Identification of a Two-Gene Signature and Establishment of a Prognostic Nomogram Predicting Overall Survival in Diffuse-Type Gastric Cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36661663", "kind": "science-record", "revision": 2, "content_hash": "sha256:ac5f2508150c419de91c60bc39ce4b0df86d63871b51abe62f3f17fe9cdf6b48", "created_at": "2026-11-01T19:01:13Z", "updated_at": "2026-11-01T21:29:55Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of a Two-Gene Signature and Establishment of a Prognostic Nomogram Predicting Overall Survival in Diffuse-Type Gastric Cancer.", "doi": "10.3390/curroncol30010014", "pmid": "36661663", "authors": ["Songyao Chen", "Jiannan Xu", "Songcheng Yin", "Huabin Wang", "Guangyao Liu", "Xinghan Jin", "Junchang Zhang", "Huijin Wang", "Han Wang", "Huan Li", "Jianming Liang", "Yulong He", "Changhua Zhang"], "journal": "Current oncology (Toronto, Ont.)", "year": 2022, "abstract": "BACKGROUND: It is widely acknowledged that the molecular biological characteristics of diffuse-type gastric cancer are different from intestinal-type gastric cancer. Notwithstanding that significant progress in high-throughput sequencing technology has been made, there is a paucity of effective prognostic biomarkers for diffuse gastric cancer for clinical practice.\nMETHODS: We downloaded four GEO datasets (GSE22377, GSE38749, GSE47007 and GSE62254) to establish and validate a prognostic two-gene signature for diffuse gastric cancer. The TGCA-STAD dataset was used for external validation. The optimal gene signature was established by using Cox regression analysis. Receiver operating characteristic (ROC) methodology was used to find the best prognostic model. Gene set enrichment analysis was used to analyze the possible signaling pathways of the two genes (MEF2C and TRIM15).\nRESULTS: A total of four differently expressed genes (DEGs) (two upregulated and two downregulated) were identified. After a comprehensive analysis, two DEGs (MEF2C and TRIM15) were utilized to construct a prognostic model. A prognostic prediction model was constructed according to T stage, N stage, M stage and the expression of MEF2C and TRIM15. The area under the time-dependent receiver operator characteristic was used to evaluate the performance of the prognosis model in the GSE62254 dataset.\nCONCLUSIONS: We demonstrated that MEF2C and TRIM15 might be key genes. We also established a prognostic nomogram based on the two-gene signature that yielded a good performance for predicting overall survival in diffuse-type gastric cancer.", "claims": [{"id": "claim-method", "statement": "After testing for collinearity, prognostic gene signatures and relevant clinical parameters were included to establish a prognostic nomogram via a stepwise Cox regression model to predict the 1-, 3- and 5-year overall survival of diffuse-type gastric cancer patients in the GSE62254 dataset.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36661663", "locator": "sections[7] \"2.5. The Establishment of the Predictive Nomogram\" paragraph 2", "quote": "After testing for collinearity, prognostic gene signatures and relevant clinical parameters were included to establish a prognostic nomogram via a stepwise Cox regression model to predict the 1-, 3- and 5-year overall survival of diffuse-type gastric cancer patients in the GSE62254 dataset."}], "state": "complete"}, {"id": "claim-data-access", "statement": "All dataset analysis for this study can be found in the Gene Expression Omnibus (GEO) database, the Cancer Genome Atlas (TCGA) database and the website of Kaplan–Meier Plotter.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36661663", "locator": "sections[26] \"notes\" paragraph 1", "quote": "All dataset analysis for this study can be found in the Gene Expression Omnibus (GEO) database, the Cancer Genome Atlas (TCGA) database and the website of Kaplan–Meier Plotter."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "", "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:36661663"], "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:GSE22377"], "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:GSE22377"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:36661663", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3390/curroncol30010014", "sha256": 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