A novel algorithm for network-based prediction of cancer recurrence.
Genomics
; 111(1): 17-23, 2019 01.
Article
em En
| MEDLINE
| ID: mdl-27453286
ABSTRACT
To develop accurate prognostic models is one of the biggest challenges in "omics"-based cancer research. Here, we propose a novel computational method for identifying dysregulated gene subnetworks as biomarkers to predict cancer recurrence. Applying our method to the DNA methylome of endometrial cancer patients, we identified a subnetwork consisting of differentially methylated (DM) genes, and non-differentially methylated genes, termed Epigenetic Connectors (EC), that are topologically important for connecting the DM genes in a protein-protein interaction network. The ECs are statistically significantly enriched in well-known tumorgenesis and metastasis pathways, and include known epigenetic regulators. Importantly, combining the DMs and ECs as features using a novel random walk procedure, we constructed a support vector machine classifier that significantly improved the prediction accuracy of cancer recurrence and outperformed several alternative methods, demonstrating the effectiveness of our network-based approach.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Algoritmos
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Biomarcadores Tumorais
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Neoplasias do Endométrio
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Metilação de DNA
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Recidiva Local de Neoplasia
Tipo de estudo:
Diagnostic_studies
/
Prognostic_studies
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Risk_factors_studies
Limite:
Female
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Humans
Idioma:
En
Ano de publicação:
2019
Tipo de documento:
Article