ArticleScientific reports2025
Machine learning analysis of gene expression profiles of pyroptosis-related differentially expressed genes in ischemic stroke revealed potential targets for drug repurposing.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- How to recognize if a cell dies from pyroptosis?Molecular biology reports · 2026Review
- A preliminary analysis of the inflammatory protein landscape in the CSF of mid- to late-stage Parkinson's disease: associations with motor severity and subtypes.BMC neurology · 2026Article
- Article
- Cerebral Ischemia-Reperfusion Injury: Unraveling the Mitophagy-Oxidative Stress Axis for Neuroprotective Strategies.International journal of molecular sciences · 2026Review
- Exploring Endoplasmic Reticulum Stress-Related Genes in Cartilage Defects: Implications for Diagnosis and Therapy.Combinatorial chemistry & high throughput screening · 2026Article
- Identification and Validation of Glycosylation‑Related Genes in Ischemic Stroke Based on Bioinformatics and Machine Learning.Journal of molecular neuroscience : MN · 2025Article
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Authors and funding
10 authors.
Funding
Abstract
The relationship between ischemic stroke (IS) and pyroptosis centers on the inflammatory response elicited by cerebral tissue damage during an ischemic stroke event. However, an in-depth mechanistic understanding of their connection remains limited. This study aims to comprehensively analyze the gene expression patterns of pyroptosis-related differentially expressed genes (PRDEGs) by employing integrated IS datasets and machine learning techniques. The primary objective was to develop classification models to identify crucial PRDEGs integral to the ischemic stroke process. Leveraging three distinct machine learning algorithms (LASSO, Random Forest, and Support Vector Machine), models were developed to differentiate between the Control and the IS patient samples. Through this approach, a core set of 10 PRDEGs consistently emerged as significant across all three machine learning models. Subsequent analysis of these genes yielded significant insights into their functional relevance and potential therapeutic approaches. In conclusion, this investigation underscores the pivotal role of pyroptosis pathways in ischemic stroke and identifies pertinent targets for therapeutic development and drug repurposing.
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Registered trials
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