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Characterizing Dysregulated Networks in Individual Patients with Ischemic Stroke Based on Monte Carlo
Guojian Sun1, Bin Luan1, Ruiying Zhao2
11 Department of Rehabilitation Medicine, The People's Hospital of Liaocheng , Liaocheng, People's Republic of China .
Abstract:
The purpose of this study was to introduce a new method to elucidating the molecular mechanisms in ischemic stroke. Genes from microarray data were performed enrichment to biological pathways. Dysregulated pathways and dysregulated pathway pairs were identified and constructed into networks. After Random Forest classification was performed, area under the curve (AUC) value of main network was calculated. After 50 bootstraps of Monte Carlo Cross-Validation, six pairs of pathways were found for >40 times. The best main network with AUC value = 0.735 was identified, including 14 pairs of pathways. Compared with the traditional method (gene set enrichment analysis), although a small part of pathways were shared, most of the pathways were closely related with ischemic stroke. The best network may give new insights into the underlying molecular mechanisms in ischemic stroke. It may play pivotal roles in the progression of ischemic stroke and particular attention should be focused on them for further research.
Insights
This study introduces a novel network-based method to uncover molecular mechanisms in ischemic stroke. The approach identified key pathway pairs, offering new insights into disease progression and potential therapeutic targets.
Area of Science:
- Biomedical research
- Computational biology
- Genomics
Background:
- Ischemic stroke is a leading cause of disability worldwide.
- Understanding the complex molecular mechanisms underlying ischemic stroke is crucial for developing effective treatments.
- Current methods for pathway analysis have limitations in capturing intricate molecular interactions.
Purpose of the Study:
- To introduce and validate a novel network-based computational method for elucidating molecular mechanisms in ischemic stroke.
- To identify dysregulated biological pathways and pathway pairs associated with ischemic stroke.
- To compare the efficacy of the new method with traditional gene set enrichment analysis.
Main Methods:
- Microarray data analysis to identify dysregulated genes.
- Pathway enrichment analysis to map genes to biological pathways.
- Construction of gene-gene and pathway-pathway networks.
- Random Forest classification and Monte Carlo Cross-Validation for network assessment.
- Calculation of Area Under the Curve (AUC) for network performance evaluation.
Main Results:
- A novel network construction method was successfully developed.
- Six pairs of pathways were consistently identified across multiple cross-validation iterations (>40 times).
- The best identified network achieved an AUC value of 0.735, comprising 14 pathway pairs.
- The identified pathways showed strong relevance to ischemic stroke, with some overlap but distinct findings compared to traditional methods.
Conclusions:
- The developed network-based method provides a powerful tool for uncovering molecular mechanisms in ischemic stroke.
- The identified pathway pairs represent potential key players in ischemic stroke progression.
- These findings offer new insights and warrant further investigation for therapeutic development in ischemic stroke.
