Seven-senescence-associated gene signature predicts overall survival for Asian patients with hepatocellular carcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:31011256", "kind": "science-record", "revision": 8, "content_hash": "sha256:05dd60cf73e614d7649b325758c74e4654509d7b45a33747f9b37b24043c02f8", "created_at": "2026-11-01T15:40:44Z", "updated_at": "2026-11-01T22:00:30Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Seven-senescence-associated gene signature predicts overall survival for Asian patients with hepatocellular carcinoma.", "doi": "10.3748/wjg.v25.i14.1715", "pmid": "31011256", "authors": ["Xiao-Hong Xiang", "Li Yang", "Xing Zhang", "Xiao-Hua Ma", "Run-Chen Miao", "Jing-Xian Gu", "Yu-Nong Fu", "Qing Yao", "Jing-Yao Zhang", "Chang Liu", "Ting Lin", "Kai Qu"], "journal": "World journal of gastroenterology", "year": 2019, "abstract": "BACKGROUND: Cellular senescence is a recognized barrier for progression of chronic liver diseases to hepatocellular carcinoma (HCC). The expression of a cluster of genes is altered in response to environmental factors during senescence. However, it is questionable whether these genes could serve as biomarkers for HCC patients.\nAIM: To develop a signature of senescence-associated genes (SAGs) that predicts patients' overall survival (OS) to improve prognosis prediction of HCC.\nMETHODS: SAGs were identified using two senescent cell models. Univariate COX regression analysis was performed to screen the candidate genes significantly associated with OS of HCC in a discovery cohort (GSE14520) for the least absolute shrinkage and selection operator modelling. Prognostic value of this seven-gene signature was evaluated using two independent cohorts retrieved from the GEO (GSE14520) and the Cancer Genome Atlas datasets, respectively. Time-dependent receiver operating characteristic (ROC) curve analysis was conducted to compare the predictive accuracy of the seven-SAG signature and serum α-fetoprotein (AFP).\nRESULTS: A total of 42 SAGs were screened and seven of them, including KIF18B, CEP55, CIT, MCM7, CDC45, EZH2, and MCM5, were used to construct a prognostic formula. All seven genes were significantly downregulated in senescent cells and upregulated in HCC tissues. Survival analysis indicated that our seven-SAG signature was strongly associated with OS, especially in Asian populations, both in discovery and validation cohorts. Moreover, time-dependent ROC curve analysis suggested the seven-gene signature had a better predictive accuracy than serum AFP in predicting HCC patients' 1-, 3-, and 5-year OS.\nCONCLUSION: We developed a seven-SAG signature, which could predict OS of Asian HCC patients. This risk model provides new clinical evidence for the accurate diagnosis and targeted treatment of HCC.", "claims": [{"id": "claim-method", "statement": "The latest statistical algorithm-the least absolute shrinkage and selection operator (LASSO) was applied to create our prognostic model.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:31011256", "locator": "sections[21] \"Research methods\" paragraph 2", "quote": "The latest statistical algorithm-the least absolute shrinkage and selection operator (LASSO) was applied to create our prognostic model."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Two genomic profiling datasets of RS cells (GSE19018 and GSE36640), three datasets of OIS cells (GSE19864, GSE40349, and GSE60652), and one HCC dataset (GSE14520) were obtained from the Gene Expression Omnibus (GEO) database (http://www.ncbi.nlm.nih.gov/geo/).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:31011256", "locator": "sections[4] \"Data sources and processing\" paragraph 2", "quote": "Two genomic profiling datasets of RS cells (GSE19018 and GSE36640), three datasets of OIS cells (GSE19864, GSE40349, and GSE60652), and one HCC dataset (GSE14520) were obtained from the Gene Expression Omnibus (GEO) database (http://www.ncbi.nlm.nih.gov/geo/)."}], "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:31011256"], "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:GSE14520"], "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:GSE14520"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:31011256", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3748/wjg.v25.i14.1715", "sha256": 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