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, Hui-ming Huang

Research output: Contribution to journalArticle

3 Scopus citations


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)
StateAccepted/In press - Feb 10 2016


ASJC Scopus subject areas

  • Genetics

Cite this

Ruan, J., Jahid, M. J., Gu, F., Lei, C., Huang, Y. W., Hsu, Y. T., Mutch, D. G., Chen, C., Kirma, N. B., & Huang, H. (Accepted/In press). A novel algorithm for network-based prediction of cancer recurrence. Genomics. https://doi.org/10.1016/j.ygeno.2016.07.005