Microarray and RNAseq are the most widely used techniques to
observe the expression of gene and miRNAs in the context of
various diseases and several analytical methods/tools were devel-
oped to identify differentially expression genes/miRNAs. Most of
these tools use statistical methods such as student’s t-test and its
variants [76], ANOVA [77], Bayesian method [78], and/or Mann-
Whitney test [79] to rank genes for differential expression. Once
the –omics data is integrated on the network, this helps in prioritiz-
ing regulatory motifs along with other network topological and
biomedical parameters to derive a small sub-network responsible
for disease progression. This small network is then subjected to a
suitable mathematical formalism as described above to find context-
specific suitable molecular signatures.2.6 Validation
of Model-Derived
Hypotheses Using
Experimental and/or
Clinical Data
The most significant step in any of the systems biology project is to
validate molecular signatures derived after modeling simulations.
Validation of predictive results from any of the computational
approaches is not only necessary to identify operational/technical
errors but is also important to justify the need of new analysis
procedures. The molecular signatures, for example gene signatures,
could be validated by using more precise gene expression measure
along with larger sample size. However, this strategy is generally
not used because of the cost of array experiments. Many researchers
validate significant results by extracting fresh mRNA from the same
specimens and measuring the expressing level using different
mRNA-measurement techniques such as RT-PCR (real-time poly-
merase chain reaction) or by using targeted gene overexpression/
silencing experiments. The availability of larger number of clinical
studies in the public domain also made it possible to analyze model-
derived results quickly before planning time consuming and expen-
sive experiments. One of such resources is “The Cancer Genome
Atlas” (TCGA) project which host multi-dimensional maps of key
genomic changes in 33 various types of cancer from thousands of
independent studies [80]. Predicted cancer related molecular sig-
natures can be easily validated for their relevance using the Kaplan-
Meier survival analysis tool available on the UCSC xena browser
(http://xena.ucsc.edu) which provides a window to access a large
collection of UCSC-hosted public databases such as TCGA [80],
International Cancer Genome Consortium (ICGC) [81], Thera-
peutically Available Research to Generate Effective Treatments
(TARGET), Genotype-Tissue Expression (GTEx), and others.3 Results/Discussion
In order to validate the presented workflow (Fig.1), we used
prostate cancer as a case study and performed various analysis
steps to predict molecular signatures associated with primary to
metastatic tumor transition.264 Faiz M. Khan et al.