Bladder carcinoma (BLCA) represents a common urinary tract malignancy, characterized by aggressive behavior and high recurrence rates. The biological response regulation during tumor proliferation and metastasis is intimately associated with liquid-liquid phase separation (LLPS). For the purpose of enhancing early detection and treatment, this study employed transcriptomic data to examine the prognostic implications of LLPS-associated genes and formulate a predictive model. Clinical and transcriptomic data of bladder cancer patients were sourced from the GEO and TCGA databases. This study applied a clustering algorithm using non-negative matrix factorization (NMF) to classify samples, which were systematically compared based on their liquid-liquid phase separation characteristics. Prognostic models were developed using multivariate Cox regression and the Least Absolute Shrinkage Selection Operator (LASSO) algorithm to establish risk formulas for nine genes. The gene signature’s validity was tested across the entire TCGA cohort (406 cases), the TCGA testing cohort (120 cases), and the external validation dataset GSE13507. The predictive accuracy of the signature system was assessed using receiver operating characteristic (ROC) and Kaplan-Meier curves. Additionally, decision curve analysis incorporating clinicopathological parameters and the genetic signature was employed to predict individual survival. This study identified two distinct molecular subtypes, C1 and C2. Patients with the C1 subtype exhibited significantly better prognoses than those with the C2 subtype. Low-risk group patients consistently showed superior prognoses compared to high-risk groups across the entire TCGA, GEO, and TCGA training cohorts. Furthermore, the LLPS-related gene model demonstrated prognostic value independent of other clinical traits. This study identifies LLPS-associated gene clusters and establishes an independent, accurate prognostic model for BLCA. The model holds potential for clinical application in BLCA prognosis assessment.
© 2024. The Author(s).