Expression signature of E2F1 and its associated genes predict superficial to invasive progression of bladder tumors

Cited 243 time in scopus
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Title
Expression signature of E2F1 and its associated genes predict superficial to invasive progression of bladder tumors
Author(s)
J S Lee; Sun Hee Leem; Sang-Yeop Lee; S C Kim; E S Park; S B Kim; Seon-Kyu Kim; Y J Kim; W J Kim; In-Sun Chu
Bibliographic Citation
Journal of Clinical Oncology, vol. 28, no. 6, pp. 2660-2667
Publication Year
2010
Abstract
PURPOSE: In approximately 20% of patients with superficial bladder tumors, the tumors progress to invasive tumors after treatment. Current methods of predicting the clinical behavior of these tumors prospectively are unreliable. We aim to identify a molecular signature that can reliably identify patients with high-risk superficial tumors that are likely to progress to invasive tumors. PATIENTS AND METHODS: Gene expression data were collected from tumor specimens from 165 patients with bladder cancer. Various statistical methods, including leave-one-out cross-validation methods, were applied to identify a gene expression signature that could predict the likelihood of progression to invasive tumors and to test the robustness of the expression signature in an independent cohort. The robustness of the gene expression signature was validated in an independent (n = 353) cohort. RESULTS: Supervised analysis of gene expression data revealed a gene expression signature that is strongly associated with invasive bladder tumors. A molecular classifier based on this gene expression signature correctly predicted the likelihood of progression of superficial tumor to invasive tumor. CONCLUSION: We present a molecular signature that can predict, at diagnosis, the likelihood of bladder cancer progression and, possibly, lead to improvements in patient therapy.
ISSN
0732-183X
Publisher
Amer Soc Clinical Oncology
DOI
http://dx.doi.org/10.1200/JCO.2009.25.0977
Type
Article
Appears in Collections:
Aging Convergence Research Center > 1. Journal Articles
Division of Biomedical Research > Metabolic Regulation Research Center > 1. Journal Articles
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