explored key signatures for metastatic prostate cancer. These genes
are members of the AP1 transcription factor family. These tran-
scription factors were participated in a variety of pathways which
implicated in cancer beginning and progression (cell differentia-
tion, proliferation, apoptosis, and oncogenic transformation)
[107]. AP transcription factors are mediators of TGFβsignaling
pathway [108]. Based on the integrative co-expression network for
metastatic prostate cancer and the large number of AP1 transcrip-
tion factors (JUN, JUNB, FOS, FOSB, and ATF3) and TGFβ
signaling pathway members, AP1 transcription factors and TGFβ
signaling pathway interrelationship dysregulation may have a criti-
cal role in metastatic prostate cancer which needs more studies to
clarify.4 Notes
To improve the treatment outcomes of patients who develop meta-
static cancer, a mechanistic understanding of the determinants of
the progression of disease is indispensable. From the last few dec-
ades, the systems biology and bioinformatics approaches produce
successful results to study complex disease with the aim of unravel-
ing their regulatory mechanisms but still it is a long way to go. To
understand complex processes and gain new insights into a complex
disease like cancer, the interdisciplinary collaborations in systems
biology usually begin with the gathering of information from the
literature and databases, summarizing components and their inter-
actions relevant for the process under investigation. The informa-
tion gathered is summarized in interaction maps, which serve as a
knowledge-base and being machine readable are amenable to
computational analysis. Interaction maps are the summary of a
large number of components interacting through feedback and
feedforward loops and overlapping pathways.
Recently, numerous large-scale biochemical networks have
been constructed with the intention to broaden the understanding
of the regulatory events behind the normal and dysregulated func-
tion of the pathways involve in certain processes [20, 21, 23, 24,
39 ]. Using structural analysis, these networks provide useful infor-
mation about the organization of the network by identifying hub
nodes, regulatory motifs, small interconnected modules, and fac-
tors that might be used as therapeutic targets [20]. Networks can
also be used to analyze large-scale data to identify expression pat-
terns by using the mapping function of the CellDesigner, Cytos-
cape, or other visualization tools. So far, most of the large-scale
network-based studies are confined to static analysis only; never-
theless, they can provide foundation for mechanistic understanding
of complex processes that are dysregulated in disease.Integrative Workflow for Predicting Disease Signatures 271