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<p>Endometrial cancer (EC) presents a major global health challenge due to its heterogeneity and complex pathophysiology. The tumor microenvironment (TME), comprising stromal cells, immune cells, endothelial cells, and non-cellular components, critically influences EC progression. Single-cell RNA sequencing (scRNA-seq) has revolutionized our understanding of EC by revealing TME cellular heterogeneity and interactions. This review synthesizes recent scRNA-seq applications in EC, focusing on two key areas: ① Mapping transcriptional heterogeneity across major cellular components (epithelial, stromal, endothelial, and immune cells); ② Elucidating the factors driving tumor evolution, immune evasion, and differential immunotherapy response. Collectively, these scRNA-seq-derived insights into the dynamic TME ecosystem provide the foundation for translating basic discoveries toward clinical applications. Such advances could thereby promote TME-based personalized therapies and more accurate prognostic predictions.</p>

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Applications of single-cell transcriptomics: updated insights in endometrial cancer

  • 期刊论文

  • 发表日期：

    2025年9月3日

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  • Shuyue Xiao,
  • Huixin Li,
  • Jianyao Liu,
  • Xinyi Xie,
  • Hanzi Xu,
  • Zhen Gong,
  • Shanliang Zhong

摘要

Endometrial cancer (EC) presents a major global health challenge due to its heterogeneity and complex pathophysiology. The tumor microenvironment (TME), comprising stromal cells, immune cells, endothelial cells, and non-cellular components, critically influences EC progression. Single-cell RNA sequencing (scRNA-seq) has revolutionized our understanding of EC by revealing TME cellular heterogeneity and interactions. This review synthesizes recent scRNA-seq applications in EC, focusing on two key areas: ① Mapping transcriptional heterogeneity across major cellular components (epithelial, stromal, endothelial, and immune cells); ② Elucidating the factors driving tumor evolution, immune evasion, and differential immunotherapy response. Collectively, these scRNA-seq-derived insights into the dynamic TME ecosystem provide the foundation for translating basic discoveries toward clinical applications. Such advances could thereby promote TME-based personalized therapies and more accurate prognostic predictions.