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From the third issue of 2017, Microarrays has changed its name to High-Throughput.

Open AccessCommunication
Microarrays 2015, 4(4), 630-646; doi:10.3390/microarrays4040630

Integrating Colon Cancer Microarray Data: Associating Locus-Specific Methylation Groups to Gene Expression-Based Classifications

1
Centre for Systems Medicine and Department of Physiology & Medical Physics, Royal College of Surgeons in Ireland, 123 Saint Stephen’s Green, Dublin 2, D02 YN77 Ireland
2
Center for Scientific Computing and Complex Systems Modelling, School of Computing, Dublin City University, Collins Avenue, Dublin 9, Ireland
2nd author details; Tel.: +353-700-5513; Fax: +353-700-5442.
*
Author to whom correspondence should be addressed.
Academic Editor: Massimo Negrini
Received: 5 July 2015 / Revised: 22 September 2015 / Accepted: 30 October 2015 / Published: 23 November 2015
(This article belongs to the Special Issue Computational Modeling and Analysis of Microarray Data: New Horizons)
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Abstract

Recently, considerable attention has been paid to gene expression-based classifications of colorectal cancers (CRC) and their association with patient prognosis. In addition to changes in gene expression, abnormal DNA-methylation is known to play an important role in cancer onset and development, and colon cancer is no exception to this rule. Large-scale technologies, such as methylation microarray assays and specific sequencing of methylated DNA, have been used to determine whole genome profiles of CpG island methylation in tissue samples. In this article, publicly available microarray-based gene expression and methylation data sets are used to characterize expression subtypes with respect to locus-specific methylation. A major objective was to determine whether integration of these data types improves previously characterized subtypes, or provides evidence for additional subtypes. We used unsupervised clustering techniques to determine methylation-based subgroups, which are subsequently annotated with three published expression-based classifications, comprising from three to six subtypes. Our results showed that, while methylation profiles provide a further basis for segregation of certain (Inflammatory and Goblet-like) finer-grained expression-based subtypes, they also suggest that other finer-grained subtypes are not distinctive and can be considered as a single subtype. View Full-Text
Keywords: colorectal cancer; gene expression; locus specific methylation; colorectal cancer subtypes; microarrays; data integration colorectal cancer; gene expression; locus specific methylation; colorectal cancer subtypes; microarrays; data integration
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Barat, A.; Ruskin, H.J.; Byrne, A.T.; Prehn, J.H.M. Integrating Colon Cancer Microarray Data: Associating Locus-Specific Methylation Groups to Gene Expression-Based Classifications. Microarrays 2015, 4, 630-646.

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