Identification of a Prognosis-Related Risk Signature for Bladder Cancer to Predict Survival and Immune Landscapes.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:34708131", "kind": "science-record", "revision": 4, "content_hash": "sha256:8222a3a8cb668ab7db30a2047b9bfc32b43993e008f502b702857e184c187d47", "created_at": "2026-11-01T17:36:22Z", "updated_at": "2026-11-01T22:05:02Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of a Prognosis-Related Risk Signature for Bladder Cancer to Predict Survival and Immune Landscapes.", "doi": "10.1155/2021/3236384", "pmid": "34708131", "authors": ["Linhui Wang", "Yutao Wang", "Jianfeng Wang", "Luanfeng Li", "Jianbin Bi"], "journal": "Journal of immunology research", "year": 2021, "abstract": "BACKGROUND: Bladder cancer is the tenth most common cancer worldwide. Valuable biomarkers in the field of diagnostic bladder cancer are urgently required.\nMETHOD: Here, the gene expression matrix and clinical data were obtained from The Cancer Genome Atlas (TCGA), GSE13507, GSE32894, and Mariathasan et al. Five prognostic genes were identified by the univariate, robust, and multivariate Cox's regression and were used to develop a prognosis-related model. The Kaplan-Meier survival curves and receiver operating characteristics were used to evaluate the model's effectiveness. The potential biological functions of the selected genes were analyzed using CIBERSORT and ESTIMATE algorithms. Cancer Therapeutics Response Portal (CTRP) and PRISM datasets were used to identify drugs with high sensitivity. Subsequently, using the bladder cancer (BLCA) cell lines, the role of TNFRSF14 was determined by Western blotting, cell proliferation assay, and 5-ethynyl-20-deoxyuridine assay.\nRESULTS: GSDMB, CLEC2D, APOL2, TNFRSF14, and GBP2 were selected as prognostic genes in bladder cancer patients. The model's irreplaceable reliability was validated by the training and validation cohorts. CD8+ T cells were highly infiltrated in the high-TNFRSF14-expression group, and M2 macrophages were the opposite. Higher expression of TNFRSF14 was associated with higher expression levels of LCK, interferon, MHC-I, and MHC-II, while risk score was the opposite. Many compounds with higher sensitivity for treating bladder cancer patients in the low-TNFRSF14-expression group were identified, with obatoclax being a potential drug most likely to treat patients in the low-TNFRSF14-expression group. Finally, the proliferation of BLCA cell lines was increased in the TNFRSF14-reduced group, and the differential expression was identified. TNFRSF14 plays a role in bladder cancer progression through the Wnt/β-catenin-dependent pathway. TNFRSF14 is a potential protective biomarker involved in cell proliferation in BLCA.\nCONCLUSION: We conducted a study to establish a 5-gene score model, providing reliable prediction for the outcome of bladder cancer patients and therapeutic drugs to individualize therapy. Our findings provide a signature that might help determine the optimal treatment for individual patients with bladder cancer.", "claims": [{"id": "claim-method", "statement": "Five prognostic genes were identified by the univariate, robust, and multivariate Cox's regression and were used to develop a prognosis-related model.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34708131", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "Five prognostic genes were identified by the univariate, robust, and multivariate Cox's regression and were used to develop a prognosis-related model."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The TCGA-BLCA dataset used in this study could be obtained from the TCGA database (https://cancergenome.nih.gov/). This study's two GEO datasets (GSE13507, GSE32894) were obtained from the GEO database (https://www.ncbi.nlm.nih.gov/geo/).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34708131", "locator": "sections[29] \"Data Availability\" paragraph 2", "quote": "The TCGA-BLCA dataset used in this study could be obtained from the TCGA database (https://cancergenome.nih.gov/). This study's two GEO datasets (GSE13507, GSE32894) were obtained from the GEO database (https://www.ncbi.nlm.nih.gov/geo/)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The study established a five-gene prognostic score model for predicting bladder cancer outcomes and identifying drugs to individualize therapy.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:34708131", "locator": "sections[0] \"Abstract\" paragraph 4", "quote": "We conducted a study to establish a 5-gene score model, providing reliable prediction for the outcome of bladder cancer patients and therapeutic drugs to individualize therapy."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The study established a five-gene prognostic score model for predicting bladder cancer outcomes and identifying drugs to individualize therapy.", "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:34708131"], "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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