A combined hypoxia and immune gene signature for predicting survival and risk stratification in triple-negative breast cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:34341184", "kind": "science-record", "revision": 7, "content_hash": "sha256:5691d5191a640ac652154d29c73515cf3882452559e147f321172d359cb40c1d", "created_at": "2026-11-01T15:41:46Z", "updated_at": "2026-11-01T22:04:24Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "A combined hypoxia and immune gene signature for predicting survival and risk stratification in triple-negative breast cancer.", "doi": "10.18632/aging.203360", "pmid": "34341184", "authors": ["Xia Yang", "Xin Weng", "Yajie Yang", "Meng Zhang", "Yingjie Xiu", "Wenfeng Peng", "Xuhui Liao", "Meiquan Xu", "Yanhua Sun", "Xia Liu"], "journal": "Aging", "year": 2021, "abstract": "BACKGROUND: Increasing evidence showed that the clinical significance of the interaction between hypoxia and immune status in tumor microenvironment. However, reliable biomarkers based on the hypoxia and immune status in triple-negative breast cancer (TNBC) have not been well established. This study aimed to explore a gene signature based on the hypoxia and immune status for predicting prognosis, risk stratification, and individual treatment in TNBC.\nMETHODS: Hypoxia-related genes (HRGs) and Immune-related genes (IRGs) were identified using the weighted gene co-expression network analysis (WGCNA) method and the single-sample gene set enrichment analysis (ssGSEA Z-score) with the transcriptomic profiles from Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) cohort. Then, prognostic hypoxia and immune based genes were identified in TNBC patients from the METABRIC (N = 221), The Cancer Genome Atlas (TCGA) (N = 142), and GSE58812 (N = 107) using univariate cox regression model. A robust hypoxia-immune based gene signature for prognosis was constructed using the least absolute shrinkage and selection operator (LASSO) method. Based on the cross-cohort prognostic hypoxia-immune related gene signature, a comprehensive index of hypoxia and immune was developed and two risk groups with distinct hypoxia-immune status were identified. The prognosis value, hypoxia and immune status, and therapeutic response in different risk groups were analyzed. Furthermore, a nomogram was constructed to predict the prognosis for individual patients, and an independent cohort from the gene expression omnibus (GEO) database was used for external validation.\nRESULTS: Six cross-cohort prognostic hypoxia-immune related genes were identified to establish the comprehensive index of hypoxia and immune. Then, patients were clustered into high- and low-risk groups based on the hypoxia-immune status. Patients in the high-risk group showed poorer prognoses to their low-risk counterparts, and the nomogram we constructed yielded favorable performance to predict survival and risk stratification. Besides, the high-risk group had a higher expression of hypoxia-related genes and correlated with hypoxia status in tumor microenvironment. The high-risk group had lower fractions of activated immune cells, and exhibited lower expression of immune checkpoint markers. Furthermore, the ratio of complete response (CR) was greatly declined, and the ratio of breast cancer related events were significantly elevated in the high-risk group.\nCONCLUSION: The hypoxia-immune based gene signature we constructed for predicting prognosis was developed and validated, which may contribute to the optimization of risk stratification for prognosis and personalized treatment in TNBC patients.", "claims": [{"id": "claim-method", "statement": "The cross-cohort prognostic HRGs and IRGs were identified by Univariate Cox regression with a cutoff value of P < 0.1. The microarray dataset GSE58812 (N = 107) were downloaded from GEO (http://www.ncbi.nlm.nih.gov/geo/) [21], the normalized RNA-Seq data of 142 TNBC samples were procured from The Cancer Genome Atlas (TCGA) (https://portal.gdc.cancer.gov/repository), and the expression profiles of the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) TNBC dataset (N = 221) was obtained from cBioportal (http://www.cbioportal.org/) [22].", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34341184", "locator": "sections[9] \"Statistical analysis\" paragraph 2", "quote": "The cross-cohort prognostic HRGs and IRGs were identified by Univariate Cox regression with a cutoff value of P < 0.1."}, {"asset": "source:pmid:34341184", "locator": "sections[3] \"Data acquisition and preparation\" paragraph 2", "quote": "The microarray dataset GSE58812 (N = 107) were downloaded from GEO (http://www.ncbi.nlm.nih.gov/geo/) [21], the normalized RNA-Seq data of 142 TNBC samples were procured from The Cancer Genome Atlas (TCGA) (https://portal.gdc.cancer.gov/repository), and the expression profiles of the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) TNBC dataset (N = 221) was obtained from cBioportal (http://www.cbioportal.org/) [22]."}], "state": "complete"}, {"id": "claim-data-access", "statement": "This study is based on public datasets, which obtained from the Cancer Genome Atlas Program (TCGA), the cBio Cancer Genomics Portal (cBioportal), and Gene Expression Omnibus (GEO).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34341184", "locator": "sections[10] \"Data availability statement\" paragraph 2", "quote": "This study is based on public datasets, which obtained from the Cancer Genome Atlas Program (TCGA), the cBio Cancer Genomics Portal (cBioportal), and Gene Expression Omnibus (GEO)."}], "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:34341184"], "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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