Urothelial cancer proteomics provides both prognostic and functional information. Issue 1 (December 2017)

• Record Type: Journal Article
• Authors: Velasco, Guillermo; Trilla-Fuertes, Lucia; Gamez-Pozo, Angelo; Urbanowicz, Maria; Ruiz-Ares, Gustavo; Sepúlveda, Juan; Prado-Vazquez, Guillermo; Arevalillo, Jorge; Zapater-Moros, Andrea; Navarro, Hilario; Lopez-Vacas, Rocio; Manneh, Ray; Otero, Irene; Villacampa, Felipe; Paramio, Jesus; Vara, Juan; Castellano, Daniel
• Is Part Of: Scientific reports. Volume 7:Issue 1(2017)
• Page Start: 1
• Page End: 8
• Publication Date: 2017-12
• Subjects: Natural history -- Research -- Periodicals; Biology -- Research -- Periodicals; Physical sciences -- Research -- Periodicals; Earth sciences -- Research -- Periodicals; Environmental sciences -- Research -- Periodicals; 502.85
• DOI: 10.1038/s41598-017-15920-6
• Languages: English
• ISSNs: 2045-2322

Abstract

Abstract Traditionally, bladder cancer has been classified based on histology features. Recently, some works have proposed a molecular classification of invasive bladder tumors. To determine whether proteomics can define molecular subtypes of muscle invasive urothelial cancer (MIUC) and allow evaluating the status of biological processes and its clinical value. 58 MIUC patients who underwent curative surgical resection at our institution between 2006 and 2012 were included. Proteome was evaluated by high-throughput proteomics in routinely archive FFPE tumor tissue. New molecular subgroups were defined. Functional structure and individual proteins prognostic value were evaluated and correlated with clinicopathologic parameters. 1, 453 proteins were quantified, leading to two MIUC molecular subgroups. A protein-based functional structure was defined, including several nodes with specific biological activity. The functional structure showed differences between subtypes in metabolism, focal adhesion, RNA and splicing nodes. Focal adhesion node has prognostic value in the whole population. A 6-protein prognostic signature, associated with higher risk of relapse (5 year DFS 70% versus 20%) was defined. Additionally, we identified two MIUC subtypes groups. Prognostic information provided by pathologic characteristics is not enough to understand MIUC behavior. Proteomics analysis may enhance our understanding of prognostic and classification. These findings can lead to improving diagnosis and treatment selection in these patients.