Identification of novel candidate genes in rosacea by bioinformatic methods.

BACKGROUND

Rosacea is a chronic inflammatory skin disease whose psychological consequences severely affect patient's quality of life.

OBJECTIVE

To identify candidate genes of rosacea for potential development of new target therapies.

METHODS

Gene Expression Omnibus datasets were retrieved to obtain differentially expressed genes (DEGs) between rosacea patients and healthy controls. Gene ontology (GO) analyses were used to identify functions of candidate genes. Related signaling pathways of DEGs were analyzed using Kyoto Encyclopedia of Genes and Genomes (KEGG) and gene set enrichment analysis. Protein-protein interaction (PPI) networks were applied using search tools for the retrieval of interacting genes/proteins and modulations involving PPI networks were evaluated with use of the MCODE app.

RESULTS

Samples from 19 rosacea patients and 10 healthy controls of dataset GSE65914 were enrolled. A total of 215 DEGs, 115 GO terms and 6 KEGG pathways were identified. A total of 182 nodes and 456 edges were enriched in PPI networks. Maximal clusters showed 15 central nodes and 96 edges. The toll-like receptor (TLR) signaling pathway was the most significant pathway detected and 5 DEGs were identified as candidate genes which included TLR2, C-C motif chemokine (CCL) 5, C-X-C motif chemokine ligand (CXCL) 9, CXCL10 and CXCL11. The results were verified in rosacea patients with use of real-time polymerase chain reaction and immunohistochemistry. Cell-type enrichment analysis revealed 8 lymphocytes that were enriched in rosacea patients.

CONCLUSIONS

The results suggest that both innate and adaptive immune responses were involved in the etiology of rosacea. Five DEGs in the TLR signaling pathway may serve as potential therapeutic target genes.