Identification of an autophagy-related 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:33351066", "kind": "science-record", "revision": 7, "content_hash": "sha256:467d86c4a363320b8c4849fd0829c383b3d56da0bf6e826051758a0d48637bfe", "created_at": "2026-11-01T15:41:20Z", "updated_at": "2026-11-01T22:02:48Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of an autophagy-related gene signature predicting overall survival for hepatocellular carcinoma.", "doi": "10.1042/BSR20203231", "pmid": "33351066", "authors": ["Wenfang Xu", "Wenke Guo", "Ping Lu", "Duan Ma", "Lei Liu", "Fudong Yu"], "journal": "Bioscience reports", "year": 2021, "abstract": "The poor prognosis of hepatocellular carcinoma (HCC) calls for the development of accurate prognostic models. The growing number of studies indicating a correlation between autophagy activity and HCC indicates there is a commitment to finding solutions for the prognosis of HCC from the perspective of autophagy. We used a cohort in The Cancer Genome Atlas (TCGA) to evaluate the expression of autophagy-related genes in 371 HCC samples using univariate Cox and lasso Cox regression analysis, and the prognostic features were identified. A prognostic model was established by combining the expression of selected genes with the multivariate Cox regression coefficient of each gene. Eight autophagy-related genes were selected as prognostic features of HCC. We established the HCC prognostic risk model in TCGA dataset using these identified prognostic genes. The model's stability was confirmed in two independent verification sets (GSE14520 and GSE36376). The model had a good predictive power for the overall survival (OS) of HCC (hazard ratio = 2.32, 95% confidence interval = 1.76-3.05, P<0.001). Moreover, the risk score computed by the model did not depend on other clinical parameters. Finally, the applicability of the model was demonstrated through a nomogram (C-index = 0.701). In the present study, we established an autophagy-related risk model having a high prediction accuracy for OS in HCC. Our findings will contribute to the definition of prognosis and establishment of personalized therapy for HCC patients.", "claims": [{"id": "claim-data-access", "statement": "The mRNA and corresponding phenotype data were obtained from UCSC Xena (https://tcga.xenahubs.net/download/TCGA.LIHC.sampleMap/HiSeqV2.gz; https://tcga.xenahubs.net/download/TCGA.LIHC.sampleMap/LIHC_clinicalMatrix) and GEO (GSE14520_GPL3921, GSE36376_GPL10558).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33351066", "locator": "sections[17] \"Data Availability\" paragraph 2", "quote": "The mRNA and corresponding phenotype data were obtained from UCSC Xena (https://tcga.xenahubs.net/download/TCGA.LIHC.sampleMap/HiSeqV2.gz; https://tcga.xenahubs.net/download/TCGA.LIHC.sampleMap/LIHC_clinicalMatrix) and GEO (GSE14520_GPL3921, GSE36376_GPL10558)."}], "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:33351066"], "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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"group_sizes": null, "group_sizes_basis": null, "platform": "3921;571", "taxon": "Homo sapiens", "title": "Gene expression data of human hepatocellular carcinoma (HCC)", "resolution_checked_at": "2026-11-01T15:39:54Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}, {"id": "data:geo:GSE36376", "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=GSE36376", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE36376", "version": null, "note": "The mRNA and corresponding phenotype data were obtained from UCSC Xena (https://tcga.xenahubs.net/download/TCGA.LIHC.sampleMap/HiSeqV2.gz; https://tcga.xenahubs.net/download/TCGA.LIHC.sampleMap/LIHC_clinicalMatrix) and GEO (GSE14520_GPL3921, GSE36376_GPL10558).", "structure": {"repository": 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