An Innovative Prognostic Model Based on Four Genes in Asian Patient with Gastric Cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:32878427", "kind": "science-record", "revision": 9, "content_hash": "sha256:3af6109b13b6d47fd6339e74e73851328de4e2db98b78079c58999cb4c91528a", "created_at": "2026-11-01T15:41:09Z", "updated_at": "2026-11-01T22:45:16Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "An Innovative Prognostic Model Based on Four Genes in Asian Patient with Gastric Cancer.", "doi": "10.4143/crt.2020.424", "pmid": "32878427", "authors": ["Jiahui Chen", "Anqiang Wang", "Jun Ji", "Kai Zhou", "Zhaode Bu", "Guoqing Lyu", "Jiafu Ji"], "journal": "Cancer research and treatment", "year": 2021, "abstract": "PURPOSE: Gastric cancer (GC) has substantial biological differences between Asian and non-Asian populations, which makes it difficult to have a unified predictive measure for all people. We aimed to identify novel prognostic biomarkers to help predict the prognosis of Asian GC patients.\nMATERIALS AND METHODS: We investigated the differential gene expression between GC and normal tissues of GSE66229. Univariate, multivariate and Lasso Cox regression analyses were conducted to establish a four-gene-related prognostic model based on the risk score. The risk score was based on a linear combination of the expression levels of individual genes multiplied by their multivariate Cox regression coefficients. Validation of the prognostic model was conducted using The Cancer Genome Atlas (TCGA) database. A nomogram containing clinical characteristics and the prognostic model was established to predict the prognosis of Asian GC patients.\nRESULTS: Four genes (RBPMS2, RGN, PLEKHS1, and CT83) were selected to establish the prognostic model, and it was validated in the TCGA Asian cohort. Receiver operating characteristic analysis confirmed the sensitivity and specificity of the prognostic model. Based on the prognostic model, a nomogram containing clinical characteristics and the prognostic model was established, and Harrell's concordance index of the nomogram for evaluating the overall survival significantly higher than the model only focuses on the pathologic stage (0.74 vs. 0.64, p < 0.001).\nCONCLUSION: The four-gene-related prognostic model and the nomogram based on it are reliable tools for predicting the overall survival of Asian GC patients.", "claims": [{"id": "claim-method", "statement": "Next, we calculated the identification of differentially expressed genes (DEGs) with the help of the Limma package [9], where genes with logFC > 2 or logFC < −2 and adjusted p < 0.001 were considered for subsequent analysis, the cutoff value of LogFC was also used in other study [10].", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:32878427", "locator": "sections[5] \"3. 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Microarray data\" paragraph 2", "quote": "The gene expression profile matrix of GSE66229 was downloaded from the GEO database, and the clinical information was obtained from the corresponding published literature [8]."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": null, "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:32878427"], "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:32878427", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.4143/crt.2020.424", "sha256": 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