Identification of Genes and Pathways Associated with Acne Using Integrated Bioinformatics Methods

Area of Science:

• Dermatology
• Genomics
• Bioinformatics

Background:

• Acne is a prevalent inflammatory skin condition with incompletely understood pathogenesis.
• Identifying molecular drivers of acne is crucial for developing effective treatments.

Purpose of the Study:

• To identify acne-associated genes and pathways using weighted gene co-expression network analysis (WGCNA).
• To uncover potential molecular targets for acne therapeutic management.

Main Methods:

• Downloaded and analyzed gene expression datasets (GSE53795, GSE6475) from lesional and nonlesional acne skin.
• Identified differentially expressed genes (DEGs) and performed WGCNA to find acne-associated gene modules.
• Constructed protein-protein interaction networks and utilized Gene Set Enrichment Analysis (GSEA) and Comparative Toxicogenomics Database (CTD) for candidate identification.

Main Results:

• Identified 716 overlapping DEGs across two datasets, clustering into 10 consensus modules.
• Two modules (brown and turquoise) comprising 359 DEGs were significantly associated with acne phenotype.
• GSEA revealed enrichment in chemokine signaling, cytokine-cytokine receptor interaction, and natural killer cell-mediated cytotoxicity pathways. Identified 24 candidate DEGs, including IL1R1, CXCL1, CXCR4, CCR1, CXCL2, and IL1β, and the Cytokine-cytokine receptor interaction pathway.

Conclusions:

• The study highlights the significant role of proinflammatory cytokines, specifically IL1β, CXCL1, CXCL2, CXCR4, and CCR1, in acne pathogenesis.
• These identified cytokines represent potential targets for future acne therapeutic strategies.