Discovery and Validation of an Epithelial-Mesenchymal Transition-Based Signature in Gastric Cancer by Genomics and Prognosis Analysis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:34746312", "kind": "science-record", "revision": 4, "content_hash": "sha256:b723a5f85b46035eb7e8fc077aed0b213c39c0ff6ab2d5b1ae60a4a173140fcf", "created_at": "2026-11-01T17:36:26Z", "updated_at": "2026-11-01T22:05:06Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Discovery and Validation of an Epithelial-Mesenchymal Transition-Based Signature in Gastric Cancer by Genomics and Prognosis Analysis.", "doi": "10.1155/2021/9026918", "pmid": "34746312", "authors": ["Huiyong Xu", "Huilai Wan", "Maoshu Zhu", "Lianghua Feng", "Hui Zhang", "Fengbing Su"], "journal": "BioMed research international", "year": 2021, "abstract": "OBJECTIVE: Epithelial-mesenchymal transition (EMT) exerts a key function in cancer initiation and progression. Herein, we aimed to develop an EMT-based prognostic signature in gastric cancer.\nMETHODS: The gene expression profiles of gastric cancer were obtained from TCGA dataset as a training set and GSE66229 and GSE84437 datasets as validation sets. By LASSO regression and Cox regression analyses, key prognostic EMT-related genes were screened for developing a risk score (RS) model. Potential small molecular compounds were predicted by the CMap database based on the RS model. GSEA was employed to explore signaling pathways associated with the RS. ESTIMATE and seven algorithms (TIMER, CIBERSORT, CIBERSORT-ABS, QUANTISEQ, MCPCOUNTER, XCELL, and EPIC) were applied to assess the RS and immune microenvironment.\nRESULTS: This study developed an EMT-related gene signature comprised of SERPINE1, PCOLCE2, MATN3, and DKK1. High-RS patients displayed poorer survival outcomes than those with low RS. ROC curves demonstrated the robustness of the model in predicting the prognosis. After external validation, the RS model was an independent risk factor for gastric cancer. Several compounds were predicted for gastric cancer treatment based on the RS model. ECM receptor interaction, focal adhesion, pathway in cancer, TGF-beta, and WNT pathways were distinctly activated in high-RS samples. Also, high RS was significantly associated with increased stromal and immune scores and increased infiltration of CD4+ T cell, CD8+ T cell, cancer-associated fibroblast, and macrophage in gastric cancer tissues.\nCONCLUSION: Our findings suggested that the EMT-related gene model may robustly predict gastric cancer prognosis, which could improve the efficacy of personalized therapy.", "claims": [{"id": "claim-method", "statement": "Genes with p < 0.05 were included for least absolute shrinkage and selection operator (LASSO) Cox regression model analyses using the glmnet package [21].", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34746312", "locator": "sections[7] \"2.5. Generation and Verification of a Risk Score (RS) Model\" paragraph 2", "quote": "Genes with p < 0.05 were included for least absolute shrinkage and selection operator (LASSO) Cox regression model analyses using the glmnet package [21]."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data used to support the findings of this study are included within the supplementary information files.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34746312", "locator": "sections[28] \"Data Availability\" paragraph 2", "quote": "The data used to support the findings of this study are included within the supplementary information files."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The study used LASSO Cox regression and Cox regression analyses to develop an EMT-related risk score from TCGA gastric cancer expression data and validated it in the GSE66229 and GSE84437 datasets.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:34746312", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "The gene expression profiles of gastric cancer were obtained from TCGA dataset as a training set and GSE66229 and GSE84437 datasets as validation sets. By LASSO regression and Cox regression analyses, key prognostic EMT-related genes were screened for developing a risk score (RS) model."}, {"asset": "source:pmid:34746312", "locator": "sections[17] \"3.2. Generation of a Prognostic EMT-Related RS Model for Gastric Cancer\" paragraph 2", "quote": "As a result, we generated a 4-EMT-related gene model for gastric cancer (Figures 2(b) and 2(c))."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The study used LASSO Cox regression and Cox regression analyses to develop an EMT-related risk score from TCGA gastric cancer expression data and validated it in the GSE66229 and GSE84437 datasets.", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:34746312"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE66229"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE66229"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:34746312", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1155/2021/9026918", "sha256": 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