Development and validation of a recurrence risk assessment model for high-grade bladder cancer based on TCGA and GEO.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39430850", "kind": "science-record", "revision": 3, "content_hash": "sha256:29c8f958757f041b04ad2ad3356fd3a0dadc989c2b131f25ddecf21b5b72fbfb", "created_at": "2026-11-01T20:29:41Z", "updated_at": "2026-11-01T22:12:59Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Development and validation of a recurrence risk assessment model for high-grade bladder cancer based on TCGA and GEO.", "doi": "10.21037/tcr-24-256", "pmid": "39430850", "authors": ["Hongxin Wang", "Yuping Zheng", "Cheng Zhang", "Mingshan Li"], "journal": "Translational cancer research", "year": 2024, "abstract": "Background: Bladder cancer is one of the most commonly diagnosed urinary cancers worldwide. Although muscle-invasive bladder cancer (MIBC) accounts for only 25% of bladder cancer cases, it has a high recurrence rate and poor prognosis, especially among high-grade cases. Despite the existence of some molecular markers, there is a clear clinical need for a robust recurrence prediction model that can assist in patient management and therapeutic decision-making. Therefore, we aimed to use public databases to develop such an effective assessment model.\nMethods: We developed a recurrence risk assessment model for high-grade bladder cancer based on the clinical information of 217 cases from The Cancer Genome Atlas (TCGA) and profiles of 87 samples from GSE31684 in the Gene Expression Omnibus (GEO) database. Edge R was used to analyze differences between RNAs of bladder cancer in the TCGA database, with thresholds of P<0.05 and |log2(fold change)| >1; least absolute shrinkage and selection operator (LASSO) Cox regression models were used to screen the RNAs significantly related to recurrence with minimum λ. Survival receiver operating characteristic (ROC) and area under the curve (AUC) was used to assess the predictive accuracy of the model in the training and validation sets of GSE31684.\nResults: There were 2,876 differential RNAs obtained from TCGA data. Among a total of 284 RNAs identified as significantly related to recurrence of bladder cancer, 49 were obtained by LASSO regression, and 30 were finally obtained by multifactor risk regression to construct a risk assessment model. The model was found to predict the prognosis of bladder cancer recurrence well, with an AUC of 0.911 in the TCGA training set and an adjusted AUC value of 0.839 in the GEO validation set.\nConclusions: The recurrence assessment model is a relatively accurate recurrence prediction tool for high-grade bladder cancer and could provide a guidance for the treatment of bladder cancer.", "claims": [{"id": "claim-method", "statement": "least absolute shrinkage and selection operator (LASSO) Cox regression models were used to screen the RNAs significantly related to recurrence with minimum λ.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39430850", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "least absolute shrinkage and selection operator (LASSO) Cox regression models were used to screen the RNAs significantly related to recurrence with minimum λ."}], "state": "complete"}, {"id": "claim-data-access", "statement": "We downloaded The Cancer Genome Atlas (TCGA) RNA expression profile data of bladder cancer in the form of count value from Xena (https://xena.ucsc.edu/). The corresponding clinical information files including gender, age, tumor differentiation, and tumor stage were also downloaded. Only files containing recurrence-free time and recurrence status were included in further data analysis.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39430850", "locator": "sections[4] \"Data download and preprocessing\" paragraph 2", "quote": "We downloaded The Cancer Genome Atlas (TCGA) RNA expression profile data of bladder cancer in the form of count value from Xena (https://xena.ucsc.edu/). The corresponding clinical information files including gender, age, tumor differentiation, and tumor stage were also downloaded. Only files containing recurrence-free time and recurrence status were included in further data analysis."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The model predicts bladder cancer recurrence well, with an AUC of 0.911 in the TCGA training set and an adjusted AUC value of 0.839 in the GEO validation set.", "kind": "quantitative", "target": {"value": 0.911, "unit": "auc", "tolerance": null, "source": "The model was found to predict the prognosis of bladder cancer recurrence well, with an AUC of 0.911 in the TCGA training set and an adjusted AUC value of 0.839 in the GEO validation set."}, "evidence": [{"asset": "source:pmid:39430850", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "The model was found to predict the prognosis of bladder cancer recurrence well, with an AUC of 0.911 in the TCGA training set and an adjusted AUC value of 0.839 in the GEO validation set."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The model predicts bladder cancer recurrence well, with an AUC of 0.911 in the TCGA training set and an adjusted AUC value of 0.839 in the GEO validation set.", "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:39430850"], "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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