Detection of bladder cancer using proteomic profiling of urine sediments.

We used protein expression profiles to develop a classification rule for the detection and prognostic assessment of bladder cancer in voided urine samples. Using the Ciphergen PBS II ProteinChip Reader, we analyzed the protein profiles of 18 pairs of samples of bladder tumor and adjacent urothelium...

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Autores principales:	Tadeusz Majewski, Philippe E Spiess, Jolanta Bondaruk, Peter Black, Charlotte Clarke, William Benedict, Colin P Dinney, Herbert Barton Grossman, Kuang S Tang, Bogdan Czerniak
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2012
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/91a0641e6d6a489bb56434bed4d4b4d1
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spelling	oai:doaj.org-article:91a0641e6d6a489bb56434bed4d4b4d12021-11-18T07:09:45ZDetection of bladder cancer using proteomic profiling of urine sediments.1932-620310.1371/journal.pone.0042452https://doaj.org/article/91a0641e6d6a489bb56434bed4d4b4d12012-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22879988/?tool=EBIhttps://doaj.org/toc/1932-6203We used protein expression profiles to develop a classification rule for the detection and prognostic assessment of bladder cancer in voided urine samples. Using the Ciphergen PBS II ProteinChip Reader, we analyzed the protein profiles of 18 pairs of samples of bladder tumor and adjacent urothelium tissue, a training set of 85 voided urine samples (32 controls and 53 bladder cancer), and a blinded testing set of 68 voided urine samples (33 controls and 35 bladder cancer). Using t-tests, we identified 473 peaks showing significant differential expression across different categories of paired bladder tumor and adjacent urothelial samples compared to normal urothelium. Then the intensities of those 473 peaks were examined in a training set of voided urine samples. Using this approach, we identified 41 protein peaks that were differentially expressed in both sets of samples. The expression pattern of the 41 protein peaks was used to classify the voided urine samples as malignant or benign. This approach yielded a sensitivity and specificity of 59% and 90%, respectively, on the training set and 80% and 100%, respectively, on the testing set. The proteomic classification rule performed with similar accuracy in low- and high-grade bladder carcinomas. In addition, we used hierarchical clustering with all 473 protein peaks on 65 benign voided urine samples, 88 samples from patients with clinically evident bladder cancer, and 127 samples from patients with a history of bladder cancer to classify the samples into Cluster A or B. The tumors in Cluster B were characterized by clinically aggressive behavior with significantly shorter metastasis-free and disease-specific survival.Tadeusz MajewskiPhilippe E SpiessJolanta BondarukPeter BlackCharlotte ClarkeWilliam BenedictColin P DinneyHerbert Barton GrossmanKuang S TangBogdan CzerniakPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 7, Iss 8, p e42452 (2012)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Tadeusz Majewski Philippe E Spiess Jolanta Bondaruk Peter Black Charlotte Clarke William Benedict Colin P Dinney Herbert Barton Grossman Kuang S Tang Bogdan Czerniak Detection of bladder cancer using proteomic profiling of urine sediments.
description	We used protein expression profiles to develop a classification rule for the detection and prognostic assessment of bladder cancer in voided urine samples. Using the Ciphergen PBS II ProteinChip Reader, we analyzed the protein profiles of 18 pairs of samples of bladder tumor and adjacent urothelium tissue, a training set of 85 voided urine samples (32 controls and 53 bladder cancer), and a blinded testing set of 68 voided urine samples (33 controls and 35 bladder cancer). Using t-tests, we identified 473 peaks showing significant differential expression across different categories of paired bladder tumor and adjacent urothelial samples compared to normal urothelium. Then the intensities of those 473 peaks were examined in a training set of voided urine samples. Using this approach, we identified 41 protein peaks that were differentially expressed in both sets of samples. The expression pattern of the 41 protein peaks was used to classify the voided urine samples as malignant or benign. This approach yielded a sensitivity and specificity of 59% and 90%, respectively, on the training set and 80% and 100%, respectively, on the testing set. The proteomic classification rule performed with similar accuracy in low- and high-grade bladder carcinomas. In addition, we used hierarchical clustering with all 473 protein peaks on 65 benign voided urine samples, 88 samples from patients with clinically evident bladder cancer, and 127 samples from patients with a history of bladder cancer to classify the samples into Cluster A or B. The tumors in Cluster B were characterized by clinically aggressive behavior with significantly shorter metastasis-free and disease-specific survival.
format	article
author	Tadeusz Majewski Philippe E Spiess Jolanta Bondaruk Peter Black Charlotte Clarke William Benedict Colin P Dinney Herbert Barton Grossman Kuang S Tang Bogdan Czerniak
author_facet	Tadeusz Majewski Philippe E Spiess Jolanta Bondaruk Peter Black Charlotte Clarke William Benedict Colin P Dinney Herbert Barton Grossman Kuang S Tang Bogdan Czerniak
author_sort	Tadeusz Majewski
title	Detection of bladder cancer using proteomic profiling of urine sediments.
title_short	Detection of bladder cancer using proteomic profiling of urine sediments.
title_full	Detection of bladder cancer using proteomic profiling of urine sediments.
title_fullStr	Detection of bladder cancer using proteomic profiling of urine sediments.
title_full_unstemmed	Detection of bladder cancer using proteomic profiling of urine sediments.
title_sort	detection of bladder cancer using proteomic profiling of urine sediments.
publisher	Public Library of Science (PLoS)
publishDate	2012
url	https://doaj.org/article/91a0641e6d6a489bb56434bed4d4b4d1
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Detection of bladder cancer using proteomic profiling of urine sediments.

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