Tumor microenvironment-related multigene prognostic prediction model for breast cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35060926", "kind": "science-record", "revision": 4, "content_hash": "sha256:4f50aeb1ffbee950eeea21ef096babd44e1dd8a6797d8ba865211d9334a5d42f", "created_at": "2026-11-01T17:36:47Z", "updated_at": "2026-11-01T22:05:39Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Tumor microenvironment-related multigene prognostic prediction model for breast cancer.", "doi": "10.18632/aging.203845", "pmid": "35060926", "authors": ["Kai Hong", "Yingjue Zhang", "Lingli Yao", "Jiabo Zhang", "Xianneng Sheng", "Yu Guo"], "journal": "Aging", "year": 2022, "abstract": "BACKGROUND: Breast cancer is an invasive disease with complex molecular mechanisms. Prognosis-related biomarkers are still urgently needed to predict outcomes of breast cancer patients.\nMETHODS: Original data were download from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO). The analyses were performed using perl-5.32 and R-x64-4.1.1.\nRESULTS: In this study, 1086 differentially expressed genes (DEGs) were identified in the TCGA cohort; 523 shared DEGs were identified in the TCGA and GSE10886 cohorts. Eight subtypes were estimated using non-negative matrix factorization clustering with significant differences seen in overall survival (OS) and progression-free survival (PFS) (P < 0.01). Univariate Cox analysis and least absolute shrinkage and selection operator (LASSO) regression analysis were performed to develop a related risk score related to the 17 DEGs; this score separated breast cancer into low- and high-risk groups with significant differences in survival (P < 0.01) and showed powerful effectiveness (TCGA all group: 1-year area under the curve [AUC] = 0.729, 3-year AUC = 0.778, 5-year AUC = 0.781). A nomogram prediction model was constructed using non-negative matrix factorization clustering, the risk score, and clinical characteristics. Our model was confirmed to be related with tumor microenvironment. Furthermore, DEGs in high-risk breast cancer were enriched in histidine metabolism (normalized enrichment score [NES] = 1.49, P < 0.05), protein export (NES = 1.58, P < 0.05), and steroid hormone biosynthesis signaling pathways (NES = 1.56, P < 0.05).\nCONCLUSIONS: We established a comprehensive model that can predict prognosis and guide treatment.", "claims": [{"id": "claim-method", "statement": "Univariate Cox analysis and least absolute shrinkage and selection operator (LASSO) regression analysis were performed to develop a related risk score related to the 17 DEGs", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35060926", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Univariate Cox analysis and least absolute shrinkage and selection operator (LASSO) regression analysis were performed to develop a related risk score related to the 17 DEGs"}], "state": "complete"}, {"id": "claim-data-access", "statement": "All data are available from online database included TCGA (https://portal.gdc.cancer.gov/), GEO (https://www.ncbi.nlm.nih.gov/geo/), THPA (https://www.proteinatlas.org/), and GSEA (https://www.gsea-msigdb.org/gsea/index.jsp).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35060926", "locator": "sections[14] \"Data availability statement\" paragraph 2", "quote": "All data are available from online database included TCGA (https://portal.gdc.cancer.gov/), GEO (https://www.ncbi.nlm.nih.gov/geo/), THPA (https://www.proteinatlas.org/), and GSEA (https://www.gsea-msigdb.org/gsea/index.jsp)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "We established a comprehensive model that can predict prognosis and guide treatment.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35060926", "locator": "sections[0] \"Abstract\" paragraph 4", "quote": "We established a comprehensive model that can predict prognosis and guide treatment."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "We established a comprehensive model that can predict prognosis and guide treatment.", "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:35060926"], "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:GSE10886"], "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": {"fold_change": "xpression analysis (log fold change [FC] >1, false discover", "fdr_threshold": "og fold change [FC] >1, false discovery rate [FDR] < 0"}, "inputs": ["data:geo:GSE10886"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:35060926", "role": "manuscript", 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online database included TCGA (https://portal.gdc.cancer.gov/), GEO (https://www.ncbi.nlm.nih.gov/geo/), THPA (https://www.proteinatlas.org/), and GSEA (https://www.gsea-msigdb.org/gsea/index.jsp).", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "All data are available from online database included TCGA (https://portal.gdc.cancer.gov/), GEO (https://www.ncbi.nlm.nih.gov/geo/), THPA (https://www.proteinatlas.org/), and GSEA (https://www.gsea-msigdb.org/gsea/index.jsp).", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM140985", "GSM140986", "GSM140990", "GSM140991", "GSM140992", "GSM140999", "GSM141001", "GSM141002", "GSM141067", "GSM141070", "GSM141071", "GSM141072", "GSM141073", "GSM141075", "GSM141076", "GSM141077", "GSM141079", "GSM141081", "GSM141084", "GSM141085", "GSM141088", "GSM141092", "GSM141093", "GSM141095", "GSM141096", "GSM141097", "GSM141098", "GSM141099", "GSM141102", "GSM141105", 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