Machine learning-based transcriptmics analysis reveals , , and as crucial biomarkers and therapeutic targets in sepsis.

Sepsis is a life-threatening condition characterized by a dysregulated host response to infection, resulting in high mortality rates and complex clinical management. This study leverages transcriptomics and machine learning (ML) to identify critical biomarkers and therapeutic targets in sepsis. Analyzing microarray data from the Gene Expression Omnibus (GEO) datasets GSE28750, GSE26440, GSE13205, and GSE9960, we discovered three pivotal biomarkers that (bone marrow tyrosine kinase gene on chromosome X), growth factor receptor bound protein 10), and (growth arrest and DNA damage inducible alpha), exhibiting exceptional diagnostic accuracy (AUC >0.9). Functional enrichment analyses revealed that these genes play key roles in reactive oxygen species metabolism and immune response regulation. Specifically, was positively correlated with eosinophils and inversely associated with activated NK cells, CD8 T cells, and activated memory CD4 T cells. showed positive correlations with eosinophils, mast cells, and neutrophils, while was linked to eosinophils and M2 macrophages. Additionally, we constructed a comprehensive mRNA-miRNA-lncRNA regulatory network, identifying key interactions that may drive sepsis pathogenesis. Molecular docking and dynamics simulations validated Bendroflumethiazide, Cianidanol, and Hexamidine as promising therapeutic agents targeting these biomarkers. In conclusion, this integrated approach provides profound insights into the molecular mechanisms underlying sepsis, pinpointing , , and as pivotal biomarkers and therapeutic targets. These findings significantly enhance our understanding of sepsis pathophysiology and lay the groundwork for developing personalized diagnostic and therapeutic strategies aimed at improving patient outcomes.