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Identification of potential therapeutic targets for stroke using data mining, network analysis, enrichment, and
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
Summary
Researchers identified key genes and pathways involved in stroke using data mining and network analysis. This study reveals potential therapeutic targets and drug candidates for developing novel stroke treatments.
Area of Science:
- Genomics
- Bioinformatics
- Computational Biology
Background:
- Stroke represents a significant global health burden, causing widespread disability and mortality.
- Identifying effective therapeutic targets is crucial for improving stroke outcomes.
Purpose of the Study:
- To identify novel therapeutic targets for stroke using a multi-analytical approach.
- To explore potential drug candidates for stroke treatment through molecular docking.
Main Methods:
- Data mining of stroke-associated genes from GeneCards and DisGeNET databases.
- Construction and analysis of a protein-protein interaction (PPI) network using STRING.
- Gene Ontology (GO) and KEGG pathway enrichment analyses using Metascape.
- Molecular docking analysis to screen for potential drug candidates.
Main Results:
- 1991 stroke-associated genes were identified.
- A PPI network with 1301 nodes and 5413 edges was constructed.
- Ten hub genes (e.g., TNF, IL6, AKT1) and five enriched KEGG pathways (e.g., TNF signaling, cytokine-cytokine receptor interaction) were identified.
- Several promising drug candidates targeting these hub genes were screened via molecular docking.
Conclusions:
- The study successfully identified key molecular players and pathways implicated in stroke pathogenesis.
- The identified hub genes and pathways offer potential targets for novel stroke therapies.
- The screened drug candidates warrant further investigation for clinical application in stroke treatment.
