Categorization and Estimation of Relative Connectivity of Genes from Meta-OFTEN Network

The most common result of analysis of highthroughput data in molecular biology represents a global list of genes, ranked accordingly to a certain score. The score can be a measure of differential expression. Recent work proposed a new method for selecting a number of genes in a ranked gene list from microarray gene expression data such that this set forms the Optimally Functionally Enriched Network (OFTEN), formed by known physical interactions between genes or their products. Here we present calculation results of relative connectivity of genes from META-OFTEN network and tentative biological interpretation of the most reproducible signal. The relative connectivity and inbetweenness values of genes from META-OFTEN network were estimated.

Authors:

• U. Kairov
• T. Karpenyuk
• E. Ramanculov
• A. Zinovyev

Keywords:

• Microarray
• META-OFTEN
• gene network.

References:

[1] van-t Veer L.J., Dai H., van de Vijver M.J. et al. "Gene expression
profiling predicts clinical outcome of breast cancer". Nature, 415:530-6,
2002.
[2] van de Vijver M.J., van't Veer L.J. et al. "ðÉ gene-expression signature as
a predictor of survival in breast cancer". N. Engl. J. Med., 347:1999-
2009, 2002.
[3] Wang Y., Klijn J.G., Zhang Y., Sieuwerts A.M. et al. "Gene-expression
profiles to predict distant metastasis of lymph-node-negative primary
breast cancer" Lancet, 365(9460):671-9, 2005.
[4] Cobleigh M.A., Tabesh B., Bitterman P., Baker J., Cronin M., Liu M.L.,
Borchik R., Mosquera J.M., Walker M.G., Shak S. "Tumor gene
expression and prognosis in breast cancer patients with 10 or more
positive lymph nodes" Clin. Cancer Res., 11(24 Pt 1):8623-31, 2005.
[5] Chuang H.-Y. et al. "Network-based classification of breast cancer
metastasis" Mol. Syst. Biol., 3:140, 2007.
[6] Rapaport F., Zinovyev A., Dutreix M., Barillot E., Vert J.-P.
"Classification of microarray data using gene networks" BMC
Bioinformatics, 8:35, 2007.
[7] Foekens J. A. et al. "Multicenter validation of a gene expression-based
prognostic signature in lymph node-negative primary breast cancer" J.
Clin. Oncol., 24:1665-1671, 2006.
[8] Finocchiaro G. et al. "Graph-based identification of cancer signaling
pathways from published gene expression signatures using PubLiME"
Nucleic Acids Res., 35(7): 2343, 2007.
[9] Reyal F., van Vliet M.H., Armstrong N.J., Horlings H.M., de Visser
K.E., Kok M., Teschendorff A.E., Mook S., van 't Veer L., Caldas C.,
Salmon R.J., van de Vijver M.J., Wessels L.F. "A comprehensive
analysis of prognostic signatures reveals the high predictive capacity of
the proliferation, immune response and RNA splicing modules in breast
cancer" Breast Cancer Res., 10(6):R93, 2008.
[10] Kairov U., Karpenyuk T., Ramanculov E., Zinovyev A. "Network
analysis of gene lists for finding reproducible prognostic breast cancer
gene signatures" Bioinformation, 8(16):773-6, 2012.
[11] Cline M., Smoot M., Cerami E. et al. "Integration of biological networks
and gene expression data using Cytoscape" Nature Protocols, 2:2366 -
2382, 2007.
[12] Zinovyev A. et al. "BiNoM: a Cytoscape plugin for manipulating and
analyzing biological networks" Bioinformatics, 24(6):876, 2008.
[13] Huang D.W., Sherman B.T., Lempicki R.A. "Bioinformatics enrichment
tools: paths toward the comprehensive functional analysis of large gene
lists" Nucleic Acids Res., 37(1):1-13, 2009.
[14] Barillot E., Calzone L., Hupe P., Vert J.-P., Zinovyev A. "Computational
Systems Biology of Cancer" CRC Press Inc, Chapman & Hall/CRC
Mathematical & Computational Biology, 452ÐÇ., 2012.
[15] Pinna G., Zinovyev A., Araujo N., Morozova N., Harel-Bellan A.
"Analysis of the growth control network specific for human lung
adenocarcinoma cells" Math. Model. Nat. Phenom., 7(01):337-368,
2012.
[16] Chen J., Sam L., Huang Y., Lee Y., Li J., Liu Y., Xing H.R., Lussier
Y.A. "Protein interaction network underpins concordant prognosis
among heterogeneous breast cancer signatures" J Biomed. Inform.,
43(3): 385-396, 2010.