A novel algorithm for network-based prediction of cancer recurrence

Jianhua Ruan, Md Jamiul Jahid, Fei Gu, Chengwei Lei, Yi Wen Huang, Ya Ting Hsu, David G. Mutch, Chun Liang Chen, Nameer B. Kirma, Tim H.M. Huang

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)17-23
Number of pages7
JournalGenomics
Volume111
Issue number1
DOIs
StatePublished - Jan 2019

ASJC Scopus subject areas

  • Genetics

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