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

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 biomarkers were assessed in both datasets. To further investigate their mechanisms, functional enrichment, immune infiltration, and drug prediction analyses were conducted. Finally, biomarker expression was validated using reverse transcription-quantitative polymerase chain reaction (RT-qPCR). Results: A total of 24 candidate genes were obtained by overlapping 579 DEGs and 1,136 MRGs. Conclusions: In this study, six mitochondria-related biomarkers in NESE were identified and preliminarily validated, which may provide novel insights into disease mechanisms and serve as a potential basis for future diagnostic and therapeutic exploration.