Construction and validation of a hypoxia-related gene signature to predict the prognosis of breast cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38561760", "kind": "science-record", "revision": 3, "content_hash": "sha256:1396f0e1babb18e07534bdc6b5b139e5e7b7a34060181d4f32599727b5200add", "created_at": "2026-11-01T19:20:31Z", "updated_at": "2026-11-01T22:11:35Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Construction and validation of a hypoxia-related gene signature to predict the prognosis of breast cancer.", "doi": "10.1186/s12885-024-12182-0", "pmid": "38561760", "authors": ["Chaoran Qiu", "Wenjun Wang", "Shengshan Xu", "Yong Li", "Jingtao Zhu", "Yiwen Zhang", "Chuqian Lei", "Weiwen Li", "Hongsheng Li", "Xiaoping Li"], "journal": "BMC cancer", "year": 2024, "abstract": "BACKGROUND: Among the most common forms of cancer worldwide, breast cancer posed a serious threat to women. Recent research revealed a lack of oxygen, known as hypoxia, was crucial in forming breast cancer. This research aimed to create a robust signature with hypoxia-related genes to predict the prognosis of breast cancer patients. The function of hypoxia genes was further studied through cell line experiments.\nMATERIALS AND METHODS: In the bioinformatic part, transcriptome and clinical information of breast cancer were obtained from The Cancer Genome Atlas(TCGA). Hypoxia-related genes were downloaded from the Genecards Platform. Differentially expressed hypoxia-related genes (DEHRGs) were identified. The TCGA filtered data was evenly split, ensuring a 1:1 distribution between the training and testing sets. Prognostic-related DEHRGs were identified through Cox regression. The signature was established through the training set. Then, it was validated using the test set and external validation set GSE131769 from Gene Expression Omnibus (GEO). The nomogram was created by incorporating the signature and clinicopathological characteristics. The predictive value of the nomogram was evaluated by C-index and receiver operating characteristiccurve. Immune microenvironment and mutation burden were also examined. In the experiment part, the function of the two most significant hypoxia-related genes were further explored by cell-line experiments.\nRESULTS: In the bioinformatic part, 141 up-regulated and 157 down-regulated DEHRGs were screened out. A prognostic signature was constructed containing nine hypoxia genes (ALOX15B, CA9, CD24, CHEK1, FOXM1, HOTAIR, KCNJ11, NEDD9, PSME2) in the training set. Low-risk patients exhibited a much more favorable prognosis than higher-risk ones (P < 0.001). The signature was double-validated in the test set and GSE131769 (P = 0.006 and P = 0.001). The nomogram showed excellent predictive value with 1-year OS AUC: 0.788, 3-year OS AUC: 0.783, and 5-year OS AUC: 0.817. Patients in the high-risk group had a higher tumor mutation burden when compared to the low-risk group. In the experiment part, the down-regulation of PSME2 inhibited cell growth ability and clone formation capability of breast cancer cells, while the down-regulation of KCNJ11 did not have any functions.\nCONCLUSION: Based on 9 DEHRGs, a reliable signature was established through the bioinformatic method. It could accurately predict the prognosis of breast cancer patients. Cell line experiment indicated that PSME2 played a protective role. Summarily, we provided a new insight to predict the prognosis of breast cancer by hypoxia-related genes.", "claims": [{"id": "claim-method", "statement": "Univariate and multivariate Cox regression were utilized to identify prognostic-related DEHRGs by using the equation", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38561760", "locator": "sections[5] \"Construction and validation of a signature\" paragraph 2", "quote": "Univariate and multivariate Cox regression were utilized to identify prognostic-related DEHRGs by using the equation"}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data could be downloaded at (https://www.cancer.gov; http://www.ncbi.nlm.nih.gov/geo; https://www.genecards.org) and the code used during the current study is available from the corresponding author on reasonable request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38561760", "locator": "sections[29] \"Data availability\" paragraph 2", "quote": "The data could be downloaded at (https://www.cancer.gov; http://www.ncbi.nlm.nih.gov/geo; https://www.genecards.org) and the code used during the current study is available from the corresponding author on reasonable request."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The nomogram showed excellent predictive value with 1-year OS AUC: 0.788, 3-year OS AUC: 0.783, and 5-year OS AUC: 0.817.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:38561760", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "The nomogram showed excellent predictive value with 1-year OS AUC: 0.788, 3-year OS AUC: 0.783, and 5-year OS AUC: 0.817."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The nomogram showed excellent predictive value with 1-year OS AUC: 0.788, 3-year OS AUC: 0.783, and 5-year OS AUC: 0.817.", "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:38561760"], "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:GSE131769"], "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:GSE131769"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:38561760", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1186/s12885-024-12182-0", "sha256": 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