目的 通过生物信息学方法分析白癜风疾病中铁死亡基因及相关发病机制,并筛选通过铁死亡相关途径治疗白癜风的潜在药物。方法 从FerrDB数据库获取铁死亡基因,通过R分析数据集GSE53146中差异表达基因,随后二者取交集。通过SVM-REF算法和LASSO回归构建机器学习模型预测白癜风铁死亡关键基因并通过数据集GSE75819进行基因表达验证。GSE203262单细胞数据进行细胞聚类分析,发现与关键基因高度相关且参与白癜风发病的关键细胞群,随后通过HPA数据库对基因表达细胞进行验证。利用cAMP数据库筛选关键基因相关小分子药物,利用分子对接技术验证小分子化合物与基因结合的能力。最后进行单基因免疫细胞相关性分析及GSEA-KEGG分析探讨小分子药物治疗白癜风的相关免疫机制。结果 获得458个铁死亡基因和706个差异表达基因,二者交集基因23个。机器学习预测模型筛选出RRM2、LCN2、OTUB1、SNCA、CTSB、WWTR1作为关键基因。外部数据集验证、单细胞聚类和HPA数据均提示关键基因中OTUB1、CTSB和LCN2主要在角质形成细胞、黑素细胞和朗格汉斯细胞等重要皮肤细胞中表达。通过高通量筛选和分子对接验证,获得雷公藤甲素作为通过铁死亡途径治疗白癜风的小分子药物。免疫细胞相关性分析发现雷公藤甲素通过影响关键基因调控自然杀伤细胞、活化的CD8⁺ T细胞等免疫细胞的功能。GSEA-KEGG分析发现雷公藤甲素可能通过趋化因子信号通路、机体代谢信号通路和NOD样受体信号通路产生治疗白癜风的作用。结论 利用生物信息学方法发现在白癜风发病中重要的铁死亡证据及相关机制,并以此为插入点筛选到雷公藤甲素作为潜在治疗药物,对白癜风发病及治疗研究具有重要意义。

Objective To analyze ferroptosis genes and related pathogenesis in vitiligo diseases by bioinformatics methods and to explore potential drugs for the treatment of vitiligo through ferroptosis related pathways. Methods Ferroptosis genes were obtained from the FerrDB database and differentially expressed genes in the dataset GSE53146 were analyzed by R. Subsequently, the two were taken to intersect. A machine learning model was constructed by SVM-REF algorithm and LASSO regression to predict key genes for ferroptosis in vitiligo and validated for gene expression by dataset GSE75819. Cell clustering analysis of the GSE203262 single-cell data identified key cell populations that were highly correlated with key genes and involved in vitiligo pathogenesis, which were subsequently validated against gene-expressing cells by the HPA database. The cAMP database was utilized to screen key gene-related small molecule drugs, and molecular docking technology was utilized to verify the ability of small molecule compounds to bind to genes. Finally, single gene immune cell correlation analysis and GSEA-KEGG analysis were performed to explore the immune mechanisms associated with small molecule drugs for treating vitiligo. Results 458 ferroptosis genes and 706 differentially expressed genes were obtained, and 23 genes were intersected by the two. The machine learning prediction model screened RRM2, LCN2, OTUB1, SNCA, CTSB, and WWTR1 as key genes. External dataset validation, single-cell clustering, and HPA data all suggested that the key genes, OTUB1, CTSB, and LCN2, were predominantly expressed in important skin cells such as keratinocytes, melanocytes, and Langerhans cells. High-throughput screening and molecular docking validation were performed to obtain triptolide as a small molecule drug for the treatment of vitiligo via the ferroptosis pathway. Immune cell correlation analysis revealed that triptolide modulates the function of immune cells such as natural killer T cell and activated CD8 T cell by affecting the key genes. GSEA-KEGG analysis revealed that triptolide may treat vitiligo through chemokine signaling pathway, body metabolic signaling pathway and NOD-like receptor signaling pathway. Conclusions Bioinformatics methods were used to discover important iron death evidence and related mechanisms in the pathogenesis of vitiligo, and this was used as an insertion point to screen triptolide as a potential therapeutic agent, which is of great significance to the study of vitiligo pathogenesis and treatment.

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国家自然科学基金地区科学基金资助项目（82160821）；新疆自治区重点研发计划项目（2022B03012-4）；“天山英才”培养计划项目（2022TSYCLJ009，2022TSYCCX0021）；国家中医药管理局青年岐黄学者培养项目（国中医药人教函[2022256]号）