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2015 ; 4
(4
): 647-70
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Cancer Biomarkers from Genome-Scale DNA Methylation: Comparison of Evolutionary
and Semantic Analysis Methods
#MMPMID27600245
Valavanis I
; Pilalis E
; Georgiadis P
; Kyrtopoulos S
; Chatziioannou A
Microarrays (Basel)
2015[Nov]; 4
(4
): 647-70
PMID27600245
show ga
DNA methylation profiling exploits microarray technologies, thus yielding a
wealth of high-volume data. Here, an intelligent framework is applied,
encompassing epidemiological genome-scale DNA methylation data produced from the
Illumina's Infinium Human Methylation 450K Bead Chip platform, in an effort to
correlate interesting methylation patterns with cancer predisposition and, in
particular, breast cancer and B-cell lymphoma. Feature selection and
classification are employed in order to select, from an initial set of ~480,000
methylation measurements at CpG sites, predictive cancer epigenetic biomarkers
and assess their classification power for discriminating healthy versus cancer
related classes. Feature selection exploits evolutionary algorithms or a
graph-theoretic methodology which makes use of the semantics information included
in the Gene Ontology (GO) tree. The selected features, corresponding to
methylation of CpG sites, attained moderate-to-high classification accuracies
when imported to a series of classifiers evaluated by resampling or blindfold
validation. The semantics-driven selection revealed sets of CpG sites performing
similarly with evolutionary selection in the classification tasks. However, gene
enrichment and pathway analysis showed that it additionally provides more
descriptive sets of GO terms and KEGG pathways regarding the cancer phenotypes
studied here. Results support the expediency of this methodology regarding its
application in epidemiological studies.
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