A gene-based risk score model for predicting recurrence-free survival in patients with hepatocellular carcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:33402113", "kind": "science-record", "revision": 7, "content_hash": "sha256:ecd3d8349ba90471b0e9283d97fae6eb54601dd121a3a0efb211a49cc999fa6c", "created_at": "2026-11-01T15:41:21Z", "updated_at": "2026-11-01T22:02:52Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "A gene-based risk score model for predicting recurrence-free survival in patients with hepatocellular carcinoma.", "doi": "10.1186/s12885-020-07692-6", "pmid": "33402113", "authors": ["Wenhua Wang", "Lingchen Wang", "Xinsheng Xie", "Yehong Yan", "Yue Li", "Quqin Lu"], "journal": "BMC cancer", "year": 2021, "abstract": "BACKGROUND: Hepatocellular carcinoma (HCC) remains the most frequent liver cancer, accounting for approximately 90% of primary liver cancers worldwide. The recurrence-free survival (RFS) of HCC patients is a critical factor in devising a personal treatment plan. Thus, it is necessary to accurately forecast the prognosis of HCC patients in clinical practice.\nMETHODS: Using The Cancer Genome Atlas (TCGA) dataset, we identified genes associated with RFS. A robust likelihood-based survival modeling approach was used to select the best genes for the prognostic model. Then, the GSE76427 dataset was used to evaluate the prognostic model's effectiveness.\nRESULTS: We identified 1331 differentially expressed genes associated with RFS. Seven of these genes were selected to generate the prognostic model. The validation in both the TCGA cohort and GEO cohort demonstrated that the 7-gene prognostic model can predict the RFS of HCC patients. Meanwhile, the results of the multivariate Cox regression analysis showed that the 7-gene risk score model could function as an independent prognostic factor. In addition, according to the time-dependent ROC curve, the 7-gene risk score model performed better in predicting the RFS of the training set and the external validation dataset than the classical TNM staging and BCLC. Furthermore, these seven genes were found to be related to the occurrence and development of liver cancer by exploring three other databases.\nCONCLUSION: Our study identified a seven-gene signature for HCC RFS prediction that can be used as a novel and convenient prognostic tool. These seven genes might be potential target genes for metabolic therapy and the treatment of HCC.", "claims": [{"id": "claim-method", "statement": "Using The Cancer Genome Atlas (TCGA) dataset, we identified genes associated with RFS. A robust likelihood-based survival modeling approach was used to select the best genes for the prognostic model. Then, the GSE76427 dataset was used to evaluate the prognostic model’s effectiveness.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33402113", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "Using The Cancer Genome Atlas (TCGA) dataset, we identified genes associated with RFS. A robust likelihood-based survival modeling approach was used to select the best genes for the prognostic model. Then, the GSE76427 dataset was used to evaluate the prognostic model’s effectiveness."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The gene expression profiles and clinical information datasets downloaded from The Cancer Genome Atlas (TCGA-LIHC)(https://portal.gdc.cancer.gov) and the Gene Expression Omnibus (GEO)(https://www.ncbi.nlm.nih.gov), accession numbers: GSE76427. Genetic alterations was retrieved from the cBioPortal website (http://www.cbioportal.org/).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33402113", "locator": "sections[26] \"Availability of data and materials\" paragraph 2", "quote": "The gene expression profiles and clinical information datasets downloaded from The Cancer Genome Atlas (TCGA-LIHC)(https://portal.gdc.cancer.gov) and the Gene Expression Omnibus (GEO)(https://www.ncbi.nlm.nih.gov), accession numbers: GSE76427. 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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:33402113", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1186/s12885-020-07692-6", "sha256": "sha256:0f898a63dc55b85f001e7392e956870e57affab0c7634c96d99b7671502a3048", "bytes": 32117, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1186/s12885-020-07692-6", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC7786458", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE76427", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE76427", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE76427", "version": null, "note": "The gene expression profiles and clinical information datasets downloaded from The Cancer Genome Atlas (TCGA-LIHC)(https://portal.gdc.cancer.gov) and the Gene Expression Omnibus (GEO)(https://www.ncbi.nlm.nih.gov), accession numbers: GSE76427. 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"band": "verbatim", "quote": "Using The Cancer Genome Atlas (TCGA) dataset, we identified genes associated with RFS. A robust likelihood-based survival modeling approach was used to select the best genes for the prognostic model. Then, the GSE76427 dataset was used to evaluate the prognostic model’s effectiveness."}}], "targets": [], "gaps": ["method.reader_named_unmapped"]}, "capability": {"state": "complete", "requirements": [{"capability_id": "capability:computational_analysis", "label": "Computational analysis platform", "assay_families": ["meta_analysis", "machine_learning", "survival_analysis", "statistical_regression", "pca", "molecular_docking", "network_analysis", "differential_expression", "gwas"], "availability_state": "owned", "tier_unlocked": "R1", "candidate": "Automated Assays Laravel application and simulated adapters"}], "gaps": []}, "readiness": {"state": "complete", "tier": "R1", "blocking_axis": "none", "blocked_on": "nothing:replicable_now", "axes": {"method": {"tier": "R1", "expressible": true, "required_operations": ["cox_ph"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: cox_proportional_hazards."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. 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