Systematic establishment and verification of an epithelial-mesenchymal transition gene signature for predicting prognosis of oral squamous cell carcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37636263", "kind": "science-record", "revision": 3, "content_hash": "sha256:0200e08c91e2eca4586bf10a01b6dde75a12ed55cc51f0f157966520a34a2ecf", "created_at": "2026-11-01T19:02:25Z", "updated_at": "2026-11-01T22:10:09Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Systematic establishment and verification of an epithelial-mesenchymal transition gene signature for predicting prognosis of oral squamous cell carcinoma.", "doi": "10.3389/fgene.2023.1113137", "pmid": "37636263", "authors": ["Jun Ai", "Yaqin Tan", "Bo Liu", "Yuhong Song", "Yanqin Wang", "Xin Xia", "Qicheng Fu"], "journal": "Frontiers in genetics", "year": 2023, "abstract": "Objective: Epithelial-mesenchymal transition (EMT) is linked to an unfavorable prognosis in oral squamous cell carcinoma (OSCC). Here, we aimed to develop an EMT gene signature for OSCC prognosis. Methods: In TCGA dataset, prognosis-related EMT genes with p < 0.05 were screened in OSCC. An EMT gene signature was then conducted with LASSO method. The efficacy of this signature in predicting prognosis was externally verified in the GSE41613 dataset. Correlations between this signature and stromal/immune scores and immune cell infiltration were assessed by ESTIMATE and CIBERSORT algorithms. GSEA was applied for exploring significant signaling pathways activated in high- and low-risk phenotypes. Expression of each gene was validated in 40 paired OSCC and normal tissues via RT-qPCR. Results: A prognostic 9-EMT gene signature was constructed in OSCC. High risk score predicted poorer clinical outcomes than low risk score. ROCs confirmed the well performance on predicting 1-, 3- and 5-year survival. Multivariate cox analysis revealed that this signature was independently predictive of OSCC prognosis. The well predictive efficacy was validated in the GSE41613 dataset. Furthermore, this signature was distinctly related to stromal/immune scores and immune cell infiltration in OSCC. Distinct pathways were activated in two subgroups. After validation, AREG, COL5A3, DKK1, GAS1, GPX7 and PLOD2 were distinctly upregulated and SFRP1 was downregulated in OSCC than normal tissues. Conclusion: Our data identified and verified a robust EMT gene signature in OSCC, which provided a novel clinical tool for predicting prognosis and several targets against OSCC therapy.", "claims": [{"id": "claim-method", "statement": "By applying glmnet package, an optimal prognostic model was built with least absolute shrinkage and selection operator (LASSO) Cox regression analysis based on the prognosis-related EMT genes (Friedman et al., 2010).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37636263", "locator": "sections[5] \"Establishment of an EMT gene model\" paragraph 2", "quote": "By applying glmnet package, an optimal prognostic model was built with least absolute shrinkage and selection operator (LASSO) Cox regression analysis based on the prognosis-related EMT genes (Friedman et al., 2010)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets presented in this study can be found in online repositories. 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