Identification and Analysis of Diagnostic and Prognostic Biomarker Genes in Sepsis using Differential Gene Expression and Protein Interaction Networks

Sepsis, a complex medical condition characterized by physiological and biochemical abnormalities, arises from an imbalanced host response to infection. Despite its global impact, sepsis remains underreported in economically challenged nations, highlighting the need for improved diagnostic and prognostic indicators. The emergence of gene chip technology has provided insights into differential gene expression in sepsis, paving the way for identifying pivotal genes involved in disease progression. In this study, we employed bioinformatics analysis to investigate hub genes associated with sepsis, construct a protein interaction network, and identify potential therapeutic targets. Utilizing the microarray dataset GSE95233, we conducted KEGG pathway analysis and Gene Ontology enrichment analysis through ShinyGO 0.80. Furthermore, we constructed a protein-protein interaction network using String and identified hub genes using CYTOSCAPE software. Our findings revealed a repertoire of crucial genes, including CD4, CD8A, CCL5, IL7R, MMP9, GZMB, PRF1, TBX21, S100A12, and IL2RB, displaying significant expression patterns in sepsis and septic shock patients. These genes hold great potential as diagnostic biomarkers for sepsis, offering non-invasive diagnostic approaches and serving as viable targets for future sepsis therapeutics. Moreover, our study sheds light on the diverse range of bacterial and viral infections that could contribute to the development of sepsis in affected individuals, enhancing our understanding of this intricate condition. The utilization of bioinformatics and gene expression profiling represents a promising avenue for advancing sepsis management and improving patient outcomes.