Dissecting the Role of NETosis-Related Biomarkers in Sepsis: An Integrated Multi-Dataset Analysis for Diagnostic and Prognostic Applications

1.Abstract: Sepsis, a systemic and life-threatening response to infection, presents complex challenges in clinical management and prognosis due to its intricate pathophysiology. The formation of Neutrophil Extracellular Traps (NETs) through a process known as NETosis has been identified as a significant contributor to the development of sepsis. This study aimed to dissect the roles of NETosis-related genes, particularly Myeloperoxidase (MPO) and Proteinase 3 (PRTN3), in sepsis progression. By integrating and analyzing multiple Gene Expression Omnibus (GEO) datasets, we conducted a comprehensive gene expression profiling that revealed consistent downregulation of MPO and PRTN3, among others, in sepsis patients. Through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses, we characterized the biological functions and pathways associated with these genes, emphasizing their relevance to immune responses in sepsis. A prediction model utilizing these biomarkers was constructed using a Random Forest classifier, which demonstrated robust predictive capability, as reflected by an AUROC of 0.77 for training and 0.68 for validation datasets. Survival analysis further underscored the prognostic value of demographic factors, particularly gender and age. The model highlighted gender-specific survival rates and revealed a significant decline in survival probability in patients over 40 years of age. These findings illuminate the diagnostic and prognostic potential of MPO and PRTN3 in sepsis, offering novel insights into the molecular dynamics of the disease and suggesting a direction for future therapeutic strategies. The study's integrated approach and novel findings advocate for personalized management of sepsis, tailoring interventions to individual patient profiles to improve outcomes.