Identification of diagnostic biomarkers and mitochondrial metabolic characteristics in sepsis-associated acute kidney injury.

ABSTRACT:

Background

This study intended to explore the molecular mechanisms and the mitochondrial metabolic characteristics of sepsis-associated acute kidney injury (S-AKI) through bioinformatics analysis and experimental validation.

Methods

The datasets of S-AKI were acquired from the GEO database while mitochondrial related genes (MRGs) were procured utilizing MitoCarta3.0 database. The "limma" R package was used to screen the differentially expressed genes (DEGs). Weighted Gene Correlation Network Analysis identified the co-expressed gene modules. GO, together with KEGG, was applied for enrichment analysis. A PPI network was constructed using the STRING database. The LASSO algorithm was adopted to screen prognostic predictors of S-AKI. The correlation between immune cells and diag

SUBMITTER: Xia Y 

PROVIDER: S-EPMC12535092 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature