An immune-related gene signature for predicting survival and immunotherapy efficacy in hepatocellular carcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:33089373", "kind": "science-record", "revision": 3, "content_hash": "sha256:059595534f4c7992a7cf2886a4b6aae6804ef127f4f9c9dcaefa56fb2fd0a69d", "created_at": "2026-11-01T17:34:13Z", "updated_at": "2026-11-01T21:23:58Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "An immune-related gene signature for predicting survival and immunotherapy efficacy in hepatocellular carcinoma.", "doi": "10.1007/s00262-020-02743-0", "pmid": "33089373", "authors": ["Yifei Dai", "Weijie Qiang", "Kequan Lin", "Yu Gui", "Xun Lan", "Dong Wang"], "journal": "Cancer immunology, immunotherapy : CII", "year": 2021, "abstract": "BACKGROUND: Hepatocellular carcinoma (HCC) ranks the fourth in terms of cancer-related mortality globally. Herein, in this research, we attempted to develop a novel immune-related gene signature that could predict survival and efficacy of immunotherapy for HCC patients.\nMETHODS: The transcriptomic and clinical data of HCC samples were downloaded from The Cancer Genome Atlas (TCGA) and GSE14520 datasets, followed by acquiring immune-related genes from the ImmPort database. Afterwards, an immune-related gene-based prognostic index (IRGPI) was constructed using the Least Absolute Shrinkage and Selection Operator (LASSO) regression model. Kaplan-Meier survival curves as well as time-dependent receiver operating characteristic (ROC) curve were performed to evaluate its predictive capability. Besides, both univariate and multivariate analyses on overall survival for the IRGPI and multiple clinicopathologic factors were carried out, followed by the construction of a nomogram. Finally, we explored the possible correlation of IRGPI with immune cell infiltration or immunotherapy efficacy.\nRESULTS: Analysis of 365 HCC samples identified 11 differentially expressed immune-related genes, which were selected to establish the IRGPI. Notably, it can predict the survival of HCC patients more accurately than published biomarkers. Furthermore, IRGPI can predict the infiltration of immune cells in the tumor microenvironment of HCC, as well as the response of immunotherapy.\nCONCLUSION: Collectively, the currently established IRGPI can accurately predict survival, reflect the immune microenvironment, and predict the efficacy of immunotherapy among HCC patients.", "claims": [{"id": "claim-method", "statement": "Afterwards, an immune-related gene-based prognostic index (IRGPI) was constructed using the Least Absolute Shrinkage and Selection Operator (LASSO) regression model.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33089373", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "Afterwards, an immune-related gene-based prognostic index (IRGPI) was constructed using the Least Absolute Shrinkage and Selection Operator (LASSO) regression model."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The transcriptomic and clinical data of HCC samples were downloaded from The Cancer Genome Atlas (TCGA) and GSE14520 datasets, followed by acquiring immune-related genes from the ImmPort database.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33089373", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "The transcriptomic and clinical data of HCC samples were downloaded from The Cancer Genome Atlas (TCGA) and GSE14520 datasets, followed by acquiring immune-related genes from the ImmPort database."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The established IRGPI can accurately predict survival, reflect the immune microenvironment, and predict the efficacy of immunotherapy among HCC patients.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:33089373", "locator": "sections[0] \"Abstract\" paragraph 4", "quote": "Collectively, the currently established IRGPI can accurately predict survival, reflect the immune microenvironment, and predict the efficacy of immunotherapy among HCC patients."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The established IRGPI can accurately predict survival, reflect the immune microenvironment, and predict the efficacy of immunotherapy among HCC patients.", "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:33089373"], "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:33089373", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1007/s00262-020-02743-0", "sha256": 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