Identification of a thirteen-gene signature predicting overall survival for hepatocellular carcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:33835133", "kind": "science-record", "revision": 4, "content_hash": "sha256:9e63dded51b78f1094f3b1f3bfb60dc36479475a697e3308ddb0a48f4501196a", "created_at": "2026-11-01T15:41:32Z", "updated_at": "2026-11-01T21:25:07Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of a thirteen-gene signature predicting overall survival for hepatocellular carcinoma.", "doi": "10.1042/BSR20202870", "pmid": "33835133", "authors": ["Xiaohan Zhou", "Chengdong Liu", "Hanyi Zeng", "Dehua Wu", "Li Liu"], "journal": "Bioscience reports", "year": 2021, "abstract": "BACKGROUND: Hepatocellular carcinoma (HCC) is a malignant tumor of the digestive system characterized by mortality rate and poor prognosis. To indicate the prognosis of HCC patients, lots of genes have been screened as prognostic indicators. However, the predictive efficiency of single gene is not enough. Therefore, it is essential to identify a risk-score model based on gene signature to elevate predictive efficiency.\nMETHODS: Lasso regression analysis followed by univariate Cox regression was employed to establish a risk-score model for HCC prognosis prediction based on The Cancer Genome Atlas (TCGA) dataset and Gene Expression Omnibus (GEO) dataset GSE14520. R package 'clusterProfiler' was used to conduct function and pathway enrichment analysis. The infiltration level of various immune and stromal cells in the tumor microenvironment (TME) were evaluated by single-sample GSEA (ssGSEA) of R package 'GSVA'.\nRESULTS: This prognostic model is an independent prognostic factor for predicting the prognosis of HCC patients and can be more effective by combining with clinical data through the construction of nomogram model. Further analysis showed patients in high-risk group possess more complex TME and immune cell composition.\nCONCLUSIONS: Taken together, our research suggests the thirteen-gene signature to possess potential prognostic value for HCC patients and provide new information for immunological research and treatment in HCC.", "claims": [{"id": "claim-method", "statement": "Student’s t test (two-tailed) was used to analyze differences between two groups using R software (version: 3.6.2).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33835133", "locator": "sections[12] \"Statistical analysis\" paragraph 2", "quote": "Student’s t test (two-tailed) was used to analyze differences between two groups using R software (version: 3.6.2)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "All data, models, or code used in the present study are available from the corresponding authors on request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33835133", "locator": "sections[21] \"Data Availability\" paragraph 2", "quote": "All data, models, or code used in the present study are available from the corresponding authors on request."}], "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:33835133"], "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. 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