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Updated: Aug 15, 2026
Cutaneous Leishmaniasis in the Dorsal Skin of Hamsters: a Useful Model for the Screening of Antileishmanial Drugs
Published on: April 21, 2012
Screening of potential hub genes involved in Cutaneous Leishmaniasis infection via bioinformatics analysis
Jia-Xin Li1, Yuan-Yi Huang1, Ze-Min Huang2
1Department of Clinical Laboratory Medicine, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou 510150, China; Department of Clinical Medicine, The First Clinical School of Guangzhou Medical University, Guangzhou 511436, China.
Background:
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.
Methods:
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.
Results:
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.
Conclusions:
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.
