Six-long non-coding RNA signature predicts recurrence-free survival in hepatocellular carcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:30670911", "kind": "science-record", "revision": 6, "content_hash": "sha256:f02269e5f658f18be140d1463a34cdec682e8a6d9783806df2400ef6accbd9fc", "created_at": "2026-11-01T15:40:41Z", "updated_at": "2026-11-01T21:21:55Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Six-long non-coding RNA signature predicts recurrence-free survival in hepatocellular carcinoma.", "doi": "10.3748/wjg.v25.i2.220", "pmid": "30670911", "authors": ["Jing-Xian Gu", "Xing Zhang", "Run-Chen Miao", "Xiao-Hong Xiang", "Yu-Nong Fu", "Jing-Yao Zhang", "Chang Liu", "Kai Qu"], "journal": "World journal of gastroenterology", "year": 2019, "abstract": "BACKGROUND: Recent evidence shows that long non-coding RNAs (lncRNAs) are closely related to hepatogenesis and a few aggressive features of hepatocellular carcinoma (HCC). Increasing studies demonstrate that lncRNAs are potential prognostic factors for HCC. Moreover, several studies reported the combination of lncRNAs for predicting the overall survival (OS) of HCC, but the results varied. Thus, more effort including more accurate statistical approaches is needed for exploring the prognostic value of lncRNAs in HCC.\nAIM: To develop a robust lncRNA signature associated with HCC recurrence to improve prognosis prediction of HCC.\nMETHODS: Univariate COX regression analysis was performed to screen the lncRNAs significantly associated with recurrence-free survival (RFS) of HCC in GSE76427 for the least absolute shrinkage and selection operator (LASSO) modelling. The established lncRNA signature was validated and developed in The Cancer Genome Atlas (TCGA) series using Kaplan-Meier curves. The expression values of the identified lncRNAs were compared between the tumor and non-tumor tissues. Pathway enrichment of these lncRNAs was conducted based on the significantly co-expressed genes. A prognostic nomogram combining the lncRNA signature and clinical characteristics was constructed.\nRESULTS: The lncRNA signature consisted of six lncRNAs: MSC-AS1, POLR2J4, EIF3J-AS1, SERHL, RMST, and PVT1. This risk model was significantly associated with the RFS of HCC in the TCGA cohort with a hazard ratio (HR) being 1.807 (95%CI [confidence interval]: 1.329-2.457) and log-rank P-value being less than 0.001. The best candidates of the six-lncRNA signature were younger male patients with HBV infection in relatively early tumor-stage and better physical condition but with higher preoperative alpha-fetoprotein. All the lncRNAs were significantly upregulated in tumor samples compared to non-tumor samples (P < 0.05). The most significantly enriched pathways of the lncRNAs were TGF-β signaling pathway, cellular apoptosis-associated pathways, etc. The nomogram showed great utility of the lncRNA signature in HCC recurrence risk stratification.\nCONCLUSION: We have constructed a six-lncRNA signature for prognosis prediction of HCC. 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 utilized to constitute our predictive model.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:30670911", "locator": "sections[19] \"Research methods\" paragraph 2", "quote": "The latest statistical algorithm, the least absolute shrinkage and selection operator (LASSO), was utilized to constitute our predictive model."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data used in this manuscript are accessible through https://www.ncbi.nlm.nih.gov/geo/ and https://portal.gdc.cancer.gov/.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:30670911", "locator": "sections[23] \"fn-group\" paragraph 1", "quote": "The data used in this manuscript are accessible through https://www.ncbi.nlm.nih.gov/geo/ and https://portal.gdc.cancer.gov/."}], "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:30670911"], "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:GSE76427"], "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:GSE76427"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:30670911", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3748/wjg.v25.i2.220", "sha256": 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