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| Paper IPM / Biological / 13978 |
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| Abstract: | |||||||||||
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DOI: 10.1007/978-3-319-19387-8_360
Prostate cancer is a serious genetic disease
known to be one of the most widespread cancers in men, yet
the molecular changes that drive its progression are not fully
understood. The availability of high-throughput gene expres-
sion data has led to the development of various computational
methods for the identification of key processes involved.
In this paper, we show that constructing stage-specific co-
expression networks provides a powerful alternative strategy
for understanding molecular changes that occur during pros-
tate cancer. In our approach, we constructed independent
networks from each cancerous stage using a derivative of
current state-of-art reverse engineering approaches. We next
highlighted crucial pathways and Gene Ontology (GO) in-
volved in the prostate cancer. We showed that such perturba-
tions in these networks, and the regulatory factors through
which they operate, can be efficiently detected by analyzing
each network individually and also in comparison with each
other.
Using this novel approach, our results led to the detection of
49 critical pathways and GOs related to prostate cancer, many
of which were previously shown to be involved in this cancer.
Correct inference of the processes and master regulators
that mediate molecular changes during cancer progression is
one of the major challenges in cancer genomics. In this paper,
we used a network-based approach to this problem. Applica-
tion of our approach to prostate cancer data has led to the re-
establishment of previous knowledge about this cancer, as well
as prediction of many other relevant processes and regulators.
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