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<p>Gastroesophageal reflux disease (GERD) is one of the most frequently diagnosed gastrointestinal disorders, adversely affecting quality of life. <i>Coriandrum sativum</i> has been shown to effectively promote gastrointestinal motility, alleviate gastric discomfort, and positively impact esophageal health, but its mechanism of action remains unclear. This study utilized the TCMSP database to retrieve the components of coriander and the GEO database to identify NERD-related genes. Weighted Gene Co-expression Network Analysis (WGCNA) and machine learning were employed to identify candidate genes, followed by Protein-Protein Interaction (PPI) network analysis and external dataset validation to identify key candidate genes. These genes were further validated through Gene Set Enrichment Analysis (GSEA) and subcellular localization. Finally, molecular docking and molecular dynamics (MD) simulations identified Ammidin, campest-5-en-3beta-ol, Isofucosterol and beta-sitosterol as the key components in <i>Coriandrum sativum</i> for improving NERD. Future research should prioritize experimental validation of these compounds and further investigate potential resistance mechanisms to enhance their therapeutic efficacy and practical applications.</p>

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Exploration of the potential therapeutic effects and targets of Coriandrum sativum on non-erosive esophagitis based on bioinformatics and molecular dynamics simulation

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年5月31日

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  • Xiaopeng Shen,
  • Bin Li,
  • Yuan Chen,
  • Yixin Wang,
  • Weijun Zhou,
  • Mengyu Deng,
  • Yuling Xu

摘要

Gastroesophageal reflux disease (GERD) is one of the most frequently diagnosed gastrointestinal disorders, adversely affecting quality of life. Coriandrum sativum has been shown to effectively promote gastrointestinal motility, alleviate gastric discomfort, and positively impact esophageal health, but its mechanism of action remains unclear. This study utilized the TCMSP database to retrieve the components of coriander and the GEO database to identify NERD-related genes. Weighted Gene Co-expression Network Analysis (WGCNA) and machine learning were employed to identify candidate genes, followed by Protein-Protein Interaction (PPI) network analysis and external dataset validation to identify key candidate genes. These genes were further validated through Gene Set Enrichment Analysis (GSEA) and subcellular localization. Finally, molecular docking and molecular dynamics (MD) simulations identified Ammidin, campest-5-en-3beta-ol, Isofucosterol and beta-sitosterol as the key components in Coriandrum sativum for improving NERD. Future research should prioritize experimental validation of these compounds and further investigate potential resistance mechanisms to enhance their therapeutic efficacy and practical applications.