Identification of a prognostic signature of nine metabolism-related genes for hepatocellular carcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:32953265", "kind": "science-record", "revision": 7, "content_hash": "sha256:50270002525b5e4851090551c10081f269e5a2e5f6e12e26f9a77c90423a46c6", "created_at": "2026-11-01T15:41:10Z", "updated_at": "2026-11-01T22:02:13Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of a prognostic signature of nine metabolism-related genes for hepatocellular carcinoma.", "doi": "10.7717/peerj.9774", "pmid": "32953265", "authors": ["Chaozhi Tang", "Jiakang Ma", "Xiuli Liu", "Zhengchun Liu"], "journal": "PeerJ", "year": 2020, "abstract": "BACKGROUND: Hepatocellular carcinoma (HCC) is the fifth most common cancer. Since changes in liver metabolism contribute to liver disease development, it is necessary to build a metabolism-related prognostic model for HCC.\nMETHODS: We constructed a metabolism-related-gene (MRG) signature comprising nine genes, which segregated HCC patients into high- and low-risk groups.\nRESULTS: The survival rate (overall survival: OS; relapse-free survival; and progression-free survival) of patients in the low-risk group of The Cancer Genome Atlas (TCGA) cohort was significantly higher than that of patients in the high-risk group. The OS prognostic signature was validated in the International Cancer Genome Consortium independent cohort. The corresponding receiver operating characteristic curves of the model indicated that the signature had good diagnostic efficiency, in terms of improving OS over 1, 3, and 5 years. Hierarchical analysis demonstrated that the MRG signature was significantly associated with better prognosis in male patients, patients aged ≤ 65 years, and patients carrying the wild-type TP53 or CTNNB1 genes. A nomogram was established, and good performance and clinical practicability were confirmed. Additionally, using the GSE109211 dataset from the Gene Expression Omnibus database, we were able to verify that the nine genes in this MRG signature had different responses to sorafenib, suggesting that some of these MRGs may act as therapeutic targets for HCC.\nCONCLUSIONS: We believe that these findings will add value in terms of the diagnosis, treatment, and prognosis of HCC.", "claims": [{"id": "claim-method", "statement": "Next, the “glmnet” package was used to implement the LASSO Cox regression model. For the discovery cohort (n = 374 patients), fragments per kilobase of exon per million mapped reads (FPKM) data and the corresponding clinical information related to gene expression were downloaded from The Cancer Genome Atlas (TCGA). For the validation cohort (n = 243 patients), the original data and corresponding clinical data related to gene expression were downloaded from the Liver Cancer-RIKEN, Japan (LIRI-JP) project of the International Cancer Genome Consortium (ICGC).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:32953265", "locator": "sections[4] \"Identification of a prognostic signature based on differential MRG expression\" paragraph 2", "quote": "Next, the “glmnet” package was used to implement the LASSO Cox regression model."}, {"asset": "source:pmid:32953265", "locator": "sections[3] \"Data processing and extraction of differentially expressed MRGs\" paragraph 2", "quote": "For the discovery cohort (n = 374 patients), fragments per kilobase of exon per million mapped reads (FPKM) data and the corresponding clinical information related to gene expression were downloaded from The Cancer Genome Atlas (TCGA)."}, {"asset": "source:pmid:32953265", "locator": "sections[3] \"Data processing and extraction of differentially expressed MRGs\" paragraph 2", "quote": "For the validation cohort (n = 243 patients), the original data and corresponding clinical data related to gene expression were downloaded from the Liver Cancer-RIKEN, Japan (LIRI-JP) project of the International Cancer Genome Consortium (ICGC)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The following information was supplied regarding data availability:\nThe data is available at the Cancer Genome Altas (TCGA, https://cancergenome.nih.gov).\nunder TCGA search term: TCGA-LIHC and from the International cancer genomics consortium (ICGC, https://icgc.org) using ICGC search term: LIRI-JP (https://dcc.icgc.org/releases/current/Projects/LIRI-JP), and NCBI GEO: GSE109211.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:32953265", "locator": "sections[31] \"Data Availability Statement\" paragraphs 1-4", "quote": "The following information was supplied regarding data availability:\nThe data is available at the Cancer Genome Altas (TCGA, https://cancergenome.nih.gov).\nunder TCGA search term: TCGA-LIHC and from the International cancer genomics consortium (ICGC, https://icgc.org) using ICGC search term: LIRI-JP (https://dcc.icgc.org/releases/current/Projects/LIRI-JP), and NCBI GEO: GSE109211."}], "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:32953265"], "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:GSE109211"], "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:GSE109211"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:32953265", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.7717/peerj.9774", "sha256": 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