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<p>Endometrial cancer (EC) is a leading cause of gynecological malignancy with poor prognosis in advanced stages. This study aimed to identify key eosinophil extracellular trap (EET) regulators involved in EC progression and explore their prognostic value using machine learning-based models. Through differential expression analysis, we identified 108 EET regulators whose expression was significantly altered in tumor tissues compared to normal tissues. Survival analysis further demonstrated that <i>S100A9</i> and other EET-related genes, such as CCL26 and CD40, were significantly associated with poor patient outcomes. Unsupervised clustering analysis revealed two distinct molecular subtypes of EC, with Cluster A showing upregulation of most EET regulators and worse clinical outcomes. We assessed immune infiltration profiles and found elevated eosinophil infiltration in Cluster A. Machine learning models incorporating <i>S100A9</i> achieved superior predictive performance, with the Lasso + RSF model demonstrating robust accuracy (C-index = 0.864) for predicting patient survival. Experimental validation of <i>S100A9</i> function in endometrial cancer cell lines demonstrated that <i>S100A9</i> knockdown effectively reduced its expression, leading to the disruption of the NF-κB pathway, as confirmed by Western blot analysis. Further, <i>S100A9</i> overexpression in Ishikawa and KLE cells resulted in increased levels of apoptotic markers, indicating its role in apoptosis regulation. These findings suggest <i>S100A9</i> as a potential prognostic biomarker for EC, influencing immune infiltration, NF-κB signaling, and tumor progression, with implications for new therapeutic strategies targeting EET-related pathways.</p>

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S100A9 regulates eosinophil extracellular trap and activates NF-κB signaling in endometrial cancer: a machine learning-based biomarker discovery

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月21日

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  • Juanjuan Ni,
  • Yanan Mao,
  • Qiaofeng Ma,
  • Ziyao Wu,
  • Min Su

摘要

Endometrial cancer (EC) is a leading cause of gynecological malignancy with poor prognosis in advanced stages. This study aimed to identify key eosinophil extracellular trap (EET) regulators involved in EC progression and explore their prognostic value using machine learning-based models. Through differential expression analysis, we identified 108 EET regulators whose expression was significantly altered in tumor tissues compared to normal tissues. Survival analysis further demonstrated that S100A9 and other EET-related genes, such as CCL26 and CD40, were significantly associated with poor patient outcomes. Unsupervised clustering analysis revealed two distinct molecular subtypes of EC, with Cluster A showing upregulation of most EET regulators and worse clinical outcomes. We assessed immune infiltration profiles and found elevated eosinophil infiltration in Cluster A. Machine learning models incorporating S100A9 achieved superior predictive performance, with the Lasso + RSF model demonstrating robust accuracy (C-index = 0.864) for predicting patient survival. Experimental validation of S100A9 function in endometrial cancer cell lines demonstrated that S100A9 knockdown effectively reduced its expression, leading to the disruption of the NF-κB pathway, as confirmed by Western blot analysis. Further, S100A9 overexpression in Ishikawa and KLE cells resulted in increased levels of apoptotic markers, indicating its role in apoptosis regulation. These findings suggest S100A9 as a potential prognostic biomarker for EC, influencing immune infiltration, NF-κB signaling, and tumor progression, with implications for new therapeutic strategies targeting EET-related pathways.