ArticleTranslational andrology and urology2026
Identification of RNA processing-related gene-based bladder cancer subtypes for prognosis and immune landscape assessment.
Article in Translational andrology and urology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Bladder cancer (BLCA) is a clinically complex malignancy characterized by high heterogeneity and recurrence, posing significant challenges for patient management. Growing evidence implicates dysregulated RNA processing, a crucial layer of gene expression control, as a key driver of BLCA pathogenesis and progression, highlighting its potential as a therapeutic vulnerability. This study aims to identify novel molecular subtypes based on RNA processing-related genes (RPRGs) and construct a robust prognostic risk model to improve survival prediction and personalize treatment strategies, particularly in the context of the immune landscape and immunotherapy response. Methods: We analyzed BLCA data from The Cancer Genome Atlas (TCGA) (training cohort, N=394) and Gene Expression Omnibus (GEO) (validation cohort, GSE32894, N=224). Using RPRGs from the Molecular Signatures Database (MSigDB), we identified prognostic genes via univariate Cox regression and performed molecular subtyping with the non-negative matrix factorization (NMF) algorithm. A prognostic model was constructed using least absolute shrinkage and selection operator (LASSO) Cox regression ("glmnet" package), and its performance was validated using receiver operating characteristic (ROC) analysis (timeROC). Immune infiltration was assessed via single-sample gene set enrichment analysis (ssGSEA), ESTIMATE, and CIBERSORT, while functional enrichment was analyzed using GSEA and clusterProfiler. Drug sensitivity was predicted using the CellMiner and DGIdb databases, along with the "pRRophetic" package. Results: Using RPRGs, we stratified BLCA into two molecular subtypes and developed an 8-gene prognostic model via LASSO Cox regression. The model effectively stratified patients into high-risk (poor prognosis) and low-risk (favorable prognosis) groups in both TCGA and GEO cohorts [area under the curve (AUC) >0.71]. High-risk group displayed immunosuppressive traits [e.g., elevated Tumor Immune Dysfunction and Exclusion (TIDE) score, M2 macrophage enrichment] and reduced immunotherapy response, while low-risk group showed elevated tumor mutation burden (TMB) and CD8 Conclusions: Based on RPRGs, this study establishes a novel risk model that effectively stratifies BLCA patients, not only predicting prognosis but also revealing its close association with the tumor microenvironment and immunotherapy response, providing a new tool for personalized treatment.
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