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Background: Opportunistic Candida species 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 Candida species. 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 parapsilosis and Candida tropicalis groups 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 cytoHubba plug-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 Candida species 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 Candida species infection. [ABSTRACT FROM AUTHOR]

• Investigating the underlying molecular mechanisms of Cutaneous Leishmaniasis. • Screening for diagnostic markers related to immune cells by bioinformatics analysis. • Uncovering the important roles of immune response in Cutaneous Leishmaniasis. Cutaneous Leishmaniasis (CL) is the most common clinical form of leishmaniasis. Despite its low mortality, CL deserves further attention because its pathogenesis is currently no well-known or well-researched. We downloaded the gene expression datasets of GSE55664 and GSE63931 with respect to leishmaniasis from the Gene Expression Synthesis (GEO) database. Additionally, the differentially expressed genes (DEGs) in the infection and control groups were identified by packages of R software. The Gene Ontology (GO) function, Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) pathway were utilized for the biological functional analysis. Subsequently, we identified the top ten hub genes from protein-protein interaction (PPI) networks based on STRING and Cytoscape software. The hub genes were validated in GraphPad Prism 8.0 using the GSE162760 dataset. Further, CIBERSORT was used to evaluate the immune cell infiltration proportions between the CL infection samples and the control samples based on the GSE43880 and GSE55664 datasets. The enrichment analysis revealed that DEGs were significantly involved in cell-mediated immune responses, such as leukocyte cell-cell adhesion and T-cell activation. STAT1, CCR7, CCR2, and CXCL10 were identified as hub genes with statistical significance. These hub genes showed close correlations with various immune cells, such as M1 cells and CD4-activated memory T-cells. In our research, we used bioinformatics analysis to identify some molecular biomarkers and significant pathways in CL infection. These hub genes may provide new options for future diagnosis and treatment. [ABSTRACT FROM AUTHOR]

Helicobacter pylori is a pathogen involved in several gastroduodenal diseases, whose infection mechanisms have not been completely confirmed. To study the specific mechanism of gastropathy caused by H. pylori , we analyzed the gene microarray of gastric mucosa and gastric cells infected by H. pylori through bioinformatics analysis. We downloaded GSE60427 and GSE74492 from the Gene Expression Omnibus (GEO) database, screened differentially expressed genes (DEGs), and identified the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) through R software. The Search Tool for the Retrieval of Interacting Genes (STRING) was applied to establish a protein–protein interaction (PPI) network and Cytoscape was used to identify the top seven hub genes. Besides, we also constructed the gene–microRNA(gene–miRNA) interaction through the miRTarBase v8.0 database by using the NetworkAnalyst tool. One hundred and fifteen DEGs were screened out, with 54 genes up-regulated and 61 genes down-regulated, among which seven hub genes, including "IGF1R," "APOE," "IRS1," "ATF3," "LCN2," "IL2RG," and "PI3," were considered as the main regulatory proteins in gastric cells when infected by H. pylori. In this study, hub genes and related signal enrichment pathways of gastropathy infected by H. pylori were analyzed through bioinformatics analysis based on the GSE60427 and GSE74492 datasets. • Bioinformatics revealed the signaling pathways related to Helicobacter pylori infection in gastric cells. • Bioinformatics revealed the main differential genes of Helicobacter pylori infection in gastric cells. • The study first found that IGF1R was down regulated in Helicobacter pylori with proper conjecture. [ABSTRACT FROM AUTHOR]