Bioinformatics analysis reveals the characteristics of immune microenvironment in major depressive disorder and vitiligo - Yayun Xiang; Hongmei Deng; Yanyue Wang; Ning Yan; Yang Lei (PLoS ONE, 2026)
Background Major depressive disorder (MDD) and vitiligo often occur together, worsening patient outcomes. However, the shared pathogenic mechanisms remain unclear. Methods This study applied integrated bioinformatics to identify shared candidate markers for MDD and vitiligo. Public transcriptomic datasets from the GEO database were analyzed for differential expression. Protein-protein interaction (PPI) networks were constructed using the STRING database. Shared differentially expressed genes (DEGs) underwent GO and KEGG functional enrichment analyses. Three machine-learning algorithms were applied to select candidate biomarker genes. Additionally, immune infiltration analysis was quantified through ssGSEA and a TF-miRNA network was constructed via NetworkAnalyst platform. Single-gene GSEA further explored pathways linked to the biomarker in both diseases. Results Differential expression analysis and PPI network construction suggest the involvement of 14 hub genes potentially linked to both MDD and vitiligo. Functional enrichment analyses indicate their putative roles in immune processes and inflammatory responses. Machine learning further prioritized three key genes: EXOSC7 , KLRG1 , and MAPK14 . Immune infiltration analysis revealed distinct patterns of inferred immune enrichment signatures, and the TF-miRNA network highlighted the complexity of the regulatory landscape. Preliminary validation suggests MAPK14 as a potential candidate gene warranting further investigation in MDD and vitiligo. Conclusion This study provides preliminary evidence suggesting that immune dysregulation and inflammatory activation may be interconnected in MDD and vitiligo. MAPK14 represents a potential candidate marker for their comorbidity. These findings primarily serve to generate hypotheses regarding shared mechanisms and prioritize targets for subsequent experimental validation.