Identifying a hypoxia related score to predict the prognosis of bladder cancer: a study with The Cancer Genome Atlas (TCGA) database.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35070817", "kind": "science-record", "revision": 5, "content_hash": "sha256:38276e85bfcb4a4e5c0bbe3476e63e47753aed51b572727871bf97791c8dd744", "created_at": "2026-11-01T17:36:48Z", "updated_at": "2026-11-01T22:05:39Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identifying a hypoxia related score to predict the prognosis of bladder cancer: a study with The Cancer Genome Atlas (TCGA) database.", "doi": "10.21037/tau-21-569", "pmid": "35070817", "authors": ["Zhenan Zhang", "Qinhan Li", "Aolin Li", "Feng Wang", "Zhicun Li", "Yisen Meng", "Qian Zhang"], "journal": "Translational andrology and urology", "year": 2021, "abstract": "BACKGROUND: Recurrence is common in bladder cancer, with a hypoxic tumor microenvironment (TME) playing a role in genetic instability and prognosis of bladder cancer. However, we still lack practical hypoxia related model for predicting the prognosis of bladder cancer. In this study, we identified new prognosis-related hypoxia genes and established a new hypoxia score related signature.\nMETHODS: The Gene Set Variation Analysis (GSVA) algorithm was utilized to calculate the hypoxia score of bladder cancer cases found on the The Cancer Genome Atlas (TCGA) database on the gene expression profiles. The cases were first divided into low- and high-hypoxia score groups and then differentially expressed genes (DEGs) expression analysis was conducted. Hypoxia-related genes were identified using weighted gene co-expression network analysis (WGCNA). We then conducted a protein-protein interaction (PPI) network and carried out functional enrichment analysis of the genes that overlapped between DEGs and hypoxia-related genes. LASSO Cox regression analysis was used to establish a hypoxia-related prognostic signature, which was validated using the GSE69795 dataset downloaded from GEO database.\nRESULTS: Results from Kaplan-Meier analysis showed that patients with a high hypoxia score had significantly poor overall survival compared to patients with low hypoxia score. We selected 270 DEGs between low- and high-hypoxia score groups, while WGCNA analysis identified 1,313 genes as hypoxia-related genes. A total of 170 genes overlapped between DEGs and hypoxia-related genes. LASSO algorithms identified 29 genes associated with bladder cancer prognosis, which were used to construct a novel 29-gene signature model. The prognostic risk model performed well, since the receiver operating characteristic (ROC) curve showed an accuracy of 0.802 (95% CI: 0.759-0.844), and Cox proportional hazards regression analysis proved the model an independent predictor with hazard ratio (HR) =1.789 (95% CI: 1.585-2.019) (P<0.001). The low-risk score patients had remarkably longer overall survival than patients with a higher score (survival rate 71.06% vs. 23.66%) in the The Cancer Genome Atlas (TCGA) cohort (P<0.0001) and in the dataset GSE69795 (P=0.0079).\nCONCLUSIONS: We established a novel 29-gene hypoxia-related signature model to predict the prognosis of bladder cancer cases. This model and identified hypoxia-related genes may further been used as biomarkers, assisting the evaluation of prognosis of bladder cancer cases and decision making in clinical practice.", "claims": [{"id": "claim-method", "statement": "LASSO Cox regression analysis was used to establish a hypoxia-related prognostic signature, which was validated using the GSE69795 dataset downloaded from GEO database.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35070817", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "LASSO Cox regression analysis was used to establish a hypoxia-related prognostic signature, which was validated using the GSE69795 dataset downloaded from GEO database."}], "state": "complete"}, {"id": "claim-data-access", "statement": "We downloaded the gene expression profiles and clinical characteristics of bladder cancer cases from the TCGA database (March 2020).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35070817", "locator": "sections[3] \"Database\" paragraph 2", "quote": "We downloaded the gene expression profiles and clinical characteristics of bladder cancer cases from the TCGA database (March 2020)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The prognostic risk model performed well, since the receiver operating characteristic (ROC) curve showed an accuracy of 0.802 (95% CI: 0.759–0.844), and Cox proportional hazards regression analysis proved the model an independent predictor with hazard ratio (HR) =1.789 (95% CI: 1.585–2.019) (P<0.001).", "kind": "quantitative", "target": {"value": 95.0, "unit": "per cent", "tolerance": null, "source": "The prognostic risk model performed well, since the receiver operating characteristic (ROC) curve showed an accuracy of 0.802 (95% CI: 0.759–0.844), and Cox proportional hazards regression analysis proved the model an independent predictor with hazard ratio (HR) =1.789 (95% CI: 1.585–2.019) (P<0.001)."}, "evidence": [{"asset": "source:pmid:35070817", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "The prognostic risk model performed well, since the receiver operating characteristic (ROC) curve showed an accuracy of 0.802 (95% CI: 0.759–0.844), and Cox proportional hazards regression analysis proved the model an independent predictor with hazard ratio (HR) =1.789 (95% CI: 1.585–2.019) (P<0.001)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The prognostic risk model performed well, since the receiver operating characteristic (ROC) curve showed an accuracy of 0.802 (95% CI: 0.759–0.844), and Cox proportional hazards regression analysis proved the model an independent predictor with hazard ratio (HR) =1.789 (95% CI: 1.585–2.019) (P<0.001).", "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:35070817"], "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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