Lu Quansheng, He Xi, Sun Yao, Lu Yu, Jiang Guan
Department of Dermatology, Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China.
Department of Dermatology, The People's Hospital of Jiawang District of Xuzhou, Xuzhou, Jiangsu, China.
Dermatol Res Pract. 2025 Sep 5;2025:6672081. doi: 10.1155/drp/6672081. eCollection 2025.
Vitiligo is a hypopigmentation skin disease that is easy to diagnose but difficult to treat. The etiology of vitiligo is unknown, which may be related to genetic and immune factors. To provide potential targets for the treatment of vitiligo through identifying signature genes based on an artificial neural network (ANN) model. We downloaded two publicly available datasets from GEO database and identified DEGs. We trained the random forest and ANN algorithm using training set GSE75819 to further identify new gene features and predicted the possibility of vitiligo. In addition, we further validated the performance of our model through the test set GSE53148 and verified the diagnostic value of our model with the validation set GSE53148. Finally, we used RT-qPCR to compare the expression of two genes randomly selected in this study in patients with vitiligo and healthy people. Two genes were randomly selected from the 30 key genes identified by ANN and validated through RT-qPCR in 6 vitiligo patients. The results showed that compared with the control group, the mRNA expression of FLJ21901 in the disease group was significantly upregulated, and the mRNA expression of MAST1 was significantly downregulated, with statistical significance. Through the identification of characteristic genes and the construction of a neural network model, it was found that the differentially expressed genes can provide a new potential target for the treatment of vitiligo.
白癜风是一种色素减退性皮肤病，易于诊断但难以治疗。白癜风的病因尚不清楚，可能与遗传和免疫因素有关。为了通过基于人工神经网络（ANN）模型识别特征基因来为白癜风治疗提供潜在靶点。我们从GEO数据库下载了两个公开可用的数据集并鉴定了差异表达基因（DEGs）。我们使用训练集GSE75819训练随机森林和ANN算法，以进一步识别新的基因特征并预测白癜风的可能性。此外，我们通过测试集GSE53148进一步验证了模型的性能，并用验证集GSE53148验证了模型的诊断价值。最后，我们使用RT-qPCR比较了本研究中随机选择的两个基因在白癜风患者和健康人中的表达。从ANN鉴定出的30个关键基因中随机选择两个基因，并在6例白癜风患者中通过RT-qPCR进行验证。结果显示，与对照组相比，疾病组中FLJ21901的mRNA表达显著上调，MAST1的mRNA表达显著下调，具有统计学意义。通过鉴定特征基因和构建神经网络模型，发现差异表达基因可为白癜风治疗提供新的潜在靶点。
