Clinical significance and immune landscape of angiogenesis-related genes in bladder cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37988196", "kind": "science-record", "revision": 3, "content_hash": "sha256:5b4a08c31b71bcc99574db2790fb6663e8102e9c6b6ff47c47c8a49f07198ac9", "created_at": "2026-11-01T19:02:54Z", "updated_at": "2026-11-01T22:10:42Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Clinical significance and immune landscape of angiogenesis-related genes in bladder cancer.", "doi": "10.18632/aging.205222", "pmid": "37988196", "authors": ["Gang Liu", "Tingting Zhang", "Dingwen Gui", "Qin Liu"], "journal": "Aging", "year": 2023, "abstract": "BACKGROUND: Angiogenesis is a major promotor of tumor progression and metastasis. Nevertheless, it is undetermined how angiogenesis-related genes (ARGs) influence bladder cancer.\nMETHODS: The profiles of bladder cancer gene expression were collected from the TCGA-BLCA cohort. The LASSO regression analysis was used to build an angiogenesis-related signature (ARG_score) with the prognostic ARGs. Verification analyses were conducted across the GSE48075 dataset to demonstrate the robustness of the signature. Differences between the two risk groups based on clinical outcomes, immune landscape, mutation status, chemotherapeutic effectiveness for anticancer drugs, and immunotherapy efficacy were analyzed. A nomogram was developed to improve the clinical efficacy of this predictive tool. The expression levels of model genes in normal bladder epithelial cell lines (SV-HUC-1) and bladder cancer cell lines (T24 and 5637) were detected by qRT-PCR assay.\nRESULTS: Four angiogenesis-associated gene signature was constructed based on the LASSO regression algorithm. The signature showed independent risk factors of overall survival for bladder cancer, validated using two external survival datasets. Additionally, we built a prognostic nomogram to improve the practicality of the ARG_score. High-risk individuals showed stronger immunocyte infiltration, immune-related functions, elevated expression of immune checkpoints, reduced TIDE score, and higher combined IPS-PD-1 and IPS-CTLA4 scores, suggesting a heightened responsiveness to immune checkpoint inhibitors. Furthermore, patients with low and high risk showed distinct responsiveness to anticancer drugs. The expression levels of 5 model genes (COL5A2, JAG1, MSX1, OLR1, and STC) were significantly increased in bladder cancer cell lines (T24 and 5637) compared with the normal bladder epithelial cell line SV-HUC-1.\nCONCLUSIONS: The model constructed based on ARGs may have wide application in predicting outcomes and therapeutic responses.", "claims": [{"id": "claim-method", "statement": "Moreover, the TCGA dataset underwent LASSO Cox regression analysis to build a predictive risk score model based on ARGs.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37988196", "locator": "sections[4] \"Establishment and verification of ARG signature\" paragraph 2", "quote": "Moreover, the TCGA dataset underwent LASSO Cox regression analysis to build a predictive risk score model based on ARGs."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data used to support the results of this study can be obtained from the Gene Expression Omnibus (GEO) and Cancer Genome Atlas (TCGA) database.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37988196", "locator": "sections[11] \"Data availability statement\" paragraph 2", "quote": "The data used to support the results of this study can be obtained from the Gene Expression Omnibus (GEO) and Cancer Genome Atlas (TCGA) database."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Four angiogenesis-associated gene signature was constructed based on the LASSO regression algorithm.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37988196", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Four angiogenesis-associated gene signature was constructed based on the LASSO regression algorithm."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Four angiogenesis-associated gene signature was constructed based on the LASSO regression algorithm.", "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:37988196"], "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:GSE13507"], "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:GSE13507"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:37988196", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.18632/aging.205222", "sha256": 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