Extracellular Matrix-Associated Pathways Promote the Progression of Gastric Cancer by Impacting the Dendritic Cell Axis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:34675633", "kind": "science-record", "revision": 4, "content_hash": "sha256:276acec5491f2ec3e571a409a92ae6e5ff3e4b8526e8948d5c514f99d1402d91", "created_at": "2026-11-01T17:36:20Z", "updated_at": "2026-11-01T22:05:00Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Extracellular Matrix-Associated Pathways Promote the Progression of Gastric Cancer by Impacting the Dendritic Cell Axis.", "doi": "10.2147/IJGM.S334245", "pmid": "34675633", "authors": ["Zhenlin Wang", "Zunyun Wang", "Xianyu Hu", "Qijun Han", "Ke Chen", "Gang Pang"], "journal": "International journal of general medicine", "year": 2021, "abstract": "BACKGROUND: Gastric cancer (GC) is the third most frequent malignant tumour in the Chinese population, let alone the whole world. Recently, most prognostic models have only focused on the levels of several genes, miRNAs, lncRNAs, gene mutations, or DNA methylation; however, the activation status of biological pathways is more stable and can reflect the comprehensive inner conditions of tumours.\nMETHODS: We collected samples from the Cancer Genome Atlas Stomach Adenocarcinoma (TCGA-STAD) cohort and GSE62254 cohort, with a total of 594 patients. We employed GSEA to first compare the diverse activated signalling pathways between dead GC patients and living patients. The least absolute shrinkage and selection operator (LASSO) regression analysis was subsequently performed by the \"glmnet\" package to generate a prognostic signature.\nRESULTS: We extracted a total of 218 genes from the KEGG Focal Adhesion and KEGG ECM Receptor Interaction pathways, which showed significant activation in dead GC patients in two enrolled cohorts, for subsequent LASSO analysis. In the TCGA-STAD cohort, patients in the high-risk group faced a significantly poorer prognosis than those in the low-risk group (P < 0.001, HR: 4.62, 95% CI: 3.447-6.183), with an AUC of 0.694. In the GSE62254 cohort, the HR value was 4.94 (95% CI: 3.413-7.165), and the AUC value was as high as 0.834. A high-risk score and poor prognosis correlated with infiltrated dendritic cells, and the receptor of IFN-α was also positively linked with the risk score, as well as poor prognosis. GC patients with high-risk scores were more likely to respond to CTLA4 treatment but not PD1 treatment.\nCONCLUSION: Taken together, we established and verified an extracellular matrix prognostic model of gastric cancer patients. The model can be used to evaluate the risk of death of GC patients, as well as the response to anti-CTLA4 immunotherapy.", "claims": [{"id": "claim-method", "statement": "We employed GSEA to first compare the diverse activated signalling pathways between dead GC patients and living patients in the TCGA-STAD cohort and GSE62254 cohort.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34675633", "locator": "sections[4] \"GSEA\" paragraph 2", "quote": "We employed GSEA to first compare the diverse activated signalling pathways between dead GC patients and living patients in the TCGA-STAD cohort and GSE62254 cohort."}], "state": "complete"}, {"id": "claim-data-access", "statement": "All data used in this work can be acquired from the GDC portal (https://portal.gdc.cancer.gov/), Gene-Expression Omnibus (GEO; https://www.ncbi.nlm.nih.gov/geo/).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34675633", "locator": "sections[20] \"Data Sharing Statement\" paragraph 2", "quote": "All data used in this work can be acquired from the GDC portal (https://portal.gdc.cancer.gov/), Gene-Expression Omnibus (GEO; https://www.ncbi.nlm.nih.gov/geo/)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The study used GSEA and LASSO regression on TCGA-STAD and GSE62254 data to establish and verify an extracellular matrix prognostic model for gastric cancer.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:34675633", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "We collected samples from the Cancer Genome Atlas Stomach Adenocarcinoma (TCGA-STAD) cohort and GSE62254 cohort, with a total of 594 patients."}, {"asset": "source:pmid:34675633", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "We employed GSEA to first compare the diverse activated signalling pathways between dead GC patients and living patients."}, {"asset": "source:pmid:34675633", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "The least absolute shrinkage and selection operator (LASSO) regression analysis was subsequently performed by the “glmnet” package to generate a prognostic signature."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The study used GSEA and LASSO regression on TCGA-STAD and GSE62254 data to establish and verify an extracellular matrix prognostic model for gastric cancer.", "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:34675633"], "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. 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