Integrative bioinformatics analysis of immune activation and gene networks in pediatric septic arthritis.

Abstract

Background: Pediatric septic arthritis, driven by Staphylococcus aureus, leads to substantial morbidity due to the host's complex inflammatory response. This study integrates bioinformatics analyses to map the genomic and immune profiles of pediatric septic arthritis, aiming to identify key biomarkers and therapeutic targets.

Methods: An integrative bioinformatics approach was adopted to analyze gene expression datasets from the GEO database, focusing on pediatric septic arthritis. DEGs were identified using GEO2R, and gene co-expression networks were generated via GeneMANIA. STRING database and Cytoscape software facilitated PPI network construction. DAVID enabled functional enrichment analysis to elucidate biological processes and pathways, while iRegulon predicted transcription factor regulation. CIBERSORT provided a detailed profile of immune cell alterations in the condition.

Results: From the datasets analyzed, 576 DEGs were extracted, with 35 shared between the two datasets, revealing an innate immunity signature with notable hub genes such as MPO and ELANE, indicative of a pronounced neutrophilic response. Functional enrichment analysis highlighted pathways pertinent to antimicrobial defense and NET formation. Key transcription factors, including PBX1, POLR2A, and STAT3, were identified as potential modulators of these pathways. Immune profiling demonstrated significant shifts in cell populations, with increased plasma cells and reduced CD4+ naïve T cells.

Conclusions: This study elucidates the complex genomic and immunological milieu of pediatric septic arthritis, uncovering potential biomarkers and signaling pathways for targeted therapeutic intervention. These findings underscore the preeminence of innate immune mechanisms in the disease's pathology and offer a foundation for future research to explore diagnostic and treatment innovations. Translation of these bioinformatics discoveries into clinical applications requires further validation and consideration of the limitations inherent to gene expression data and its interpretation.