Identification of differentially expressed genes and signaling pathways with Candidainfection by bioinformatics analysis

Background: Opportunistic Candidaspecies causes severe infections when the human immune system is weakened, leading to high mortality. Methods: In our study, bioinformatics analysis was used to study the high-throughput sequencing data of samples infected with four kinds of Candidaspecies. And the hub genes were obtained by statistical analysis. Results: A total of 547, 422, 415 and 405 differentially expressed genes (DEGs) of Candida albicans, Candida glabrata, Candida parapsilosisand Candida tropicalisgroups were obtained, respectively. A total of 216 DEGs were obtained after taking intersections of DEGs from the four groups. A protein–protein interaction (PPI) network was established using these 216 genes. The top 10 hub genes (FOSB, EGR1, JUNB, ATF3, EGR2, NR4A1, NR4A2, DUSP1, BTG2, and EGR3) were acquired through calculation by the cytoHubbaplug-in in Cytoscape software. Validated by the sequencing data of peripheral blood, JUNB, ATF3 and EGR2 genes were  significant statistical significance. Conclusions: In conclusion, our study demonstrated the potential pathogenic genes in Candidaspecies and their underlying mechanisms by bioinformatic analysis methods. Further, after statistical validation, JUNB, ATF3 and EGR2 genes were attained, which may be used as potential biomarkers with Candidaspecies infection.