Sepsis, characterized by systemic inflammatory response syndrome and life-threatening organ dysfunction, remains a significant global cause of disability and death. Despite its impact, reliable biomarkers for sepsis diagnosis are yet to be identified.
This study aims to investigate and identify key genes and pathways in sepsis through the analysis of multiple microarray datasets, providing potential treatment targets for future clinical trials.
Two independent gene expression profiles (GSE54514 and GSE69528) were downloaded from the Gene Expression Omnibus (GEO) database. After merging and batch normalization, differentially expressed genes (DEGs) were obtained using the “limma” package. Gene Ontology (GO) and Gene Set Enrichment Analysis (GSEA) were performed using “R” software. A Protein-Protein Interaction (PPI) network was constructed using the Search Tool for the Retrieval of Interacting Genes (STRING). The top 10 hub genes were identified using Cytoscape. A Nomogram model for predicting sepsis occurrence was constructed and evaluated.
Bioinformatic analysis of 210 sepsis and 91 control blood samples identified 72 DEGs. GO analyses revealed associations with immune response processes. GSEA indicated involvement in key signaling pathways. S100A12, MMP9, and PRTN3 were identified as independent risk factors for sepsis.
This study unveils critical genes and pathways in sepsis through bioinformatic methods. S100A12, MMP9, and PRTN3 may play essential roles in the immune response to infection, influencing sepsis prognosis.

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