Previously submitted to: JMIR Dermatology (no longer under consideration since May 02, 2025)
Date Submitted: Dec 23, 2024
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Exploring potential biomarkers for acne based on WGCNA and machine learning algorithms
ABSTRACT
Background:
Acne stands as a prevalent chronic inflammatory dermatological condition, typically characterized by concurrent skin inflammation and scarring.
Objective:
Currently, although many studies have been conducted on the pathogenesis of acne, a comprehensive understanding of its potential biomarkers is still pending.
Methods:
Key genes and immune cell characteristics associated with acne were investigated through bioinformatics analysis of transcriptomic data from acne patients in the public database GEO.
Results:
A total of 198 significantly differentially expressed genes were identified in GSE6475 and GSE53795 datasets. Gene modules significantly associated with disease traits were identified by WGCNA network analysis, and their functional enrichment was further analyzed. The best diagnostic genes were LRRC17, TCN1, S100A12 and CCL19. The accuracy of the diagnostic model was verified by ROC curves and expression was validated in both datasets. GSEA functional enrichment analysis revealed the biological functions of these diagnostic genes. In addition, the analysis of immune cell scores by CIBERSORT algorithm revealed the differences in immune cells between acne patients and healthy patients, and found the correlation between multiple immune cells and diagnostic gene presentation.
Conclusions:
This bioinformatics study provides insights into the molecular mechanisms and immune regulation of acne and uncovers potential biomarkers that provide important clues for future disease diagnosis and treatment. Clinical Trial: Not applicable.
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