A Four-Gene Signature Predicts Disease Progression in Muscle Invasive Bladder Cancer View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2011-05

AUTHORS

Wun-Jae Kim, Seon-Kyu Kim, Pildu Jeong, Seok-Joong Yun, In-Chang Cho, Isaac Yi Kim, Sung-Kwon Moon, Hong-Duck Um, Yung Hyun Choi

ABSTRACT

There are no reliable criteria to handle disease progression of muscle invasive bladder cancer (MIBC), which strongly influences patient survival. Therefore, an accurate predicting method to identify progressive MIBC patients is greatly needed. The aim of this study was to identify a genetic signature associated with disease progression in MIBC. To address this issue, we analyzed three independent cohorts (a training set, test set 1 and test set 2) comprising a total of 128 MIBC patients. Microarray gene expression profiling, including gene network analysis, was performed in the training set to identify a gene expression signature associated with disease progression. The prognostic value of the signature was validated in test set 1 and test set 2 by microarray and real-time reverse transcriptase polymerase chain reaction (RT-PCR), respectively. The determination of gene expression patterns by microarray data analysis identified 1,320 genes associated with disease progression. Gene network analysis of the 1,320 genes suggested that IL1B, S100A8, S100A9 and EGFR were important mediators of MIBC progression. We validated this putative four-gene signature in two independent cohorts (log-rank test, P < 0.05 each, respectively) and estimated the predictive value of the signature by multivariate Cox regression analysis (hazard ratio [HR], 6.24; 95% confidence interval [CI], 1.58-24.61; P = 0.009). Finally, signature-based stratification demonstrated that the four-gene signature was an independent predictor of MIBC progression. In conclusion, a molecular signature defined by four genes represents a promising diagnostic tool for the identification of MIBC patients at high risk of progression. More... »

PAGES

478-485

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.2119/molmed.2010.00274

DOI

http://dx.doi.org/10.2119/molmed.2010.00274

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1068965349

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/21308147


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