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<p>Psoriasis is a common chronic skin disorder with a polygenic background.It is widely acknowledged that Th17/IL-17A axis plays a key role in the pathogenesisof psoriasis. However, numerous regulatory genes upstream of the pathway remainundiscovered, creating a knowledge gap in our understanding of the genetic aspectsof the Th17/IL-17A axis. In this study, we employed machine learning algorithms toidentify three target genes associated with psoriasis: CCR7, IL2RG, and PLEK. Thevalidation of these genes was carried out in specimens from psoriatic patients. Invivo, investigations assessed the relationship between these three genes andIL-17A-related inflammation and their connection to psoriatic phenotypes. To furtherconfirm the significance of the newly discovered gene, PLEK, we performedexperiments involving the blockade of its expression. Our bioinformatics analysisrevealed three novel genes closely linked to psoriasis: CCR7, IL2RG, and PLEK. Thesegenes exhibited upregulated expression in psoriasis, consistently aligning with theTh17/IL-17A axis. Inhibition of PLEK expression reduced Th17/IL-17A-relatedinflammation and alleviated psoriatic phenotypes. CCR7, IL2RG, and PLEK showpotential as three novel biomarkers for psoriasis, with PLEK being reported for thefirst time in this context. These genes contribute to pathogenesis by associatingwith the Th17/IL-17A signaling pathway.</p>

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RETRACTED ARTICLE: Identification of novel IL17-related genes as prognostic and therapeutic biomarkers of psoriasis using comprehensive bioinformatics analysis and machine learning

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年4月2日

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  • Xingling He,
  • Jingjing Huang,
  • Hanying Ma,
  • Zhujun Ma,
  • Changzheng Huang,
  • Yunli Ling,
  • Bin Zhou,
  • Jingang Li

摘要

Psoriasis is a common chronic skin disorder with a polygenic background.It is widely acknowledged that Th17/IL-17A axis plays a key role in the pathogenesisof psoriasis. However, numerous regulatory genes upstream of the pathway remainundiscovered, creating a knowledge gap in our understanding of the genetic aspectsof the Th17/IL-17A axis. In this study, we employed machine learning algorithms toidentify three target genes associated with psoriasis: CCR7, IL2RG, and PLEK. Thevalidation of these genes was carried out in specimens from psoriatic patients. Invivo, investigations assessed the relationship between these three genes andIL-17A-related inflammation and their connection to psoriatic phenotypes. To furtherconfirm the significance of the newly discovered gene, PLEK, we performedexperiments involving the blockade of its expression. Our bioinformatics analysisrevealed three novel genes closely linked to psoriasis: CCR7, IL2RG, and PLEK. Thesegenes exhibited upregulated expression in psoriasis, consistently aligning with theTh17/IL-17A axis. Inhibition of PLEK expression reduced Th17/IL-17A-relatedinflammation and alleviated psoriatic phenotypes. CCR7, IL2RG, and PLEK showpotential as three novel biomarkers for psoriasis, with PLEK being reported for thefirst time in this context. These genes contribute to pathogenesis by associatingwith the Th17/IL-17A signaling pathway.