ArticleTranslational andrology and urology2024
An 11-gene glycosyltransferases-related model for the prognosis of patients with bladder urothelial carcinoma: development and validation based on TCGA and GEO datasets.
Article in Translational andrology and urology, 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.
- An O-glycosylation-based prognostic signature for biochemical recurrence in prostate cancer identifiesTranslational andrology and urology · 2026Article
- FOXQ1 activates GLT8D2 to enhance CCL2 N-glycosylation and promote prostate cancer bone metastasis.Cell death & disease · 2026Article
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7 authors.
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Abstract
Background: Bladder urothelial carcinoma (BLCA) is a highly heterogeneous cancer with a wide range of prognoses, ranging from low-grade non-muscle-invasive bladder cancer (NMIBC), which has a good prognosis but a high recurrence rate, to high-grade muscle-invasive bladder cancer (MIBC), which has a poor prognosis. Glycosylation dysregulation plays a significant role in cancer development. Therefore, this study aimed to investigate the role of glycosyltransferases (GT)-related genes in the prognosis of BLCA and to develop a prognostic model based on these genes to predict overall survival (OS) and assess its clinical application. Methods: The Cancer Genome Atlas (TCGA)-BLCA dataset, comprising 411 tumor and 19 normal samples. The validation set, GSE13507 from the Gene Expression Omnibus (GEO) database, included 165 primary bladder cancer samples with survival data. Differentially expressed GT-related genes (DEGRGs) in BLCA were identified in the training set. Predictive DEGRGs were used to construct risk score models by univariate Cox regression, least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression. The predictive value of the models was assessed using Kaplan-Meier survival analysis and receiver operating characteristic (ROC) analysis in the training and validation sets. A nomogram was developed and its performance was evaluated with calibration curves. In addition, the relationship between the risk score and the tumor immune microenvironment was explored, and tumor immune dysfunction score (TIDE) and immune signature scores were used to predict the response to immunotherapy in BLCA patients. Results: Thirty-three DEGRGs were identified in the comparison of BLCA patients with control samples. A risk score model was constructed based on 11 of these genes ( Conclusions: The 11 GT-related genes identified were associated with OS in BLCA patients, suggesting that the model has potential predictive value. At the same time, further research is needed to explore its role in clinical practice.
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