ArticleEuropean journal of medical research2025
Identification of diagnostic biomarkers and mitochondrial metabolic characteristics in sepsis-associated acute kidney injury.
Article in European journal of medical research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Identification of immune cell mitochondrial dysfunction characteristics and clinical predictive biomarkers in sepsis via multi-cohort machine learning and single-cell RNA sequencing.Inflammation research : official journal of the European Histamine Research Society ... [et al.] · 2026Article
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5 authors.
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Abstract
backgroundThis 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.
methodsThe 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 diagnostic biomarkers was reflected through immune cell infiltration analysis utilizing CIBERSORT. In cellular experiments, the CCK-8 assay detected the cell viability. RT-qPCR and western blot assessed PMPCA and PMPCB expressions. The release of inflammatory cytokines and oxidative stress markers was assessed using ELISA and corresponding assay kits. Western blot assessed the expressions of proteins implicated in mitochondrial function.
results163 intersected genes between DEGs and MRGs were screened. 103 key genes were acquired via the intersection of module genes with the 163 MRDEGs and 10 hub genes were determined. Functional enrichment analysis disclosed that the key genes were primarily enriched in mitochondrial metabolic pathways. Five significant immune cells showing differences between S-AKI and controls were identified. Correlation analysis displayed a negative association of Gpx4 with resting NK cells, its positive association with M2 macrophages as well as a negative association of Amacr with Th17 Cells. Three independent diagnostic biomarkers Gpx4, PMPCB and Amacr for S-AKI were determined. The validation cellular experiments showed that PMPCB overexpression could alleviate LPS-induced oxidative stress, inflammation and viability damage in HK-2 cells.
conclusionsThis work determined three independent diagnostic biomarkers in S-AKI, which might shed novel insight into its diagnosis and treatment.
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