时长

24 days

期刊介绍： Computational Biology and Chemistry publishes original research papers and review articles in all areas of computational life sciences. High quality research contributions with a major computational component in the areas of nucleic acid and protein sequence research, molecular evolution, molecular genetics (functional genomics and proteomics), theory and practice of either biology-specific or chemical-biology-specific modeling, and structural biology of nucleic acids and proteins are particularly welcome. Exceptionally high quality research work in bioinformatics, systems biology, ecology, computational pharmacology, metabolism, biomedical engineering, epidemiology, and statistical genetics will also be considered. Given their inherent uncertainty, protein modeling and molecular docking studies should be thoroughly validated. In the absence of experimental results for validation, the use of molecular dynamics simulations along with detailed free energy calculations, for example, should be used as complementary techniques to support the major conclusions. Submissions of premature modeling exercises without additional biological insights will not be considered. Review articles will generally be commissioned by the editors and should not be submitted to the journal without explicit invitation. However prospective authors are welcome to send a brief (one to three pages) synopsis, which will be evaluated by the editors.