A glycosylation risk score comprehensively assists the treatment of bladder neoplasm in the real-world cohort, including the tumor microenvironment, molecular and clinical prognosis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37818187", "kind": "science-record", "revision": 3, "content_hash": "sha256:adad1ce0d44c6433d3394d33136b5066f369afc9d7444224b22ee4dd05b49dda", "created_at": "2026-11-01T19:02:40Z", "updated_at": "2026-11-01T22:10:26Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "A glycosylation risk score comprehensively assists the treatment of bladder neoplasm in the real-world cohort, including the tumor microenvironment, molecular and clinical prognosis.", "doi": "10.3389/fphar.2023.1280428", "pmid": "37818187", "authors": ["Jinhui Liu", "Yunbo He", "Weimin Zhou", "Zhuoming Tang", "Zicheng Xiao"], "journal": "Frontiers in pharmacology", "year": 2023, "abstract": "Background: Bladder cancer is a common urological cancer associated high significant morbidity and mortality rates. Immunotherapy has emerged as a promising treatment option, although response rates vary among patients. Glycosylation has been implicated in tumorigenesis and immune regulation. However, our current comprehensive understanding of the role of glycosylation in bladder cancer and its clinical implications is limited. Methods: We constructed a training cohort based on the downloaded TCGA-BLCA dataset, while additional datasets (Xiangya cohort, GSE32894, GSE48075, GSE31684, GSE69795 and E-MTAB-1803) from Xiangya hospital, GEO and ArrayExpress database were obtained and used as validation cohorts. To identify glycosylation-related genes associated with prognosis, univariate Cox regression and LASSO regression were performed. A Cox proportional hazards regression model was then constructed to develop a risk score model. The performance of the risk score was assessed in the training cohort using Kaplan-Meier survival curves and ROC curves, and further validated in multiple validation cohorts. Results: We classified patients in the training cohort into two groups based on glycosylation-related gene expression patterns: Cluster 1 and Cluster 2. Prognostic analysis revealed that Cluster 2 had poorer survival outcomes. Cluster 2 also showed higher levels of immune cell presence in the tumor microenvironment and increased activation in key steps of the cancer immune response cycle. We developed an independent prognostic risk score (p < 0.001) and used it to construct an accurate prognostic prediction nomogram. The high glycosylation risk score group exhibited higher tumor immune cell infiltration, enrichment scores in immune therapy-related pathways, and a tendency towards a basal subtype. Conversely, the low-risk score group had minimal immune cell infiltration and tended to have a luminal subtype. These findings were consistent in our real-world Xiangya cohort. Conclusion: This multi-omics glycosylation score based on these genes reliably confirmed the heterogeneity of bladder cancer tumors, predicted the efficacy of immunotherapy and molecular subtypes, optimizing individual treatment decisions.", "claims": [{"id": "claim-method", "statement": "Finally, the glycosylation risk score was constructed using the Cox proportional hazard regression model with the “glmnet” R package, incorporating 20 genes.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37818187", "locator": "sections[8] \"Development of glycosylation risk score\" paragraph 2", "quote": "Finally, the glycosylation risk score was constructed using the Cox proportional hazard regression model with the “glmnet” R package, incorporating 20 genes."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37818187", "locator": "sections[21] \"Data availability statement\" paragraph 2", "quote": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The paper developed an independent prognostic risk score (p < 0.001) and used it to construct an accurate prognostic prediction nomogram.", "kind": "quantitative", "target": {"value": 0.001, "unit": "p-value", "tolerance": null, "source": "We developed an independent prognostic risk score (p < 0.001) and used it to construct an accurate prognostic prediction nomogram."}, "evidence": [{"asset": "source:pmid:37818187", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "We developed an independent prognostic risk score (p < 0.001) and used it to construct an accurate prognostic prediction nomogram."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The paper developed an independent prognostic risk score (p < 0.001) and used it to construct an accurate prognostic prediction nomogram.", "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:37818187"], "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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