1. A novel algorithm for network-based prediction of cancer recurrence.
- Author
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Ruan J, Jahid MJ, Gu F, Lei C, Huang YW, Hsu YT, Mutch DG, Chen CL, Kirma NB, and Huang TH
- Subjects
- CpG Islands, DNA, Neoplasm, Epigenomics, Female, Gene Expression Profiling, Gene Regulatory Networks, High-Throughput Nucleotide Sequencing, Humans, Models, Genetic, Prognosis, Protein Interaction Domains and Motifs, Sequence Analysis, DNA, Algorithms, Biomarkers, Tumor, DNA Methylation, Endometrial Neoplasms diagnosis, Endometrial Neoplasms genetics, Endometrial Neoplasms pathology, Neoplasm Recurrence, Local
- 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., (Copyright © 2016 Elsevier Inc. All rights reserved.)
- Published
- 2019
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