Molecular subtyping of bladder cancer using Kohonen self-organizing maps.
Cancer Med
; 3(5): 1225-34, 2014 Oct.
Article
em En
| MEDLINE
| ID: mdl-25142434
ABSTRACT
Kohonen self-organizing maps (SOMs) are unsupervised Artificial Neural Networks (ANNs) that are good for low-density data visualization. They easily deal with complex and nonlinear relationships between variables. We evaluated molecular events that characterize high- and low-grade BC pathways in the tumors from 104 patients. We compared the ability of statistical clustering with a SOM to stratify tumors according to the risk of progression to more advanced disease. In univariable analysis, tumor stage (log rank P = 0.006) and grade (P < 0.001), HPV DNA (P < 0.004), Chromosome 9 loss (P = 0.04) and the A148T polymorphism (rs 3731249) in CDKN2A (P = 0.02) were associated with progression. Multivariable analysis of these parameters identified that tumor grade (Cox regression, P = 0.001, OR.2.9 (95% CI 1.6-5.2)) and the presence of HPV DNA (P = 0.017, OR 3.8 (95% CI 1.3-11.4)) were the only independent predictors of progression. Unsupervised hierarchical clustering grouped the tumors into discreet branches but did not stratify according to progression free survival (log rank P = 0.39). These genetic variables were presented to SOM input neurons. SOMs are suitable for complex data integration, allow easy visualization of outcomes, and may stratify BC progression more robustly than hierarchical clustering.
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Texto completo:
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Base de dados:
MEDLINE
Assunto principal:
Neoplasias da Bexiga Urinária
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Redes Neurais de Computação
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Modelos Biológicos
Tipo de estudo:
Etiology_studies
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Prognostic_studies
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Risk_factors_studies
Limite:
Adult
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Aged
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Aged80
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Female
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Humans
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Male
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Middle aged
Idioma:
En
Ano de publicação:
2014
Tipo de documento:
Article