ArticleBMC nephrology2025
Novel insights into the molecular mechanisms of sepsis-associated acute kidney injury: an integrative study of GBP2, PSMB8, PSMB9 genes and immune microenvironment characteristics.
Article in BMC nephrology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Biomarker-Based Diagnosis and Risk Stratification in Sepsis-Associated Acute Kidney Injury: From Molecular Mechanisms to Multimarker Panels.Diagnostics (Basel, Switzerland) · 2026Review
- Identification of common diagnostic biomarkers and immune landscapes in sepsis and acute kidney injury: a transcriptomic study integrating machine learning and single-cell analysis.Frontiers in genetics · 2026Article
- Diagnostic Biomarkers and Targeted Drug Prediction for Acute Kidney Injury: A Computational ApproachEndocrine, metabolic & immune disorders drug targets · 2026Article
- Targeting the renin-angiotensin system in sepsis-associated AKI: from pathophysiology to precision medicine.Frontiers in immunology · 2026Review
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Authors and funding
6 authors.
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Abstract
backgroundSepsis-associated acute kidney injury (SA-AKI) is a prevalent and severe complication of sepsis, but its complex pathogenesis remains unclear. This study aims to identify potential biomarkers for SA-AKI by elucidating its molecular mechanisms through bioinformatics methods.
methodsTranscriptional data related to SA-AKI were obtained from the Gene Expression Omnibus (GEO) database. We used differentially expressed genes (DEGs) and weighted gene co-expression network analysis (WGCNA) to identify characteristic genes associated with SA-AKI and conducted enrichment analyses. Hub genes were determined using protein-protein interaction (PPI) network analysis and the Least Absolute Shrinkage and Selection Operator (LASSO). Additionally, ROC curves were plotted to assess the diagnostic value of these core genes. Immune cell infiltration was analyzed using the CIBERSORT algorithm, and potential associations between the hub genes and clinicopathological features were explored based on the Nephroseq database. Finally, a murine model of SA-AKI was induced with lipopolysaccharide (LPS) to validate the findings, and mRNA abundance and protein production levels of pivotal genes were confirmed via RT-qPCR, Western blotting, and immunohistochemical methods.
resultsWe identified 268 characteristic genes associated with SA-AKI that are enriched in immune and inflammation-related pathways. Utilizing machine learning techniques, three key genes were screened: GBP2, PSMB8 and PSMB9. The expression patterns of these three genes were well-validated through animal experiments and databases. Correlation between these genes and clinical indicators was confirmed using the Nephroseq database. Furthermore, immune infiltration analysis provided additional insights into their potential functions.
conclusionGBP2, PSMB8, and PSMB9 are promising candidate genes for SA-AKI, providing a novel perspective on its pathological mechanisms. Further exploration of the biological roles of these genes in the pathogenesis of SA-AKI is needed in the future.
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