Basic HTML Version
Computational Molecular Biology
13
series of bioinformatic analysis including cluster
analysis, differential sites identification, network
building and functional enrichment analysis, we
explored the characteristic of differentially methylated
CpG sites and argued that the
bona fide
differentially
methylated sites among six cancers may be the real
functional elements related with DNA methylation in
cancers. Our study proposed a new strategy to identify
cancer-specific methylation markers which may be
useful for cancer-specific diagnosis, treatment and
prognosis.
3 Materials and Methods
3.1 DNAmethylation data
The DNA methylation data were downloaded from
Gene Expression Omnibus (GEO) repository under
accession numbers “GSE17648”, “GSE21304”,
“GSE22867”, “GSE26319” and “GSE26990” (Barrett
et al., 2009). All these data were profiled by Illumina
HumanMethylation27 BeadChip (Human Methylation
27_270596_v.1.2) which allows researchers to
interrogate 27,578 highly informative CpG sites
located within the proximal promoter regions of
transcription start sites of 14,475 consensus coding
sequencing in the NCBI Database (Genome Build 36).
In this study, we used 27,543 CpGs whose
methylation levels have been detected in all 297
samples from six cancers (colorectal cancer, multiple
myeloma cancer, plasma cell leukemia, glioblastoma
cancer, prostate cancer, and breast cancer) and five
matched normal control tissues (colorectal, plasma,
brain, prostate and breast). For each CpG site, the
methylation level in a cancer/tissue is the mean of
methylation levels in all the replicate samples per
cancer/tissue.
3.2 Hierarchical clustering
Both the hierarchical clustering of all CpGs in all 297
samples and the hierarchical clustering in six cancers
and five normal tissues were performed by
GenePattern (http://genepattern. broadinstitute.org)
(Reich et al., 2006). Euclidean distance was used as
the distance measure for both column and row
distance clustering. In order to avoid preexisting bias
in the distance measure, we also repeated the
hierarchical clustering in six cancers and five normal
tissues using Pearson correlation. Other parameters
were used as the default given in GenePattern.
3.3 Identification of C-DMSs and T-DMSs
The C-DMSs and T-DMSs used in this paper were
identified by QDMR which we developed in a
previous study (Zhang et al., 2011). For each CpG site,
the methylation differences among six cancers were
quantified by QDMR. The CpG sites with entropy less
than the DMR threshold (3.259) for six samples given
by QDMR were identified as C-DMSs. In the same
way, we obtained the quantified methylation
differences of each CpG site among five normal
control tissues and the T-DMSs with entropy lower
than the threshold (2.701) for five samples.
Acknowledgments
The authors thank Scientific Research Fund of Heilongjiang
Provincial Education Department for funding. This work is
funded by the Scientific Research Fund of Heilongjiang
Provincial Education Department [12521270].
References
Agrawal A., Murphy R.F., and Agrawal D.K., 2007, DNA
methylation in breast and colorectal cancers, Mod Pathol,
20: 711-721
http://dx.doi.org/10.1038/modpathol.3800822
PMid:17464311
Barrett T., Troup D.B., Wilhite S.E., Ledoux P., Rudnev D.,
Evangelista C., Kim I.F., Soboleva A., Tomashevsky M.,
Marshall K.A., Phillippy K.H., Sherman P.M., Muertter
R.N., and Edgar R., 2009, NCBI GEO: archive for
high-throughput functional genomic data, Nucleic Acids
Res, 37: D885-890
http://dx.doi.org/10.1093/nar/gkn764
PMid:18940857 PMCid:PMC2686538
Bhatia S., Frangioni J.V., Hoffman R.M., Iafrate A.J., and
Polyak K., 2012, The challenges posed by cancer
heterogeneity, Nat Biotechnol, 30: 604-610
http://dx.doi.org/10.1038/nbt.2294
PMid:22781679
Calin G.A., and Croce C.M., 2006, MicroRNA signatures in
human cancers, Nat Rev Cancer, 6: 857-866
http://dx.doi.org/10.1038/nrc1997
PMid:17060945
Chen X., Cheung S.T., So S., Fan S.T., Barry C., Higgins J., Lai
K.M., Ji J., Dudoit S., Ng I.O., Van De Rijn M., Botstein
D., and Brown P.O., 2002, Gene expression patterns in
human liver cancers, Mol Biol Cell, 13: 1929-1939
http://dx.doi.org/10.1091/mbc.02-02-0023.
PMid:12058060 PMCid:PMC117615
Computational
Molecular Biology