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

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35990996", "kind": "science-record", "revision": 4, "content_hash": "sha256:59c7a380dc1f4471a8b7565646e0d6f56830a46ee0ed256149ac4ecc88d9ecce", "created_at": "2026-11-01T18:15:08Z", "updated_at": "2026-11-01T22:07:19Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "EMT-Related Gene Signature Predicts the Prognosis in Uveal Melanoma Patients.", "doi": "10.1155/2022/5436988", "pmid": "35990996", "authors": ["Yufei Lv", "Lixian He", "Mengyi Jin", "Wenxin Sun", "Gang Tan", "Zuguo Liu"], "journal": "Journal of oncology", "year": 2022, "abstract": "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.\nMethods: 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.\nResults: 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.\nConclusion: 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.", "claims": [{"id": "claim-method", "statement": "Next, the selected genes were analyzed using LASSO Cox regression to further screen the candidate genes for model construction and to calculate the coefficient of the selected genes.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35990996", "locator": "sections[7] \"2.5. Construction of EMT Feature-Based Prognostic Model\" paragraph 2", "quote": "Next, the selected genes were analyzed using LASSO Cox regression to further screen the candidate genes for model construction and to calculate the coefficient of the selected genes."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The microarray data supporting this study are from previously reported databases, which have been cited. The processed data are available from the corresponding author upon request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35990996", "locator": "sections[18] \"Data Availability\" paragraph 2", "quote": "The microarray data supporting this study are from previously reported databases, which have been cited. The processed data are available from the corresponding author upon request."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The EMT feature-based prognostic model was constructed using LASSO Cox regression and showed strong prognostic performance in TCGA-UVM and two external validation cohorts.", "kind": "quantitative", "target": {"value": 0.958, "unit": "auc", "tolerance": null, "source": "Results indicated that patients in the high-EMT score group had a significantly poorer prognosis (Figures 3(d), 3(e), P=6.6e − 09, and Figure 3(f), AUC = 0.958)."}, "evidence": [{"asset": "source:pmid:35990996", "locator": "sections[12] \"3.3. Construction and Validation of the EMT Feature-Based Prognostic Model\" paragraph 2", "quote": "An EMT feature-based prognostic model (EMT score) was constructed using a machine-learning-based method."}, {"asset": "source:pmid:35990996", "locator": "sections[12] \"3.3. Construction and Validation of the EMT Feature-Based Prognostic Model\" paragraph 2", "quote": "Finally, LASSO Cox regression was used to identify the most robust prognostic genes among them and to calculate the coefficients of the selected genes (Figures 3(a) and 3(b))."}, {"asset": "source:pmid:35990996", "locator": "sections[12] \"3.3. Construction and Validation of the EMT Feature-Based Prognostic Model\" paragraph 6", "quote": "Results indicated that patients in the high-EMT score group had a significantly poorer prognosis (Figures 3(d), 3(e), P=6.6e − 09, and Figure 3(f), AUC = 0.958)."}, {"asset": "source:pmid:35990996", "locator": "sections[12] \"3.3. 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