Bladder Carcinoma Data with Clinical Risk Factors and Molecular Markers: A Cluster Analysis. (19th March 2015)

• Record Type: Journal Article
• Authors: Redondo-Gonzalez, Enrique; de Castro, Leandro Nunes; Moreno-Sierra, Jesús; Maestro de las Casas, María Luisa; Vera-Gonzalez, Vicente; Ferrari, Daniel Gomes; Corchado, Juan Manuel
• Other Names: Bichindaritz Isabelle Academic Editor.
• Is Part Of: BioMed research international. Volume 2015(2015)
• Publication Date: 2015-03-19
• Subjects: Medicine -- Periodicals; Biology -- Periodicals; Biotechnology -- Periodicals; Life sciences -- Periodicals; 610.5
• DOI: 10.1155/2015/168682
• Languages: English
• ISSNs: 2314-6133

Abstract

Abstract : Bladder cancer occurs in the epithelial lining of the urinary bladder and is amongst the most common types of cancer in humans, killing thousands of people a year. This paper is based on the hypothesis that the use of clinical and histopathological data together with information about the concentration of various molecular markers in patients is useful for the prediction of outcomes and the design of treatments of nonmuscle invasive bladder carcinoma (NMIBC). A population of 45 patients with a new diagnosis of NMIBC was selected. Patients with benign prostatic hyperplasia (BPH), muscle invasive bladder carcinoma (MIBC), carcinoma in situ (CIS), and NMIBC recurrent tumors were not included due to their different clinical behavior. Clinical history was obtained by means of anamnesis and physical examination, and preoperative imaging and urine cytology were carried out for all patients. Then, patients underwent conventional transurethral resection (TURBT) and some proteomic analyses quantified the biomarkers (p53, neu, and EGFR). A postoperative follow-up was performed to detect relapse and progression. Clusterings were performed to find groups with clinical, molecular markers, histopathological prognostic factors, and statistics about recurrence, progression, and overall survival of patients with NMIBC. Four groups were found according to tumor sizes, risk of relapse or progression, and biological behavior. Outlier patients were also detected and categorized according to their clinical characters and biological behavior.