Computational Molecular Biology 2015, Vol. 5, No. 5, 1-9
1
ReviewArticle Open Access
Meta Analysis of Gene Expression Data of Multiple Cancer Types To Predict
Biomarkers and Drug Targets
Shashank K.S.
1
, Mamatha H R.
1
, Prashantha C.N.
2,
1 Department of Information Science, PES Institute of Technology, Bangalore, India
2 Department of Biological sciences, Scientific Bio-Minds, Bangalore, India
Corresponding author email
Computational Molecular Biology, 2015, Vol.5, No.5 do
Received: 17 Aug., 2015
Accepted: 25 Sep, 2015
Published: 16 Oct., 2015
© 2015
Shashank
et al This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use,
distribution, and reproduction in any medium, provided the original work is properly cited.
Preferred citation for this article:
Shashank K.S., Mamatha H.R., and Prashantha C.N., 2015,
Meta Analysis of Gene Expression Data of Multiple Cancer Types To Predict
Biomarkers and Drug Targets Interactions in Ovarian Cancer
, Computational Molecular Biology, 5(5): 1-9
Abstract
Meta analysis of gene expression data of multiple cancer types such as breast, colon and ovary used to identify gene
signatures that functionally used as a marker to prognosis and molecular diagnostics. There is a reliable identification of gene
signatures is associated with different cancer types remains a challenge. The aim of this study is to develop microarray statistical data
analysis methods and SVM classifiers to identify differentially expressed genes in different cancer types. Using our method t o
perform 16 datasets such as 6 breast cancer, 4 colon cancer and 6 ovarian cancer of different datasets. Our results is analysed in 4
different methods (a) preprocess the data to identify quality expression of datasets by removing null values and non significant values
(p<0.05) (b). Differential gene expression analysis using statistical analysis to predict upregulation and downregulated gene
signatures (c) subgrouping of datasets that has been classified based on cancer types (d) gene network prediction to identify
gene-gene interaction to understand biological markers. We have predicted 8 markers in breast cancer, 10 markers in colon cancer
and 16 markers in ovarian cancer is providing new direction for diagnostics and therapeutic development..
Keywords
breast cancer; Colon cancer; Ovarian cancer; Microarray; Statistics; Limma; Biocoductor; geNETClassifier
Introduction
Cancer is a large family of disease that can threat to
people’s health ad life. According to 2014-15 survey
shows 22% of disease death is observed in worldwide
(Cancer Fact sheet N°297, WHO, 2014). In India
cancer is second largest death following with heart
disease. The statistical survey of cancer shows 82% of
women is affecting breast cancer, 62% of men and
women is affecting with colon and 90% of women is
affecting with ovarian cancer (Matsushita K et al.,2010)
The decreased trend in diagnostics techniques for
identification of cancer in early stage of development.
In the 21
st
decade, molecular biomarkers that helps to
identify disease in early stage. In the current research
using three cancer types (breast, colon and ovarian
cancer) is helps to identify molecular markers using
microarray technique.
In the present year 9.1 million women is affecting
with breast cancer in worldwide. In addition 232,670
women is diagnosed with in a year. 30% of women
population is affected due to genetic abnormalities
such as mutation of BRCA1 ad BRCA2 genes
(Dumitrescu RG et al.,2005). In addition there are
some other oncogenic genes such as k-RAS, p53,
PTEN, NBS1 etc also causing breast cancer (Honrado
E et al.,2006). The colon cancer also leading cancer
types that frequently affecting other tissues such as
lungs, breast and prostrate tissues. The genetic
alterations of k-RAS, APC, P53, β-catenin, GSK-3β
that mainly affect WNT- β-catenin signaling pathways
that also affects breast and ovarian cancer (Vogelstein
B et al., 1988), (Fearon ER et al., 1990). The epithelial
ovarian cancer is also dangerous cancer type in women,
the mutation of p53, BCL-XL, EGFR, MDM2, MCI-2,
NOXA etc is mainly involved in ovarian c ancer
(Baekelandt M et al., 1999), (Kupryjanczyk J et al.,
2003), (Nielsen JS et al., 2004). A number of genetic
marker has been proposed to identify cancer such as
BRCA1, BRCA2 of breast cancer, APC, GSK-3β of
colon cancer and CA125 of ovarian cancer. In addition
