The prognostic significance and clinical relevance of stem cell characteristic in bladder cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38333832", "kind": "science-record", "revision": 4, "content_hash": "sha256:7c248c5418547be3178fdd56843d66c677d403430ed533c51efd995ba92a781b", "created_at": "2026-11-01T19:20:16Z", "updated_at": "2026-11-01T22:53:39Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "The prognostic significance and clinical relevance of stem cell characteristic in bladder cancer.", "doi": "10.1016/j.heliyon.2024.e24858", "pmid": "38333832", "authors": ["Xia Chen", "Yuanyuan Yin", "Yuming He", "Fanqi Meng", "Jing Zhao", "Fang Liu", "Yu Xu", "Guoqiang Wang", "Xin Zhu", "Sijia Ma", "Huafei Lu", "Shangli Cai", "Yanping Song", "Junyong Dai"], "journal": "Heliyon", "year": 2024, "abstract": "Background: Bladder cancer (BLCA) is a common malignant tumor of urinary system and prognostic biomarkers are needed for better clinical decision-making and patient management. Cancer stem cells (CSCs) are involved in carcinogenesis, development, metastasis and recurrence of BLCA. This study explored the prognostic and predictive value of CSCs-related genes and laid the groundwork for precision treatment development in BLCA.\nMethods: The mRNA data and corresponding clinical information obtained from TCGA-BLCA cohort was used to discover biomarkers and develop CSCs-related prognostic model, which was further validated in GSE32548 and GSE32894 datasets. In addition, the association between CSCs-related risk score and therapeutic efficacy was analyzed to explore the potential predictive value of the prognostic model.\nResults: We identified four CSCs-related subtypes and 900 differentially expressed genes (DEGs) among subtypes. Then the CSCs-related prognostic model was built based on 16 CSCs-related DEGs with the most significant prognostic value. Patients in the low-risk group had better overall survival than those in high-risk group (P < 0.001; HR, 0.42; 95 %CI, 0.31-0.57). Multivariable Cox analysis in training and test sets confirmed the independence of CSCs-related risk score as a prognostic factor (P < 0.05). The difference of survival between two risk groups were probably due to the significantly varied immune microenvironment based on the analysis of infiltrated immune cells. Additionally, the risk score was significantly associated with chemotherapy sensitivity and the response to anti-PD-L1 therapy (P < 0.05) which suggested a potential predictive value of CSCs-related risk model.\nConclusion: We established a risk classifier based on 16 CSCs-related genes for predicting survival in patients with BLCA. The CSCs-related risk model has both prognostic value and potential predictive value for therapeutic efficacy, which brings us closer to understanding the important role of CSCs in BLCA and may provide guidance for clinical treatment decision-making and patient management. The clinical utility of the CSCs-related risk classifier warrants further studies.", "claims": [{"id": "claim-method", "statement": "The OS between patients in different risk group was compared by Kaplan-Meier survival analysis and log-rank test.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38333832", "locator": "sections[6] \"Construction and validation of the CSCs-related prognostic model\" paragraph 2", "quote": "The OS between patients in different risk group was compared by Kaplan-Meier survival analysis and log-rank test."}], "state": "complete"}, {"id": "claim-data-access", "statement": "All data used are from public databases including TCGA database (https://tcga-data.nci.nih.gov/tcga/), UCSC XENA database (https://xenabrowser.net/datapages/), GEO database (https://www.ncbi.nlm.nih.gov/geo/), IMvigor210 cohort, MSigDB (https://www.gsea-msigdb.org/gsea/msigdb/), and GDSC database (https://www.cancerrxgene.org/), and the sources have all been noted in this paper.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38333832", "locator": "sections[21] \"Data availability\" paragraph 2", "quote": "All data used are from public databases including TCGA database (https://tcga-data.nci.nih.gov/tcga/), UCSC XENA database (https://xenabrowser.net/datapages/), GEO database (https://www.ncbi.nlm.nih.gov/geo/), IMvigor210 cohort, MSigDB (https://www.gsea-msigdb.org/gsea/msigdb/), and GDSC database (https://www.cancerrxgene.org/), and the sources have all been noted in this paper."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Patients in the low-risk group had better overall survival than those in high-risk group (P < 0.001; HR, 0.42; 95 %CI, 0.31–0.57).", "kind": "quantitative", "target": {"value": 95.0, "unit": "per cent", "tolerance": null, "source": "Patients in the low-risk group had better overall survival than those in high-risk group (P < 0.001; HR, 0.42; 95 %CI, 0.31–0.57)."}, "evidence": [{"asset": "source:pmid:38333832", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Patients in the low-risk group had better overall survival than those in high-risk group (P < 0.001; HR, 0.42; 95 %CI, 0.31–0.57)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Patients in the low-risk group had better overall survival than those in high-risk group (P < 0.001; HR, 0.42; 95 %CI, 0.31–0.57).", "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:38333832"], "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:GSE32548"], "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:GSE32548"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:38333832", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1016/j.heliyon.2024.e24858", "sha256": 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A run that reproduces the effect is judged against this threshold."}], "gaps": ["method.reader_differs_from_extractor"]}, "capability": {"state": "complete", "requirements": [{"capability_id": "capability:computational_analysis", "label": "Computational analysis platform", "assay_families": ["meta_analysis", "machine_learning", "survival_analysis", "statistical_regression", "pca", "molecular_docking", "network_analysis", "differential_expression", "gwas"], "availability_state": "owned", "tier_unlocked": "R1", "candidate": "Automated Assays Laravel application and simulated adapters"}], "gaps": []}, "readiness": {"state": "complete", "tier": "R1", "blocking_axis": "none", "blocked_on": "nothing:replicable_now", "axes": {"method": {"tier": "R1", "expressible": true, "required_operations": ["survival_km"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: kaplan_meier."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. 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