Pathogenomic analysis reveals clinically relevant epithelial-mesenchymal plasticity in esophageal squamous cell carcinoma

<b>Rationale</b>: Esophageal squamous cell carcinoma (ESCC) is a highly aggressive malignancy. The metastasis and poor prognosis of ESCC are closely associated with tumor microenvironment (TME) heterogeneity, which is driven by epithelial-mesenchymal transition (EMT). Clinically, how to diagnose and target EMT progression remains a key challenge for ESCC. <b>Methods</b>: Integration of pathological images and bulk RNA sequencing profiles identified a high-risk subtype exhibiting EMT enrichment and immunosuppression. Single-cell and spatial transcriptomics revealed EMT macrostates and their spatial distribution. The role of <i>CACNA1C</i> in programming malignant phenotype was tested <i>in vitro</i>. A pathological image-based deep learning model successfully predicted the spatial expression distribution of <i>CACNA1C</i>, indicating possible clinical utility. <b>Results</b>: EMT progression comprised three macrostates: the early state (high epithelial and metastatic potential), the stable state (hybrid E/M phenotype and high stemness), and the late state (high mesenchymal and invasive propensity). <i>ITGA3</i> and <i>ITGB4</i> antagonistically regulate malignant phenotype in the early state. Notably, suppression of <i>CACNA1C</i> induced transdifferentiation from stable/late-state cells to normal epithelium-like cells. <b>Conclusions</b>: This study provides novel insights into the EMT mechanism in ESCC, proposes an intervention strategy, and emphasizes the promising clinical application of pathological images in EMT assessment.