Network-based classification of recurrent endometrial cancers using high-throughput DNA methylation data




Ruan, Jianhua
Jahid, Md. Jamiul
Gu, Fei
Lei, Chengwei
Huang, Yi-Wen
Hsu, Ya-Ting
Goodfellow, Paul J.
Chen, Chun-Liang
Huang, Tim H.-M.

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UTSA Department of Computer Science


Motivation: DNA methylation plays important roles in cancer, which is a complex disease involving many genes. However, by far DNA methylation analysis has not been integrated with the gene / protein networks that regulate various biological processes within the cell. Here, we developed a novel computational method to analyze whole-genome DNA methylation data for endometrial tumors within the context of a human protein-protein interaction (PPI) network, in order to identify subnetworks as potential epigenetic biomarkers for predicting tumor recurrence. Our method consists of the following steps. First, differentially methylated (DM) genes between recurrent and non-recurrent tumors are identified and mapped onto a human PPI network. Then, a PPI subnetwork consisting of DM genes and genes that are topologically important for connecting the DMs on the PPI network, termed epigenetic connectors (ECs), are extracted using a Steiner-tree based algorithm. Finally, a random-walk based machine learning method is used to propagate the DNA methylation scores from the DMs to the ECs, which enables the ECs to be used as features in a support vector machine classifier for predicting recurrence.

Results: While the DMs are not enriched in any cancer-related pathways, the ECs are enriched in many well-known tumorgenesis and metastasis pathways and include known epigenetic regulators. Moreover, combining the DMs and ECs significantly improves the accuracy for classifying recurrence. Therefore, the network-based method is effective in identifying a subnetwork consisting of both differentially methylated genes and other important non-differentially methylated genes which are nevertheless important for the understanding and prediction of tumor recurrence.





Computer Science