Development and validation of a machine-learning-based model for identification of genes associated with sepsis-associated acute kidney injury.

Sepsis frequently induces acute kidney injury (AKI), and the complex interplay between these two conditions worsens prognosis, prolongs hospitalization, and increases mortality. Despite therapeutic options such as antibiotics and supportive care, early diagnosis and treatment remain a challenge. Understanding the underlying molecular mechanisms linking sepsis and AKI is critical for the development of effective diagnostic tools and therapeutic strategies. We used two sepsis (GSE57065 and GSE28750) and three AKI (GSE30718, GSE139061, and GSE67401) datasets from the NCBI Gene Expression Omnibus (GEO) for model development and validation, and performed batch effect mitigation, differential gene, and functional enrichment analysis using R software packages. We assessed 113 combinations of 12 different algorithms to develop an internally and externally validated machine-learning model for diagnosing AKI. Finally, we used functional enrichment analysis to identify potential therapeutic agents for AKI. We identified 556 and 725 DEGs associated with sepsis and AKI, respectively, with 28 overlapping genes suggesting shared pathways. Functional enrichment analysis revealed important associations of AKI with immune responses and cell adhesion processes. The immune infiltration analysis showed significant differences in immune cell presence between sepsis and AKI patients compared with the control group. The machine-learning models identified eight key genes (NR3C2, PLEKHO1, CEACAM1, CDC25B, HEPACAM2, VNN1, SLC2A3, RPL36) with potential for diagnosing AKI. The diagnostic performance of the model constructed in this way was excellent (area under the curve = 0.978), especially in the under 60 years and male patient subgroups. The diagnostic performance outperformed previous models in both the training and validation sets. In addition, cyclosporin A and nine other drugs were identified as potential agents for treating sepsis-associated AKI. This study highlights the potential of integrating bioinformatics and machine-learning approaches to generate a new diagnostic model for sepsis-associated AKI using molecular crossovers with sepsis. The genes identified have potential to serve as biomarkers and therapeutic targets, providing avenues for future research aimed at enhancing sepsis-associated AKI diagnosis and treatment.