Integrative Gene Expression Analysis to Identify Potential Biomarkers in Candida Pathogenesis

Abstract

Candida species are prevalent fungal pathogens that cause invasive infections, frequently leading to significant morbidity and mortality. Timely diagnosis and efficacious treatment continue to pose difficulties owing to intricate host-pathogen interactions and the absence of precise biomarkers. The objective of this study was to find possible diagnostic and therapeutic biomarkers for Candida infections by employing differential gene expression analysis, enrichment analysis, and protein-protein interaction (PPI) network modeling utilizing transcriptome datasets (GSE114174 and GSE114179). Results, a total of 216 shared differentially expressed genes (DEGs) were found among four Candida species: C. albicans, C. glabrata, C. parapsilosis, and C. tropicalis. The ten principal hub genes comprised FOSB, EGR1, JUNB, ATF3, EGR2, NR4A1, NR4A2, DUSP1, BTG2, and EGR3, several of which serve as crucial regulators of immune response and cellular stress signaling. GO and KEGG enrichment analysis indicated substantial participation in inflammatory response, transcriptional control, and pathways including cell cycle, DNA replication, and glycan production. These genes exhibited a strong association with β-glucan recognition pathways and HSP70-mediated stress responses. The discovered hub genes serve as promising indicators and possible treatment targets for Candida infections. Their connections to fungal cell wall constituents (e.g., β-glucan) and stress response proteins (e.g., HSP70) emphasize their significance in host-pathogen interactions and immune evasion strategies. Additional experimental validation is necessary to evaluate their effectiveness in clinical diagnostics and targeted antifungal approaches.