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

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posted on 2022-03-31, 14:07 authored by Ana Barat, Heather J. Ruskin, Annette ByrneAnnette Byrne, Jochen PrehnJochen Prehn

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.


We thank Anguraj Sadanandam and Diether Lambrechts for helpful discussions. The research leading to these results has received funding from the European Union Seventh Framework Programme FP7 under grant agreement No. 278981 “AngioPredict”.



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Published Citation

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

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  • Centre for Systems Medicine
  • Physiology and Medical Physics