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short-paper

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

Authors:

Md. Jamiul Jahid,

David G. Mutch,

Chun-Liang Chen,

Nameer B. Kirma,

Tim H. HuangAuthors Info & Claims

BCB '12: Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine

Pages 418 - 425

https://doi.org/10.1145/2382936.2382990

Published: 07 October 2012 Publication History

Abstract

DNA methylation, a well-studied mechanism of epigenetic regulation, plays important roles in cancer. Increased levels of global DNA methylation is observed in primary solid tumors including endometrial carcinomas and is generally associated with silencing of tumor suppressor genes. The role of DNA methylation in cancer recurrence after therapeutic intervention is not clear. 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. Remarkably, we found that 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 prediction accuracy of cancer recurrence and outperforms several alternative methods. Therefore, the network-based method is effective in identifying gene subnetworks that are crucial both for the understanding and prediction of tumor recurrence.

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Cited By

Ruan JJahid MGu FLei CHuang YHsu YMutch DChen CKirma NHuang T(2019)A novel algorithm for network-based prediction of cancer recurrenceGenomics10.1016/j.ygeno.2016.07.005111:1(17-23)Online publication date: Jan-2019
https://doi.org/10.1016/j.ygeno.2016.07.005
Liu LWei JRuan J(2017)Pathway Enrichment Analysis with NetworksGenes10.3390/genes81002468:10(246)Online publication date: 28-Sep-2017
https://doi.org/10.3390/genes8100246
Hira ZGillies D(2016)Identifying Significant Features in Cancer Methylation Data Using Gene Pathway SegmentationCancer Informatics10.4137/CIN.S3985915(CIN.S39859)Online publication date: 20-Sep-2016
https://doi.org/10.4137/CIN.S39859

Index Terms

Network-based classification of recurrent endometrial cancers using high-throughput DNA methylation data
1. Information systems
  1. Information systems applications

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Information & Contributors

Information

Published In

cover image ACM Conferences

BCB '12: Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine

October 2012

725 pages

ISBN:9781450316705

DOI:10.1145/2382936

General Chair:
Sanjay Ranka
University of Florida
,
Program Chairs:
Tamer Kahveci
University of Florida
,
Mona Singh
Princeton University

Copyright © 2012 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGBio: ACM Special Interest Group on Bioinformatics

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 October 2012

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National Institutes of Health

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BCB' 12

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SIGBio

BCB' 12: ACM International Conference on Bioinformatics, Computational Biology and Biomedicine

October 7 - 10, 2012

Florida, Orlando

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BCB '12 Paper Acceptance Rate 33 of 159 submissions, 21%;

Overall Acceptance Rate 254 of 885 submissions, 29%

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Cited By

Ruan JJahid MGu FLei CHuang YHsu YMutch DChen CKirma NHuang T(2019)A novel algorithm for network-based prediction of cancer recurrenceGenomics10.1016/j.ygeno.2016.07.005111:1(17-23)Online publication date: Jan-2019
https://doi.org/10.1016/j.ygeno.2016.07.005
Liu LWei JRuan J(2017)Pathway Enrichment Analysis with NetworksGenes10.3390/genes81002468:10(246)Online publication date: 28-Sep-2017
https://doi.org/10.3390/genes8100246
Hira ZGillies D(2016)Identifying Significant Features in Cancer Methylation Data Using Gene Pathway SegmentationCancer Informatics10.4137/CIN.S3985915(CIN.S39859)Online publication date: 20-Sep-2016
https://doi.org/10.4137/CIN.S39859

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