Advancing bladder cancer management: development of a prognostic model and personalized therapy.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39104534", "kind": "science-record", "revision": 2, "content_hash": "sha256:f11abf5864d267d701e9d70204c297d1b46c0243ae5c9c23ad1f22a466176fbf", "created_at": "2026-11-01T20:29:11Z", "updated_at": "2026-11-01T21:33:36Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Advancing bladder cancer management: development of a prognostic model and personalized therapy.", "doi": "10.3389/fimmu.2024.1430792", "pmid": "39104534", "authors": ["Xiang Huang", "Guotu Du", "Ying Yang", "Peng Su", "Shicheng Chen", "Chongjiong Cai", "Tianyu Huang", "Yu Zeng", "Yonggang Tao", "Demei Tian", "Neng Zhang"], "journal": "Frontiers in immunology", "year": 2024, "abstract": "Background: Bladder cancer (BLCA) was recognized as a significant public health challenge due to its high incidence and mortality rates. The influence of molecular subtypes on treatment outcomes was well-acknowledged, necessitating further exploration of their characterization and application. This study was aimed at enhancing the understanding of BLCA by mapping its molecular heterogeneity and developing a robust prognostic model using single-cell and bulk RNA sequencing data. Additionally, immunological characteristics and personalized treatment strategies were investigated through the risk score.\nMethods: Single-cell RNA sequencing (scRNA-seq) data from GSE135337 and bulk RNA-seq data from several sources, including GSE13507, GSE31684, GSE32894, GSE69795, and TCGA-BLCA, were utilized. Molecular subtypes, particularly the basal-squamous (Ba/Sq) subtype associated with poor prognosis, were identified. A prognostic model was constructed using LASSO and Cox regression analyses focused on genes linked with the Ba/Sq subtype. this model was validated across internal and external datasets to ensure predictive accuracy. High- and low-risk groups based on the risk score derived from TCGA-BLCA data were analyzed to examine their immune-related molecular profiles and treatment responses.\nResults: Six molecular subtypes were identified, with the Ba/Sq subtype being consistently associated with poor prognosis. The prognostic model, based on basal-squamous subtype-related genes (BSSRGs), was shown to have strong predictive performance across diverse clinical settings with AUC values at 1, 3, and 5 years indicating robust predictability in training, testing, and entire datasets. Analysis of the different risk groups revealed distinct immune infiltration and microenvironments. Generally higher tumor mutation burden (TMB) scores and lower tumor immune dysfunction and exclusion (TIDE) scores were exhibited by the low-risk group, suggesting varied potentials for systemic drug response between the groups. Finally, significant differences in potential systemic drug response rates were also observed between risk groups.\nConclusions: The study introduced and validated a new prognostic model for BLCA based on BSSRGs, which was proven effective in prognosis prediction. The potential for personalized therapy, optimized by patient stratification and immune profiling, was highlighted by our risk score, aiming to improve treatment efficacy. This approach was promised to offer significant advancements in managing BLCA, tailoring treatments based on detailed molecular and immunological insights.", "claims": [{"id": "claim-method", "statement": "A prognostic model was constructed using LASSO and Cox regression analyses focused on genes linked with the Ba/Sq subtype.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39104534", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "A prognostic model was constructed using LASSO and Cox regression analyses focused on genes linked with the Ba/Sq subtype."}], "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:39104534", "locator": "sections[19] \"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 prognostic model demonstrated strong predictive performance, with 1-, 3-, and 5-year AUC values of 0.732, 0.749, and 0.787 in the training set, 0.744, 0.684, and 0.742 in the testing set, and 0.737, 0.725, and 0.759 in the entire dataset.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:39104534", "locator": "sections[14] \"Prognostic model development for BLCA\" paragraph 6", "quote": "ROC curves were used to assess the predictive accuracy of the constructed prognostic model, yielding AUC values for 1-, 3-, and 5-year overall survivals in the training set: 0.732, 0.749, and 0.787 ( Figure 8A ). In the testing set, corresponding AUC values were 0.744, 0.684, and 0.742 ( Figure 8B ), while in the entire dataset, they were 0.737, 0.725, and 0.759, respectively ( Figure 8C )."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The prognostic model demonstrated strong predictive performance, with 1-, 3-, and 5-year AUC values of 0.732, 0.749, and 0.787 in the training set, 0.744, 0.684, and 0.742 in the testing set, and 0.737, 0.725, and 0.759 in the entire dataset.", "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:39104534"], "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:39104534", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fimmu.2024.1430792", "sha256": "sha256:7480d56780a8c138c850ff2f0b8187812f9e8df049650d966346b55d6876f57b", "bytes": 62007, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fimmu.2024.1430792", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC11298345", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE13507", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE13507", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE13507", "version": null, "note": "The datasets presented in this study can be found in online repositories. 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