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<p>Vasculogenic mimicry (VM), a novel endothelial-independent blood perfusion pathway, is linked to advanced stage and poor prognosis in esophageal squamous cell carcinoma (ESCC). In this study, by integrating single-cell RNA sequencing and transcriptomic data and employing a machine learning framework incorporating 117 algorithmic combinations, we constructed a robust 4-VM-related gene prognostic model for ESCC. Consensus clustering further stratified patients into two subtypes. The high-risk subtype (C2) was characterized by unfavorable prognosis, activated stroma, enrichment of M2 macrophages, and multidrug resistance. As the core regulatory hub of this model, SAP18 was markedly upregulated in ESCC tissues and showed positive correlations with aggressive clinicopathological features. Mechanistically, SAP18 binds to PIK3CB, activating the AKT/mTOR signaling cascade and upregulating HIF-1α, thereby conferring VM-forming capability to epithelial-derived tumor cells. Both in vitro and in vivo experiments confirmed that knockdown of SAP18 significantly suppressed malignant phenotypes in ESCC. Pharmacological intervention using the highly selective AKT inhibitor MK-2206 effectively abolished VM network formation and profoundly inhibited tumor growth. Our integrated multi-omics and functional analyses decipher the molecular architecture of VM in ESCC, nominating SAP18 as a precise prognostic biomarker and therapeutic target, and providing a foundation for individualized VM-targeted strategies in ESCC management.</p>

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SAP18 drives vasculogenic mimicry in esophageal squamous cell carcinoma: a machine learning and multi-omics investigation

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月1日

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  • Lei Wang,
  • Jingjing Ge,
  • Lanjie Wang,
  • Jiajia Du,
  • Bo You,
  • Tian Xia,
  • Yanru Qin,
  • Qingwen Zhu,
  • Ruyue Zhang

摘要

Vasculogenic mimicry (VM), a novel endothelial-independent blood perfusion pathway, is linked to advanced stage and poor prognosis in esophageal squamous cell carcinoma (ESCC). In this study, by integrating single-cell RNA sequencing and transcriptomic data and employing a machine learning framework incorporating 117 algorithmic combinations, we constructed a robust 4-VM-related gene prognostic model for ESCC. Consensus clustering further stratified patients into two subtypes. The high-risk subtype (C2) was characterized by unfavorable prognosis, activated stroma, enrichment of M2 macrophages, and multidrug resistance. As the core regulatory hub of this model, SAP18 was markedly upregulated in ESCC tissues and showed positive correlations with aggressive clinicopathological features. Mechanistically, SAP18 binds to PIK3CB, activating the AKT/mTOR signaling cascade and upregulating HIF-1α, thereby conferring VM-forming capability to epithelial-derived tumor cells. Both in vitro and in vivo experiments confirmed that knockdown of SAP18 significantly suppressed malignant phenotypes in ESCC. Pharmacological intervention using the highly selective AKT inhibitor MK-2206 effectively abolished VM network formation and profoundly inhibited tumor growth. Our integrated multi-omics and functional analyses decipher the molecular architecture of VM in ESCC, nominating SAP18 as a precise prognostic biomarker and therapeutic target, and providing a foundation for individualized VM-targeted strategies in ESCC management.