ArticleTranslational cancer research2024
Development and validation of a recurrence risk assessment model for high-grade bladder cancer based on TCGA and GEO.
Article in Translational cancer research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- Development and validation of a biochemical recurrence risk assessment model for prostate cancer based on TCGA database.Translational andrology and urology · 2026Article
- Single-cell RNA-seq reveals gene expression heterogeneity in NSCLC and its link to the immune microenvironment.Discover oncology · 2025Article
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4 authors.
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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. Methods: 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 |log Results: 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. Conclusions: 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.
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