Keratinocyte-Associated Biomarkers Reveal Pathogenic Mechanisms in Acne

Area of Science:

• Dermatology and Computational Biology
• Investigates the molecular underpinnings of skin diseases using advanced bioinformatics.

Background:

• Acne pathogenesis is complex, limiting targeted therapy development.
• The role of keratinocytes in acne-related inflammation is not fully understood.
• Identifying specific molecular signatures is crucial for advancing acne treatment.

Purpose of the Study:

• To elucidate the transcriptional programs and regulatory networks of keratinocytes in acne.
• To identify novel, disease-relevant biomarkers for acne pathogenesis.
• To explore potential therapeutic interventions through molecular signature analysis.

Main Methods:

• Integrated computational framework combining single-cell RNA sequencing (scRNA-seq) and Weighted Gene Co-expression Network Analysis (WGCNA).
• Utilized machine learning algorithms (SVM-RFE, LASSO) for biomarker identification.
• Mapped cellular composition, keratinocyte differentiation, and miRNA-transcription factor-drug interactions.

Main Results:

• Revealed significant keratinocyte heterogeneity and pro-inflammatory states in acne lesions.
• Identified six keratinocyte-associated biomarkers (PYGL, C10orf99, C12orf75, S100A2, PI3, CARD18) with high diagnostic accuracy (AUC > 0.85).
• Uncovered upstream regulators (hsa-let-7b-5p, FOXC1) and suggested drug repurposing (cyclosporin A, valproic acid).

Conclusions:

• Delineated a keratinocyte-centered molecular signature critical to acne pathogenesis.
• Established potential therapeutic biomarkers for targeted acne treatments.
• Highlighted the utility of integrative computational approaches in skin disease research.