Unraveling the Underlying Comorbidity Mechanisms of Atopic Dermatitis and Periodontitis via Bioinformatics

Purpose: Atopic dermatitis (AD) and periodontitis (PD) exhibit overlapping immune and adhesion processes (e.g., Th2-skewing in AD and Th1/Th17 responses with Th2 components in PD). We aimed to identify shared transcriptomic signatures and prioritise candidate genes, miRNAs, and transcription factors linking AD and PD using integrative bioinformatics to generate hypotheses for subsequent experimental and clinical validation. Methods: Expression profiles from AD (GSE120721, GSE182740) and PD (GSE16134, GSE23586) datasets were analyzed. Differentially expressed genes (DEGs) shared between both conditions were identified and validated. Analyses included GO and KEGG enrichment, PPI network construction, and identification of hub genes, miRNA interactions, and transcription factors. Results: 124 DEGs were identified, with 13 hub genes enriched in immune response and cell adhesion pathways. Machine learning refined these to 4 key genes (CD69, MMP9, PXDN, VCAM1). ROC analysis validated these genes’ diagnostic efficacy (AUC > 0.7). Networks of key genes with miRNA and transcription factors (NFKB1, RELA, IRF1, EP300, JUN, IKBKB). Key miRNAs included hsa-miR-9-5p, hsa-miR-21-5p, hsa-miR-143-3p, and hsa-miR-155-5p. We also demonstrated the diagnostic potential of these genes (AUC > 0.7) across both conditions. Conclusions: The hub genes, key miRNAs, and transcription factors identified in this study may serve as biomarkers and candidate therapeutic targets for AD and PD comorbidity. These findings are hypothesis-generating and warrant experimental validation and prospective clinical studies to assess disease relevance and potential clinical utility.