Chapter title |
Urinary Protein Markers for the Detection and Prognostication of Urothelial Carcinoma
|
---|---|
Chapter number | 19 |
Book title |
Urothelial Carcinoma
|
Published in |
Methods in molecular biology, January 2018
|
DOI | 10.1007/978-1-4939-7234-0_19 |
Pubmed ID | |
Book ISBNs |
978-1-4939-7233-3, 978-1-4939-7234-0
|
Authors |
Tibor Szarvas, Péter Nyirády, Osamu Ogawa, Hideki Furuya, Charles J. Rosser, Takashi Kobayashi, Szarvas, Tibor, Nyirády, Péter, Ogawa, Osamu, Furuya, Hideki, Rosser, Charles J., Kobayashi, Takashi |
Abstract |
Bladder cancer diagnosis and surveillance is mainly based on cystoscopy and urine cytology. However, both methods have significant limitations; urine cytology has a low sensitivity for low-grade tumors, while cystoscopy is uncomfortable for the patients. Therefore, in the last decade urine analysis was the subject of intensive research resulting in the identification of many potential biomarkers for the detection, surveillance, or prognostic stratification of bladder cancer. Current trends move toward the development of multiparametric models to improve the diagnostic accuracy compared with single molecular markers. Recent technical advances for high-throughput and more sensitive measurements have led to the development of multiplex assays showing potential for more efficient tools toward future clinical application. In this review, we focus on the findings of urinary protein research in the context of detection and prognostication of bladder cancer. Furthermore, we provide an up-to-date overview on the recommendations for the quality evaluation of published studies as well as for the conduction of future urinary biomarker studies. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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Unknown | 16 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
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Student > Master | 2 | 13% |
Researcher | 2 | 13% |
Student > Ph. D. Student | 2 | 13% |
Student > Doctoral Student | 1 | 6% |
Student > Bachelor | 1 | 6% |
Other | 2 | 13% |
Unknown | 6 | 38% |
Readers by discipline | Count | As % |
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Agricultural and Biological Sciences | 2 | 13% |
Engineering | 1 | 6% |
Unknown | 6 | 38% |