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<p>The epithelial-to-mesenchymal transition (EMT) is a pivotal cellular process integral to metastasis. Emerging evidence indicates that EMT progresses through a continuum of cellular states, yet the intricate regulatory mechanisms underpinning this transition are not fully elucidated. This study aims to reconstruct the EMT spectrum in lung adenocarcinoma (LUAD) by integrating single-cell RNA sequencing data with advanced cell trajectory inference techniques. We identified five distinct EMT states across 16,310 malignant epithelial cells derived from 10 LUAD patients, encompassing epithelial, mesenchymal, and hybrid epithelial/mesenchymal (E/M) phenotypes. Notably, the hybrid E/M states exhibited the highest metastatic potential and were correlated with a poorer prognosis for patients. By modeling the EMT regulatory network, we evaluated the influence of transcription factor (TF) activation on network stability during cellular state transitions. We identified critical TFs, including TCF7, FOXA1, FOXA2, and SMAD7, which collectively regulate pathways such as TGF-β through both intrinsic EMT-related signaling and the modulation of tumor-associated macrophages. Furthermore, we developed a risk stratification model based on EMT continuum signatures, providing a novel tool for prognostic assessment. Our findings contribute to a comprehensive understanding of EMT-driven tumor evolution and open new avenues for prognostic stratification and targeted therapies in LUAD.</p>

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Construction of the cancer cell continuum reveals hybrid EMT state driving lung adenocarcinoma aggression

  • 期刊论文

  • 发表日期：

    2025年11月25日

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  • Qiuhao Qu,
  • Yixin Ma,
  • Chao Huang,
  • Botao Fa,
  • Zhenni Liu,
  • Yuhan Li,
  • Shigao Yang,
  • Zhengtao Xiao

摘要

The epithelial-to-mesenchymal transition (EMT) is a pivotal cellular process integral to metastasis. Emerging evidence indicates that EMT progresses through a continuum of cellular states, yet the intricate regulatory mechanisms underpinning this transition are not fully elucidated. This study aims to reconstruct the EMT spectrum in lung adenocarcinoma (LUAD) by integrating single-cell RNA sequencing data with advanced cell trajectory inference techniques. We identified five distinct EMT states across 16,310 malignant epithelial cells derived from 10 LUAD patients, encompassing epithelial, mesenchymal, and hybrid epithelial/mesenchymal (E/M) phenotypes. Notably, the hybrid E/M states exhibited the highest metastatic potential and were correlated with a poorer prognosis for patients. By modeling the EMT regulatory network, we evaluated the influence of transcription factor (TF) activation on network stability during cellular state transitions. We identified critical TFs, including TCF7, FOXA1, FOXA2, and SMAD7, which collectively regulate pathways such as TGF-β through both intrinsic EMT-related signaling and the modulation of tumor-associated macrophages. Furthermore, we developed a risk stratification model based on EMT continuum signatures, providing a novel tool for prognostic assessment. Our findings contribute to a comprehensive understanding of EMT-driven tumor evolution and open new avenues for prognostic stratification and targeted therapies in LUAD.