Bioinformatics-based identification and validation of mitochondria-related genes associated with neonatal sepsis.

ABSTRACT:

Background

While mitochondrial involvement in sepsis has been widely studied, its role in neonatal sepsis (NESE) remains unclear. This study aimed to explore the molecular mechanisms of mitochondrial-related genes (MRGs) in NESE using bioinformatics analysis.

Methods

This study utilized neonatal sepsis-related datasets GSE69686 and GSE95233. Differentially expressed genes (DEGs) were identified by comparing NESE and control groups. Subsequently, candidate genes were then selected by intersecting DEGs with MRGs. These candidate genes were further refined using least absolute shrinkage and selection operator (LASSO) regression and the Boruta algorithm to identify potential biomarkers. Expression levels and receiver operating characteristic (ROC) curve analyses of the candidate

SUBMITTER: Zhong Y 

PROVIDER: S-EPMC12717851 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature