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Lipid metabolism is abnormal in patients with atopic dermatitis (AD). This study aimed to screen lipid metabolism-related gene (LMRG) in AD, providing insights into the underlying mechanisms of lipid metabolism abnormalities in AD. Gene expression profiles from the Gene Expression Omnibus were analyzed to identify a hub LMRG in AD through an integrative approach combining weighted gene co-expression network analysis, differential expression analysis, and machine learning. The diagnostic value of the hub gene was assessed by receiver operating characteristic curve analysis. Its biological functions and associations with immune cells were investigated using Gene Set Enrichment Analysis (GSEA)/GSVA and ssGSEA/correlation analysis, respectively. The expression of ganglioside GM2 activator ( GM2A ) in clinical samples was measured using reverse transcription quantitative polymerase chain reaction and an ELISA assay. We identified 14 LMRGs that were differentially expressed between AD and normal samples and were correlated with AD onset. By three machine learning algorithms, GM2A was identified as a robust LMRG in AD. GM2A expression was observed to be elevated in AD samples across transcriptomic analyses and clinical validation. Moreover, GM2A might be effective in distinguishing between individuals with AD and those without. We further discovered that GM2A was associated with immunomodulation, inflammatory response, and biosynthesis of unsaturated fatty acid pathways. GM2A expression was positively correlated with follicular helper T cells, Activated CD4 + T cells, and natural killer T cells. Besides, there was a significant relationship between GM2A and multiple drugs. This study highlighted the significant up-regulation of GM2A as an efficient biomarker for AD, linking it to immune and inflammatory responses as well as immune cell infiltration.
Wang et al. (Sat,) studied this question.