Bioinformatics Analysis for Identifying Pertinent Pathways and Genes in Sepsis

Area of Science:

• Genomics
• Bioinformatics
• Molecular Biology

Background:

• Sepsis is a leading cause of hospital mortality with increasing incidence.
• The genetic basis of sepsis pathogenesis and prognosis remains incompletely understood.
• Urgent need for identifying sepsis-related genes and pathways to address the growing burden.

Purpose of the Study:

• To identify differentially expressed genes (DEGs) associated with sepsis.
• To elucidate the biological pathways and networks involved in sepsis.
• To discover potential gene biomarkers for sepsis.

Main Methods:

• Utilized gene expression profiles from the GSE69528 dataset.
• Employed Limma software for differential gene expression analysis.
• Performed enrichment analysis using Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO).
• Constructed a protein-protein interaction (PPI) network using the STRING database.

Main Results:

• Identified 101 DEGs (81 upregulated, 20 downregulated).
• Upregulated DEGs were enriched in interferon-gamma response and granulocyte differentiation.
• KEGG analysis implicated prion diseases, complement/coagulation cascades, and Staphylococcus aureus infection pathways.
• Identified CEACAM8, MPO, and RETN as crucial hub genes in the sepsis PPI network.

Conclusions:

• Discovered significant signal pathways and key genes implicated in sepsis mechanisms.
• These identified genes and pathways represent promising therapeutic targets and diagnostic biomarkers for sepsis.