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<p>Vitiligo is a complex autoimmune skin disorder characterized by depigmentation and immune dysregulation. To elucidate the role of ferroptosis-related genes (FRGs) in vitiligo, we conducted a comprehensive analysis of gene expression data from the GSE53146 and GSE65127 datasets obtained from the GEO database. We identified 31 differentially expressed FRGs (DE-FRGs), with 21 genes upregulated and 10 downregulated. Functional enrichment analysis revealed that these DE-FRGs are significantly involved in oxidative stress, immune regulation, and vitiligo-associated signaling pathways. Utilizing machine learning approaches, including LASSO and SVM-RFE, we identified four key marker genes (ALOX5, SNCA, SLC1A4, and IL33) with strong diagnostic potential. Immune landscape analysis demonstrated that these marker genes influence immune cell composition, particularly showing correlations with CD8 + T cells and regulatory T cells. Furthermore, drug-gene interaction analysis proposed potential therapeutic targets, while ceRNA network analysis uncovered intricate regulatory relationships involving miRNAs and lncRNAs. Collectively, our findings provide novel insights into the molecular mechanisms underpinning vitiligo and suggest new avenues for diagnostic and therapeutic development.</p>

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Bioinformatic analysis of ferroptosis related biomarkers and potential therapeutic targets in vitiligo

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年1月15日

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  • Linli Liu,
  • Lingli Deng,
  • Li Guan,
  • Yuan Hu,
  • Qianying Li,
  • Chunshui Yu

摘要

Vitiligo is a complex autoimmune skin disorder characterized by depigmentation and immune dysregulation. To elucidate the role of ferroptosis-related genes (FRGs) in vitiligo, we conducted a comprehensive analysis of gene expression data from the GSE53146 and GSE65127 datasets obtained from the GEO database. We identified 31 differentially expressed FRGs (DE-FRGs), with 21 genes upregulated and 10 downregulated. Functional enrichment analysis revealed that these DE-FRGs are significantly involved in oxidative stress, immune regulation, and vitiligo-associated signaling pathways. Utilizing machine learning approaches, including LASSO and SVM-RFE, we identified four key marker genes (ALOX5, SNCA, SLC1A4, and IL33) with strong diagnostic potential. Immune landscape analysis demonstrated that these marker genes influence immune cell composition, particularly showing correlations with CD8 + T cells and regulatory T cells. Furthermore, drug-gene interaction analysis proposed potential therapeutic targets, while ceRNA network analysis uncovered intricate regulatory relationships involving miRNAs and lncRNAs. Collectively, our findings provide novel insights into the molecular mechanisms underpinning vitiligo and suggest new avenues for diagnostic and therapeutic development.