Identification and analysis of exosome-associated signatures in pediatric sepsis by integrated bioinformatics analysis and machine learning
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View abstract on PubMed
Summary
This summary is machine-generated.Pediatric sepsis (PS) involves immune dysregulation and exosome-related genes (ERGs). This study identified key ERGs and developed accurate machine learning models for early PS detection and prognosis.
Area Of Science
- Biomedical research
- Genomics
- Immunology
Background
- Pediatric sepsis (PS) is a life-threatening condition with immune dysregulation, involving exosome-mediated immune modulation.
- Understanding exosome-related genes (ERGs) is crucial for identifying diagnostic and therapeutic targets in PS.
Purpose Of The Study
- To investigate the role of ERGs in the pathogenesis of pediatric sepsis.
- To identify novel diagnostic and therapeutic targets for PS.
Main Methods
- Differential expression analysis of 56 ERGs across four GEO datasets (GSE66099, GSE13904, GSE26378, GSE26440).
- Consensus clustering to identify PS subtypes based on ERG expression patterns.
- Weighted gene co-expression network analysis (WGCNA) to identify PS-related genes (SRGs) and construct machine learning diagnostic models.
Main Results
- Identified 21 significantly altered ERGs in PS, revealing two distinct PS subtypes.
- WGCNA highlighted hub genes in exosome function and PS, with enriched immune pathways (phagocytosis, NF-κB signaling).
- Machine learning models achieved high diagnostic accuracy (AUC > 0.995), identifying CD177, GYG1, IRAK3, MCEMP1, and TLR5 as key biomarkers, validated externally.
Conclusions
- Elucidated the critical role of ERGs and immune dysregulation in pediatric sepsis pathogenesis.
- Developed highly accurate diagnostic models for early detection and prognosis of PS, offering promising clinical tools.
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