Unraveling the molecular landscape and therapeutic strategies for acute kidney injury: insights from transcriptomics, network pharmacology, virtual screening, and in vitro experiments.

Acute kidney injury (AKI) is a clinical syndrome characterized by a rapid decline in renal function, high morbidity and mortality, and a lack of effective early diagnostic markers or targeted therapies. To address this critical unmet need, we employed an integrated multi-omics and network pharmacology approach to systematically investigate the molecular mechanisms and potential therapeutic targets of AKI. Core targets were identified through differential gene expression (DEG) analysis combined with weighted gene co-expression network analysis (WGCNA), followed by exploration using protein-protein interaction (PPI) networks and pathway enrichment analyses. Inflammation, oxidative stress, and energy metabolism emerged as key pathways involved in AKI pathogenesis. Using ten CytoHubba algorithms and the MCODE module for comprehensive screening, we identified three hub genes-ACO2, FBP1, and PFKL. Their expression patterns and cellular specificity were further characterized using single-cell RNA sequencing data from AKI renal tissues. Additionally, we constructed a miRNA-hub gene regulatory network, providing insights into miRNA-based therapeutic strategies. Molecular docking analysis identified three approved drugs-Ajmaline, Cimetidine, and Tretinoin-with strong binding affinities to the hub proteins, suggesting their potential for repurposing in AKI treatment. Finally, by reviewing knockout mouse models from the Mouse Genome Informatics (MGI) database and conducting in vitro cell experiments, we explored the in vivo and in vitro roles of these core targets, providing experimental evidence of their physiological relevance. Overall, this study integrates cross-cohort transcriptomic profiling, network-based hub prioritization, single-nucleus cell-type localization, translational drug repurposing analyses, and in vitro experimental validation thereby providing a multi-layered framework to prioritize candidate biomarkers for AKI.