Identification of a prognostic 5-Gene expression signature for gastric cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:28035468", "kind": "science-record", "revision": 3, "content_hash": "sha256:14fa95729c5740598102e126233d0eff93695064e2153b1b471a6f1049aa3b82", "created_at": "2026-11-01T15:40:22Z", "updated_at": "2026-11-01T21:20:45Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of a prognostic 5-Gene expression signature for gastric cancer.", "doi": "10.1007/s00432-016-2324-z", "pmid": "28035468", "authors": ["Jun-Yi Hou", "Yu-Gang Wang", "Shi-Jie Ma", "Bing-Yin Yang", "Qian-Ping Li"], "journal": "Journal of cancer research and clinical oncology", "year": 2017, "abstract": "PURPOSE: Gastric cancer (GC) is a major tumor throughout the world with remaining high morbidity and mortality. The aim is to generate a gene model to assess the prognoses risk of patients with GC.\nMETHODS: Gene expression profiling of gastric cancer patients, GSE62254 (300 samples) and GSE26253 (432 samples), was downloaded from Gene Expression Omnibus (GEO) database. Univariate survival analysis and LASSO (Least Absolute Shrinkage and Selectionator operator) (1000 iterations) of differentially expressed genes in GSE62254 was assessed using survival and glmnet in R package, respectively. Kaplan-Meier analysis on the clustering algorithm from each regression model was performed to calculate the influence to the prognosis. Random samples in GSE26253 were analyzed in multivariate and univariate survival analysis for one thousand times to calculate statistical stability of each regression model.\nRESULTS: A total of 854 Genes were identified differentially expressed in GSE62254, among which 367 Genes were found influencing the prognoses. Six gene clusters were selected with good stability. Hereinto, five or more genes in 11-Gene model, TRPC1, SGCE, TNFRSF11A, LRRN1, HLF, CYS1, PPP1R14A, NOV, NBEA, CES1 and RGN, was available to evaluate the prognostic risk of GC patients in GSE26253 (P = 0.00445). The validity and reliability was validated.\nCONCLUSION: In conclusion, we successfully generated a stable 5-Gene model, which could be utilized to predict prognosis of GC patients and would contribute to postoperational treatment and follow-up strategies.", "claims": [{"id": "claim-target-result", "statement": "Using GEO gene-expression profiles from GSE62254 and GSE26253, with univariate survival analysis and LASSO used for model development, the study generated a stable 5-gene model to predict gastric cancer prognosis.", "kind": "quantitative", "target": {"value": 0.00445, "unit": "p-value", "tolerance": null, "source": "Hereinto, five or more genes in 11-Gene model, TRPC1, SGCE, TNFRSF11A, LRRN1, HLF, CYS1, PPP1R14A, NOV, NBEA, CES1 and RGN, was available to evaluate the prognostic risk of GC patients in GSE26253 (P = 0.00445)."}, "evidence": [{"asset": "source:pmid:28035468", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "Gene expression profiling of gastric cancer patients, GSE62254 (300 samples) and GSE26253 (432 samples), was downloaded from Gene Expression Omnibus (GEO) database."}, {"asset": "source:pmid:28035468", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "Univariate survival analysis and LASSO (Least Absolute Shrinkage and Selectionator operator) (1000 iterations) of differentially expressed genes in GSE62254 was assessed using survival and glmnet in R package, respectively."}, {"asset": "source:pmid:28035468", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Hereinto, five or more genes in 11-Gene model, TRPC1, SGCE, TNFRSF11A, LRRN1, HLF, CYS1, PPP1R14A, NOV, NBEA, CES1 and RGN, was available to evaluate the prognostic risk of GC patients in GSE26253 (P = 0.00445)."}, {"asset": "source:pmid:28035468", "locator": "sections[0] \"Abstract\" paragraph 4", "quote": "In conclusion, we successfully generated a stable 5-Gene model, which could be utilized to predict prognosis of GC patients and would contribute to postoperational treatment and follow-up strategies."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "Using GEO gene-expression profiles from GSE62254 and GSE26253, with univariate survival analysis and LASSO used for model development, the study generated a stable 5-gene model to predict gastric cancer prognosis.", "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:28035468"], "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": "blocked", "blocked_on": "data:accession_absent", "description": "The GEO series accession resolves and its sample accessions are listed. 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