Effect of differential hypoxia-related gene expression on glioblastoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:34024193", "kind": "science-record", "revision": 8, "content_hash": "sha256:7af7e64916d58ff1a491dc60d406cdc8adf76251e54b9fb8fcb864ce44d9f63a", "created_at": "2026-11-01T15:41:36Z", "updated_at": "2026-11-01T22:03:48Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Effect of differential hypoxia-related gene expression on glioblastoma.", "doi": "10.1177/03000605211013774", "pmid": "34024193", "authors": ["Chao-Qun Lin", "Lu-Kui Chen"], "journal": "The Journal of international medical research", "year": 2021, "abstract": "OBJECTIVE: Glioblastoma (GB) is a refractory malignancy with a high rate of recurrence and treatment resistance. Hypoxia-related genes are promising prognostic indicators for GB, so we herein developed a reliable hypoxia-related gene risk scoring model to predict the prognosis of patients with GB.\nMETHOD: Gene expression profiles and corresponding clinicopathological features of patients with GB were obtained from the Cancer Genome Atlas (TCGA; n = 160) and Gene Expression Omnibus (GEO) GSE7696 (n = 80) databases. Univariate and multivariate Cox regression analyses of differentially expressed hypoxia-related genes were performed using R 3.5.1 software.\nRESULT: Fourteen prognosis-related genes were identified and used to construct a risk signature. Patients with high-risk scores had significantly lower overall survival (OS) than those with low-risk scores. The median risk score was used as a critical value and for OS prediction in an independent external verification GSE7696 cohort. Risk score was not significantly affected by clinical-related factors. We also developed a prediction nomogram based on the TCGA training set to predict survival rates, and included six independent prognostic parameters in the TCGA prediction model.\nCONCLUSION: We determined a reliable hypoxia-related gene risk scoring model for predicting the prognosis of patients with GB.", "claims": [{"id": "claim-method", "statement": "Univariate and multivariate Cox regression analyses of differentially expressed hypoxia-related genes were performed using R 3.5.1 software.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34024193", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "Univariate and multivariate Cox regression analyses of differentially expressed hypoxia-related genes were performed using R 3.5.1 software."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Gene expression profiles and corresponding clinicopathological features of patients with GB were obtained from the Cancer Genome Atlas (TCGA; n = 160) and Gene Expression Omnibus (GEO) GSE7696 (n = 80) databases.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34024193", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "Gene expression profiles and corresponding clinicopathological features of patients with GB were obtained from the Cancer Genome Atlas (TCGA; n = 160) and Gene Expression Omnibus (GEO) GSE7696 (n = 80) 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:34024193"], "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:GSE7696"], "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:GSE7696"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:34024193", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1177/03000605211013774", "sha256": "sha256:17a09ebd7db18385699c640828258282031b73a7129334fdd0328da237e144d6", "bytes": 19430, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1177/03000605211013774", "version": null, "note": "Full article fetched from PMC; 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