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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., (© 2022. The Author(s).)

Dengue fever virus (DENV) is a global health threat that is becoming increasingly critical. However, the pathogenesis of dengue has not yet been fully elucidated. In this study, we employed bioinformatics analysis to identify potential biomarkers related to dengue fever and clarify their underlying mechanisms. The results showed that there were 668, 1901, and 8283 differentially expressed genes between the dengue-infected samples and normal samples in the GSE28405, GSE38246, and GSE51808 datasets, respectively. Through overlapping, a total of 69 differentially expressed genes (DEGs) were identified, of which 51 were upregulated and 18 were downregulated. We identified twelve hub genes, including MX1, IFI44L, IFI44, IFI27, ISG15, STAT1, IFI35, OAS3, OAS2, OAS1, IFI6, and USP18. Except for IFI44 and STAT1, the others were statistically significant after validation. We predicted the related microRNAs (miRNAs) of these 12 target genes through the database miRTarBase, and finally obtained one important miRNA: has-mir-146a-5p. In addition, gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment were carried out, and a protein–protein interaction (PPI) network was constructed to gain insight into the actions of DEGs. In conclusion, our study displayed the effectiveness of bioinformatics analysis methods in screening potential pathogenic genes in dengue fever and their underlying mechanisms. Further, we successfully predicted IFI44L and IFI6, as potential biomarkers with DENV infection, providing promising targets for the treatment of dengue fever to a certain extent. Author summary: Dengue fever is a mosquito borne viral disease caused by a single stranded RNA virus with four serotypes. DENV infection can cause various diseases, such as breakbone fever, haemorrhagic fever, and shock syndrome. As one of the most viral diseases leading to incidence rate and mortality in animal arthropods, Dengue fever has become an increasingly serious global health threat. However, the pathogenesis of dengue fever has not been fully elucidated. In this study, we used bioinformatics analysis to identify potential biomarkers associated with dengue fever and elucidate their underlying mechanisms. Finally, we predicted that IFI44L and IFI6 might be potential biomarkers of DENV infection. This finding provides a promising target for the treatment of dengue fever to a certain extent. In addition, the Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment, protein–protein interaction (PPI) network were implemented to analyze the key differentially expressed genes after DENV infection, and the related mechanisms were illuminated by this study. [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]