Wang Qiyu, Yuan Jingwei, Zhang Mengdi, Jia Haiyan, Lu Hongjie, Wu Yan
Beijing Technology and Business University, Beijing, China.
Air Force Medical Center of the Chinese People's Liberation Army, Beijing, China.
Front Immunol. 2025 May 15;16:1543355. doi: 10.3389/fimmu.2025.1543355. eCollection 2025.
Vitiligo is a skin disorder characterized by the progressive loss of pigmentation in the skin and mucous membranes. The exact aetiology and pathogenesis of vitiligo remain incompletely understood.
First, a microarray dataset of blood samples from multiple patients with vitiligo was collected from GEO database.The limma package was used to analyze the microarray data and identify significant differentially expressed genes (DEGs). The merged microarray data were then used for WGCNA to identify modules of features genes. DEGs selected with the limma package and module genes derived from the WGCNA were intersected using the Venn package in R. Enrichment analyses were performed on the overlapping genes, including Gene Ontology and Kyoto Encyclopedia of Genes and Genomes methodology. Advanced screening was performed using the least absolute shrinkage and selection operator and support vector machine techniques from the machine learning toolkit. CIBERSORT was used to analyse the immune cell composition in the microarray data to assess the relationships among these genes and immune cells. Biological samples were obtained from the patients, and gene expression analysis was performed to evaluate the levels of core genes throughout the progression of vitiligo. Finally, we obtained the microarray datasets GSE53146 and GSE75819 from the affected skin of vitiligo patients and GSE205155 from healthy skin to perform expression analysis and gene set enrichment analysis of the hub genes.
Two hub genes, and , were identified via machine learning and WGCNA. The analysis of immune cell infiltration suggested that different immune cell types could play a role in the progression of vitiligo. Moreover, these hub genes exhibited varying degrees of association with immune cell profiles. qRT-PCR analysis of blood samples from vitiligo patients revealed notable downregulation of the hub genes. Analysis of the microarray datasets derived from skin lesions revealed that expression levels remained relatively stable, whereas expression levels markedly decreased.
may influence vitiligo development via the Nod-like receptor signaling pathway and could serve as a potential diagnostic marker for evaluating skin lesions in vitiligo.
白癜风是一种以皮肤和黏膜色素沉着进行性丧失为特征的皮肤疾病。白癜风的确切病因和发病机制仍未完全明确。
首先，从基因表达综合数据库（GEO数据库）收集多个白癜风患者血液样本的微阵列数据集。使用limma软件包分析微阵列数据并鉴定显著差异表达基因（DEG）。然后将合并后的微阵列数据用于加权基因共表达网络分析（WGCNA）以鉴定特征基因模块。使用R语言中的Venn软件包对通过limma软件包选择的DEG和源自WGCNA的模块基因进行交集分析。对重叠基因进行富集分析，包括基因本体论和京都基因与基因组百科全书方法。使用机器学习工具包中的最小绝对收缩和选择算子以及支持向量机技术进行进一步筛选。使用CIBERSORT分析微阵列数据中的免疫细胞组成，以评估这些基因与免疫细胞之间的关系。从患者获取生物样本，并进行基因表达分析以评估核心基因在白癜风整个病程中的水平。最后，我们从白癜风患者的患病皮肤中获得微阵列数据集GSE53146和GSE75819，以及从健康皮肤中获得GSE205155，以进行枢纽基因的表达分析和基因集富集分析。
通过机器学习和WGCNA鉴定出两个枢纽基因。免疫细胞浸润分析表明，不同的免疫细胞类型可能在白癜风的进展中起作用。此外，这些枢纽基因与免疫细胞谱表现出不同程度的关联。对白癜风患者血液样本的定量逆转录聚合酶链反应（qRT-PCR）分析显示枢纽基因明显下调。对源自皮肤病变的微阵列数据集分析表明，[基因名称1]表达水平保持相对稳定，而[基因名称2]表达水平明显下降。
[基因名称1]可能通过核苷酸结合寡聚化结构域样受体信号通路影响白癜风的发展，并可作为评估白癜风皮肤病变的潜在诊断标志物。
