A systematic analysis of a potential metabolism-related prognostic signature for breast cancer patients.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:33708957", "kind": "science-record", "revision": 8, "content_hash": "sha256:428f97f4185b85b2ceb09947495412c842ca72aa5d567fc3bf9b5dc3501256e0", "created_at": "2026-11-01T15:41:28Z", "updated_at": "2026-11-01T22:03:18Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "A systematic analysis of a potential metabolism-related prognostic signature for breast cancer patients.", "doi": "10.21037/atm-20-7600", "pmid": "33708957", "authors": ["Shibo Yu", "Xiaowen Wang", "Lizhe Zhu", "Peiling Xie", "Yudong Zhou", "Siyuan Jiang", "Heyan Chen", "Xiaoqin Liao", "Shengyu Pu", "Zhenzhen Lei", "Bin Wang", "Yu Ren"], "journal": "Annals of translational medicine", "year": 2021, "abstract": "BACKGROUND: Metabolic pathways play an essential role in breast cancer. However, the role of metabolism-related genes in the early diagnosis of breast cancer remains unknown.\nMETHODS: In our study, RNA sequencing (RNA-seq) expression data and clinicopathological information from The Cancer Genome Atlas (TCGA) and GSE20685 were obtained. Univariate cox regression and least absolute shrinkage and selection operator (LASSO) regression analyses were performed on the differentially expressed metabolism-related genes. Then, the formula of the metabolism-related risk model was composed, and the risk score of each patient was calculated. The breast cancer patients were divided into high-risk and low-risk groups with a cutoff of the median expression value of the risk score, and the prognostic analysis was also used to analyze the survival time between these two groups. In the end, we also analyzed the expression, interaction, and correlation among genes in the metabolism-related gene risk model.\nRESULTS: The results from the prognostic analysis indicated that the survival was significantly poorer in the high-risk group than in the low-risk group in both TCGA and GSE20685 datasets. In addition, after adjusting for different clinicopathological features in multivariate analysis, the metabolism-related risk model remained an independent prognostic indicator in TCGA dataset.\nCONCLUSIONS: In summary, we systematically developed a potential metabolism-related gene risk model for predicting prognosis in breast cancer patients.", "claims": [{"id": "claim-method", "statement": "Furthermore, univariate and multivariate Cox regression analyses were used to determine whether the metabolism-related gene risk model could be an independent predictor.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33708957", "locator": "sections[5] \"Statistical analysis\" paragraph 4", "quote": "Furthermore, univariate and multivariate Cox regression analyses were used to determine whether the metabolism-related gene risk model could be an independent predictor."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The RNA sequencing (RNA-seq) expression data and clinicopathological information of female breast cancer patients from 1096 breast cancer tissue samples and 112 non-tumor tissue samples were downloaded from the TCGA-BRCA dataset as the training set (https://portal.gdc.cancer.gov/). All data were publicly available and downloaded from online databases.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33708957", "locator": "sections[3] \"Data collection\" paragraph 2", "quote": "The RNA sequencing (RNA-seq) expression data and clinicopathological information of female breast cancer patients from 1096 breast cancer tissue samples and 112 non-tumor tissue samples were downloaded from the TCGA-BRCA dataset as the training set (https://portal.gdc.cancer.gov/)."}, {"asset": "source:pmid:33708957", "locator": "sections[19] \"notes\" paragraph 1", "quote": "All data were publicly available and downloaded from online databases."}], "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:33708957"], "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:33708957", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.21037/atm-20-7600", "sha256": "sha256:2958a122da9da98a5a8a17c365388109bfbebed3681f6255796b8e415ccc9073", "bytes": 26944, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.21037/atm-20-7600", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC7944328", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE20685", "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=GSE20685", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE20685", "version": null, "note": "The RNA sequencing (RNA-seq) expression data and clinicopathological information of female breast cancer patients from 1096 breast cancer tissue samples and 112 non-tumor tissue samples were downloaded from the TCGA-BRCA dataset as the training set (https://portal.gdc.cancer.gov/). 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