bioRxivCancer BiologyPreprint
Single-cell profiling reveals MMP7-associated epithelial transition and immune-stromal remodeling in esophageal carcinogenesis
Qi WangiD, Peng Li, Li Fu, Yuewen ZhangiD, Wanli Zheng, Lin Li, Qinggang Hao, Wei LuoiD, Haiyan Guo, Mingyao MengiD, Ruhong Li, 侯宗柳, Yangfan GuoiD
Abstract
The progression from reflux esophagitis (RE) to Barretts esophagus (BE) and esophageal adenocarcinoma (EAC) is a major route of inflammation-associated esophageal tumorigenesis, but the cellular transitions involved remain incompletely understood. We performed single-cell transcriptomic profiling of RE, BE, and EAC tissues and integrated public EAC datasets, yielding 51,283 cells across the RE-BE-EAC continuum. The analysis identified stage-associated epithelial and microenvironmental differences. Pseudotime analysis placed Basal epithelial cells along two differentiation branches, one toward mature epithelial states and the other through proliferative and gastric-type metaplastic states toward copy number alteration-bearing malignant epithelial cells. MMP7 increased along this malignant branch, was enriched in cancer-associated epithelial cells. Its knockdown reduced the proliferation and migration of OE19 and OE33 cells, supporting an association with proliferative and migratory phenotypes. In parallel, the microenvironment shifted toward immune suppression and stromal remodeling, with increased T-cell exhaustion, macrophage polarization toward tumor-associated states, and enrichment of CAF-like myofibroblasts. Ligand-receptor analysis further suggested an epithelial-centered communication network involving MDK-SDC2/LRP1/NCL interactions between malignant epithelial cells and fibroblast, T-cell, and myeloid compartments. These results identify MMP7-associated epithelial states and candidate MDK-related communication pathways for further longitudinal and functional validation.
- organism: human
- assay: scRNA-seq
- research focus: cell type annotation
- research focus: trajectory inference
- research focus: cell-cell communication
- approach: deep learning
- Citations
- 0
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