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Background <p>Esophageal adenocarcinoma (EAC) is a highly aggressive malignancy with poor prognosis, often evolving from Barrett’s esophagus (BE). Understanding the molecular mechanisms driving this progression is critical for identifying diagnostic biomarkers and therapeutic targets.</p> Methods <p>We integrated single-cell RNA sequencing and bulk transcriptomic datasets to investigate fibroblast heterogeneity and epithelial–stromal crosstalk across disease stages. A disease-associated fibroblast signature (DAFS) was established and applied for consensus clustering to define EAC molecular subtypes. HSPH1, the core prognostic gene, was identified using multiple machine learning algorithms and further analyzed for pathway enrichment, immune landscape, and drug sensitivity. Its expression and function were subsequently validated by immunohistochemistry and in vitro assays.</p> Results <p>Distinct fibroblast subpopulations were identified in BE and EAC, with specific clusters exhibiting enhanced interactions with epithelial cells. Ligand–receptor analysis revealed key signaling pathways, including COL1A1–SDC1, MDK–NCL, and ITGA2–ITGB1, potentially contributing to malignant transition. Molecular subtyping revealed two EAC clusters with distinct immune infiltration. Among the DAFS genes, HSPH1 was identified as a key prognostic marker associated with higher tumor mutation burden, reduced immune infiltration, and increased tumor purity. Functional assays confirmed that HSPH1 promotes EAC cell proliferation, migration, and invasion. A progressive increase in HSPH1 expression from normal tissue to BE to EAC supports its potential utility as a biomarker for malignant progression.</p> Conclusion <p>Our study reveals critical fibroblast–epithelial interactions in NC-BE-EAC progression and identifies HSPH1 as a potential biomarker and therapeutic target. These findings may facilitate risk stratification and personalized treatment strategies for EAC.</p>

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Single-cell and bulk omics uncover fibroblast heterogeneity and HSPH1 as a key driver in Barrett’s esophagus to esophageal adenocarcinoma progression

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月10日

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  • Lulong Tao,
  • Yanting Guo,
  • Fang Lu,
  • Changyong Qi,
  • Guoxin Zhang,
  • Jin Yan

摘要

Background

Esophageal adenocarcinoma (EAC) is a highly aggressive malignancy with poor prognosis, often evolving from Barrett’s esophagus (BE). Understanding the molecular mechanisms driving this progression is critical for identifying diagnostic biomarkers and therapeutic targets.

Methods

We integrated single-cell RNA sequencing and bulk transcriptomic datasets to investigate fibroblast heterogeneity and epithelial–stromal crosstalk across disease stages. A disease-associated fibroblast signature (DAFS) was established and applied for consensus clustering to define EAC molecular subtypes. HSPH1, the core prognostic gene, was identified using multiple machine learning algorithms and further analyzed for pathway enrichment, immune landscape, and drug sensitivity. Its expression and function were subsequently validated by immunohistochemistry and in vitro assays.

Results

Distinct fibroblast subpopulations were identified in BE and EAC, with specific clusters exhibiting enhanced interactions with epithelial cells. Ligand–receptor analysis revealed key signaling pathways, including COL1A1–SDC1, MDK–NCL, and ITGA2–ITGB1, potentially contributing to malignant transition. Molecular subtyping revealed two EAC clusters with distinct immune infiltration. Among the DAFS genes, HSPH1 was identified as a key prognostic marker associated with higher tumor mutation burden, reduced immune infiltration, and increased tumor purity. Functional assays confirmed that HSPH1 promotes EAC cell proliferation, migration, and invasion. A progressive increase in HSPH1 expression from normal tissue to BE to EAC supports its potential utility as a biomarker for malignant progression.

Conclusion

Our study reveals critical fibroblast–epithelial interactions in NC-BE-EAC progression and identifies HSPH1 as a potential biomarker and therapeutic target. These findings may facilitate risk stratification and personalized treatment strategies for EAC.