A novel metabolic gene signature-based nomogram to predict overall survival in breast cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:33842588", "kind": "science-record", "revision": 8, "content_hash": "sha256:03567a1e55433dbadc67ca6aa1caa60b1a09419d9e288c1fecf017cee3bd2005", "created_at": "2026-11-01T15:41:32Z", "updated_at": "2026-11-01T22:03:32Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "A novel metabolic gene signature-based nomogram to predict overall survival in breast cancer.", "doi": "10.21037/atm-20-4813", "pmid": "33842588", "authors": ["Xi Sun", "Zhi-Rui Zhou", "Yan Fang", "Shuning Ding", "Shuangshuang Lu", "Zheng Wang", "Hui Wang", "Xiaosong Chen", "Kunwei Shen"], "journal": "Annals of translational medicine", "year": 2021, "abstract": "BACKGROUND: Breast cancer risk prediction is often based on clinicopathological characteristics despite the high heterogeneity derived from gene expression. Metabolic alteration is a hallmark of cancer, and thus, the integration of a metabolic signature with clinical parameters is necessary to predict disease outcomes in breast cancers.\nMETHODS: Metabolic genes were downloaded from the Gene Set Enrichment Analysis (GSEA) dataset. Genes with statistical significance in the univariate analysis were applied in the least absolute shrinkage and selection operator (LASSO) analysis to build a gene signature in the GSE20685 dataset. Clinicopathological characteristics and risk scores with prognostic significance were incorporated into the nomogram to predict the overall survival (OS) of patients. The Cancer Genome Atlas (TCGA) and GSE866166 datasets were used as the validation datasets. Time-dependent receiver operating characteristic (tROC) curves and calibration plots were used to assess the accuracy and discrimination of the model.\nRESULTS: A 55-gene metabolic gene signature (MGS) was constructed, and was significantly related to OS both in the discovery (P<0.001) and validation (P<0.001) datasets. The MGS was an independent prognostic factor and could divide patients into high- and low-risk groups regardless of their different prediction analysis of microarray 50 (PAM50) subtypes. Time-dependent ROC curves indicated that the risk scores based on the MGS [area under the ROC curve (AUC): 0.931] were superior to the those based on the American Joint Committee on Cancer (AJCC) stage (AUC: 0.781) and PAM50 (AUC: 0.675). A nomogram based on the AJCC stage and risk score could predict OS, and the calibration curves showed good agreement to the actual outcome, indicating that the nomogram may have practical utility. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analysis indicated that this MGS was primarily enriched in amino acid pathways.\nCONCLUSIONS: Our results demonstrated that the MGS was superior to existing risk predictors such as PAM50 and AJCC stage. By combining clinical factors (AJCC stage) and the MGS, a nomogram was constructed and showed good predictive ability for OS in breast cancer.", "claims": [{"id": "claim-method", "statement": "Univariate Cox regression was conducted to assess the association between gene expression levels and the OS of patients. Genes with a P value <0.05 were included in further analyses. The LASSO model, which uses a L1-norm to penalize the weight of the model parameters, was then applied to remove genes of high correlation (20,21).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33842588", "locator": "sections[4] \"Construction of the risk score\" paragraph 2", "quote": "Univariate Cox regression was conducted to assess the association between gene expression levels and the OS of patients."}, {"asset": "source:pmid:33842588", "locator": "sections[4] \"Construction of the risk score\" paragraph 2", "quote": "Genes with a P value <0.05 were included in further analyses."}, {"asset": "source:pmid:33842588", "locator": "sections[4] \"Construction of the risk score\" paragraph 2", "quote": "The LASSO model, which uses a L1-norm to penalize the weight of the model parameters, was then applied to remove genes of high correlation (20,21)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The metabolic signature was downloaded from the Gene Set Enrichment Analysis (GSEA) dataset.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33842588", "locator": "sections[3] \"Datasets and preprocessing\" paragraph 3", "quote": "The metabolic signature was downloaded from the Gene Set Enrichment Analysis (GSEA) dataset."}], "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:33842588"], "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:GSE20685"], "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:GSE20685"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:33842588", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.21037/atm-20-4813", "sha256": 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