EMT-Related Gene Signature Predicts the Prognosis in Uveal Melanoma Patients

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Background: Uveal melanoma (UVM) is the most common primary intraocular malignancy in adults. Epithelial-mesenchymal transition (EMT) is an essential regulator of the UVM's immune microenvironment. However, the precise role of EMT in UVM remains to be explored and the development of a related treatment strategy is urgently needed. Methods: Multiomics data and clinical information for TCGA-UVM were used to identify the EMT subtypes and analyze their regulatory role in the immune microenvironment in UVM. A machine-learning method based on the identified subtypes was utilized to construct the EMT feature-based prognostic model. External validation cohorts GSE84976 and GSE22138 were employed to validate the model's robustness. Immunotherapy cohort IMvigor210 was used to explore the model's potential to predict immunotherapy responsiveness. Results: Two EMT subtypes were identified in UVM. The role of EMT in shaping the immune microenvironment and regulating cancer-immunity circle of UVM was analyzed. A robust prognostic model was presented and validated to predict patient prognosis. The model also predicted patient's immune features and immunotherapy responsiveness. Conclusion: The EMT-mediated immune features in UVM were illustrated, providing a reliable model to facilitate precise UVM treatment. This research may assist in decision-making during clinical UVM therapy.

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