Abstract
The stromal microenvironment has been shown to affect the infiltration of esophageal carcinoma (ESCA), which is linked to prognosis. However, the complicated mechanism of how infiltration is influenced by the stromal microenvironment is not well-defined. In this study, a stromal activation classifier was established with ridge cox regression to calculate stroma scores for training (n = 182) and validation cohorts (n = 227) based on the stroma-related 32 hub genes identified by sequential bioinformatics algorithms. Patients with high stromal activation were associated with high T stage and poor prognosis in both esophagus adenocarcinoma and esophagus squamous cell carcinoma. Besides, comprehensive multi-omics analysis was used to outline stromal characterizations of 2 distinct stromal groups. Patients with activated tumor stoma showed high stromal cell infiltration (fibroblasts, endothelial cells, and monocyte macrophages), epithelial-mesenchymal transition, tumor angiogenesis and M2 macrophage polarization (CD163 and CD206). Tumor mutation burden of differential stromal groups was also depicted. In addition, a total of 6 stromal activation markers in ESCA were defined and involved in the function of carcinoma-associated fibroblasts that were crucial in the differentiation of distinct stromal characterizations. Based on these studies, a practical classifier for the stromal microenvironment was successfully proposed to predict the prognosis of ESCA patients.