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Updated: Feb 28, 2026

Induction of Mesenchymal-Epithelial Transitions in Sarcoma Cells
Published on: April 7, 2017
Core Epithelial-to-Mesenchymal Transition Gene Signature Predicts Metastasis and Poor Survival in Synovial Sarcoma
Megan Jagosky1, Colin Anderson2, Nury Steuerwald3
1Department of Solid Tumor Oncology, Atrium Health Levine Cancer Institute, Charlotte, NC, USA.
Background:
Synovial sarcoma (SS) is a rare soft tissue malignancy comprising 5%-10% of all sarcomas, typically affecting young adults. It is characterized by a pathognomonic SS18-SSX gene fusion, with variable histologic subtypes demonstrating epithelial and mesenchymal features. This study investigates the association between epithelial-to-mesenchymal transition (EMT) gene expression and metastatic potential in SS.
Methods:
A retrospective, single-institution analysis was conducted using primary tumor specimens from 21 treatment-naïve SS patients. RNA sequencing was performed on formalin-fixed paraffin-embedded (FFPE) tissue to assess gene expression profiles. EMT scores were calculated using three independent methods: Hallmark EMT gene set from GSEA, the 76-gene EMT signature (76GS), and Kolmogorov-Smirnov (KS) testing. Patients were categorized into metastatic and nonmetastatic cohorts, and phenotype-specific survival analyses were conducted.
Results:
Tumors from patients who developed metastases showed significant enrichment of EMT-related genes (GSEA NES 1.71, P = 0.025), oxidative phosphorylation, and immune-related pathways. EMT scores were consistently higher (more mesenchymal) in metastatic tumors across all scoring methods. Mesenchymal phenotype was associated with significantly worse overall survival (GSEA log-rank P = 0.0009).
Conclusions:
This study demonstrates a correlation between mesenchymal gene expression signatures and increased metastatic risk in SS. EMT status, derived from primary tumor profiling at diagnosis, may serve as a potential prognostic biomarker. These findings support further investigation into EMT as a stratification tool for tailoring treatment intensity and surveillance strategies in SS.
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