目的 通過生物信息學方法分析白癜風疾病中鐵死亡基因及相關(guān)發(fā)病機制,并篩選通過鐵死亡相關(guān)途徑治療白癜風的潛在藥物。方法 從FerrDB數(shù)據(jù)庫獲取鐵死亡基因,通過R分析數(shù)據(jù)集GSE53146中差異表達基因,隨后二者取交集。通過SVM-REF算法和LASSO回歸構(gòu)建機器學習模型預(yù)測白癜風鐵死亡關(guān)鍵基因并通過數(shù)據(jù)集GSE75819進行基因表達驗證。GSE203262單細胞數(shù)據(jù)進行細胞聚類分析,發(fā)現(xiàn)與關(guān)鍵基因高度相關(guān)且參與白癜風發(fā)病的關(guān)鍵細胞群,隨后通過HPA數(shù)據(jù)庫對基因表達細胞進行驗證。利用cAMP數(shù)據(jù)庫篩選關(guān)鍵基因相關(guān)小分子藥物,利用分子對接技術(shù)驗證小分子化合物與基因結(jié)合的能力。最后進行單基因免疫細胞相關(guān)性分析及GSEA-KEGG分析探討小分子藥物治療白癜風的相關(guān)免疫機制。結(jié)果 獲得458個鐵死亡基因和706個差異表達基因,二者交集基因23個。機器學習預(yù)測模型篩選出RRM2、LCN2、OTUB1、SNCA、CTSB、WWTR1作為關(guān)鍵基因。外部數(shù)據(jù)集驗證、單細胞聚類和HPA數(shù)據(jù)均提示關(guān)鍵基因中OTUB1、CTSB和LCN2主要在角質(zhì)形成細胞、黑素細胞和朗格漢斯細胞等重要皮膚細胞中表達。通過高通量篩選和分子對接驗證,獲得雷公藤甲素作為通過鐵死亡途徑治療白癜風的小分子藥物。免疫細胞相關(guān)性分析發(fā)現(xiàn)雷公藤甲素通過影響關(guān)鍵基因調(diào)控自然殺傷細胞、活化的CD8⁺ T細胞等免疫細胞的功能。GSEA-KEGG分析發(fā)現(xiàn)雷公藤甲素可能通過趨化因子信號通路、機體代謝信號通路和NOD樣受體信號通路產(chǎn)生治療白癜風的作用。結(jié)論 利用生物信息學方法發(fā)現(xiàn)在白癜風發(fā)病中重要的鐵死亡證據(jù)及相關(guān)機制,并以此為插入點篩選到雷公藤甲素作為潛在治療藥物,對白癜風發(fā)病及治療研究具有重要意義。

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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國家自然科學基金地區(qū)科學基金資助項目（82160821）；新疆自治區(qū)重點研發(fā)計劃項目（2022B03012-4）；“天山英才”培養(yǎng)計劃項目（2022TSYCLJ009，2022TSYCCX0021）；國家中醫(yī)藥管理局青年岐黃學者培養(yǎng)項目（國中醫(yī)藥人教函[2022256]號）