Computational Molecular Biology 2015, Vol. 5, No. 5, 1-9
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lengths id different with differential gene expression
(Parker JS et al.,2009). The differential expressed genes
were annotated using GO.db package. The Annotation of
HGU 133plus 2 package of GO annotation helps to
understand the genes involved in differential expressed
genes along with biological process, molecular function
or cellular components of genes with systematic
classification.
Comparative analysis of differentially expressed
genes
Using geNETClassifier algorithm to classify the genes
was differentially expressed in different disease
datasets along with gene networks. The genome-wide
association studies of expression sets or expression
matrix files of ranked genes, probe sets of different
variables is optimizedwith training sets. Using multi-class
SVM based class ifier to quires genes chos en for
class ification; the mutual- information (interactions)
and the co-expression (correlations) between the genes
are also calculated and analyzed by the algorithm.
These allow estimating the degree of association
between the variables and they are used to generate a
gene network for each class. These networks can be
plotted, providing an integrated overview of the genes
that characterized each disease (i.e. each class).
Functional Annotation and Enrichment
Analysis
In order to obtain the functional enrichment of the
differentially expressed genes on the cell level, we
used the GO (Gene Ontology) database to classify the
gene function and location information. We performed
GO cluster analysis by using the cluster Profiler package,
then deduced the affection of these differentially
expressed genes to the cells by cluster the cells within
the molecular functions and biological processes. The
Database for Annotation, Visualization and Integrated
Discovery (DAVID) (
) and
GOrilla tool were us ed to identify over-repres ented
biologic al functions and pathways among the
differentially expressed genes.
Results and Discussion
This study is focused on three of the most prevalent
cancer types such as 6 breast cancer, 4 colon and 6
ovarian cancer microarray datasets is available in
publicly available GEO database. The datasets contains
cancerous genome sets corresponding with control
tissues that help to predict drug targets of each
individual c ancer types or groups of cancers as
gene-gene interactions.
Prediction of drug targets for individual
cancer types
We have searched individual datasets of each cancer
types whose gene expression patters is classif ied
based on cancer types and control tissues. Specifically,
all datasets is classified to predict drug targets that
specifically distinguishing the cancer types of both
disease and control types. In addition, we have ranked
the k-genes that significantly expressed in both
upregulated and down regulation that classified based
on gene-gene interaction studies.
A. Breast cancer
The analysis of breast cancer dataset contains 6 samples
such as 3 SUM149 cells transfected with control
sample of siRNA and SUM149 cells transfected with
siRNA targeting of tarzarotene-induced gene 1 (TIG1).
All thes e 6 samples is annotated w ith hgu133plus2
contains 54675 genes, using normalization methods to
filter the genes that significantly associated with
p-values, we have filter the 54675 genes of which
12788 genes that has significantly associated with
gene expression. 1220 genes have upregulated and
11568 genes are downregulated that differentially
expressed in breast cancer. Using SVM classification,
we have identified the 1275 most common significant
genes that associated with breast cancer. Among the
1275 genes 751 genes that encodes proteins, these
protein codes genes that helps to predict disease target
genes that helps for drug targets to control disease
(Figure:1). Using functional annotation and enrichment
analysis 130 genes that s ignific antly upregulated
these genes such as SHISA2, FBXO23, mmp7, fn1,
Cfi, Egr1, DCLK1, DCN, SERPINB3, SERPINB4,
MAP3K4, ITGBL1, OLFML3, NPY1R and
PHLDA1 genes is mainly assoc iated w it h
transcriptional regulation of breast cancer (table:1).
Us ing gene-gene interaction studies of both classes
shows 30 genes that s ignificantly associated w ith
gene regulation. There are 8 genes such as FBXO23,
MMP7, FN1, CFI, DCN, SERPINB3, SERPINB4
and MAP3K4 is expressed in blood serum within breast
