Identification and analysis of exosome-associated signatures in pediatric sepsis by integrated bioinformatics analysis and machine learning.

ABSTRACT:

Background

Pediatric sepsis (PS) is a critical condition characterized by life-threatening organ dysfunction and immune dysregulation, including exosome-mediated immune modulation, often linked to infections. Investigating the role of exosome-related genes (ERGs) in the pathogenesis of PS is essential for identifying significant diagnostic and therapeutic targets.

Methods

Four datasets, namely GSE66099 (training set) and GSE13904, GSE26378, and GSE26440 (validation sets), were retrieved from the Gene Expression Omnibus (GEO). The differential expression of 56 ERGs was analyzed, followed by consensus clustering to identify distinct exosome-related patterns in PS. Weighted gene co-expression network analysis (WGCNA) was utilized to identify PS-related genes (SRGs). Additionall

SUBMITTER: Huang J 

PROVIDER: S-EPMC12790779 | biostudies-literature | 2026

REPOSITORIES: biostudies-literature