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<p>Atopic dermatitis (AD) is a long-term inflammatory skin disorder affecting individuals of all ages. The N7-methylguanosine (m7G) has an influential role in controlling gene expression and RNA molecules’ stability, which ultimately implicates it in several immune diseases. However, the association with AD has not been reported. Here, comprehensive bioinformatics analyses were utilized to investigate the significantly imbalanced mRNA expression patterns of m7G regulators in AD using the public database. To identify the diagnostic risk score, Lasso regression analysis was conducted to identify six m7G-related regulators. Machine learning techniques were used to identify prominent m7G risk score regulators, and identified NUDT7, EIF4E2, and AGO2 as significant contributors to the risk score. The three key genes were ultimately verified in the 2,4-dinitrochlorobenzene (DNCB) mouse model and AD skin samples. And we confirmed the down-regulated expression of NUDT7 and the up-regulated expression of EIF4E2 and AGO2. In vitro cell experiments showed that NUDT7, EIF4E2, and AGO2 regulated the production of pro-inflammatory factors. The molecular docking was conducted to find small molecules that could bind and target EIF4E2 and AGO2, which identified two amide compounds F030-0028 and K284-5656, as potential drugs for AD treatment. Taken together, NUDT7, EIF4E2, and AGO2 were related to the occurrence of AD, and our study may provide new insights into understanding m7G modification in the pathophysiology of AD.</p>

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N7-methylguanosine regulator-mediated RNA methylation modification patterns involved in atopic dermatitis

  • 期刊论文

  • 发表日期：

    2025年7月10日

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  • Maoxin Huang,
  • Liping Dong,
  • Tingyue Deng,
  • Yun Lu,
  • Xinying Cai,
  • Fengli Xiao

摘要

Atopic dermatitis (AD) is a long-term inflammatory skin disorder affecting individuals of all ages. The N7-methylguanosine (m7G) has an influential role in controlling gene expression and RNA molecules’ stability, which ultimately implicates it in several immune diseases. However, the association with AD has not been reported. Here, comprehensive bioinformatics analyses were utilized to investigate the significantly imbalanced mRNA expression patterns of m7G regulators in AD using the public database. To identify the diagnostic risk score, Lasso regression analysis was conducted to identify six m7G-related regulators. Machine learning techniques were used to identify prominent m7G risk score regulators, and identified NUDT7, EIF4E2, and AGO2 as significant contributors to the risk score. The three key genes were ultimately verified in the 2,4-dinitrochlorobenzene (DNCB) mouse model and AD skin samples. And we confirmed the down-regulated expression of NUDT7 and the up-regulated expression of EIF4E2 and AGO2. In vitro cell experiments showed that NUDT7, EIF4E2, and AGO2 regulated the production of pro-inflammatory factors. The molecular docking was conducted to find small molecules that could bind and target EIF4E2 and AGO2, which identified two amide compounds F030-0028 and K284-5656, as potential drugs for AD treatment. Taken together, NUDT7, EIF4E2, and AGO2 were related to the occurrence of AD, and our study may provide new insights into understanding m7G modification in the pathophysiology of AD.