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

## TL;DR

This study explores how epithelial-mesenchymal transition (EMT) drives cancer progression in esophageal squamous cell carcinoma and proposes new diagnostic and therapeutic strategies.

## Contribution

The study identifies a high-risk EMT subtype in ESCC and proposes a deep learning model for predicting EMT progression using pathological images.

## Key findings

- EMT progression in ESCC includes three macrostates with distinct biological characteristics.
- Suppression of CACNA1C can reverse malignant cell states to normal epithelium-like cells.
- A deep learning model based on pathological images can predict CACNA1C spatial expression.

## Abstract

Rationale: 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.

Methods: 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 CACNA1C in programming malignant phenotype was tested in vitro. A pathological image-based deep learning model successfully predicted the spatial expression distribution of CACNA1C, indicating possible clinical utility.

Results: 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). ITGA3 and ITGB4 antagonistically regulate malignant phenotype in the early state. Notably, suppression of CACNA1C induced transdifferentiation from stable/late-state cells to normal epithelium-like cells.

Conclusions: 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.