Abstract
The aim of this paper is to provide a snapshot of functional networks with central nodes (hubs) relying on the behavior of genes linked to a specific interaction network. Utilizing a ‘breast cancer signature’, a Bayesian approach is applied for the construction of gene interaction networks from different populations. We demonstrate that the hub genes of the differentiating network between cancer and control states can be regarded as potential markers for breast cancer. Furthermore, the differentiating subnetworks can be informative of the phenotype and provide new knowledge about the functional interactions and molecular pathways involved in breast cancer.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
- 1.
GEO: Gene Expression Omnibus; available at https://www.ncbi.nlm.nih.gov/geo/.
- 2.
KEGG: Kyoto Encyclopedia of Genes and Genomes; available at http://www.genome.jp/kegg/.
- 3.
Wikipathways; available at http://wikipathways.org/index.php/WikiPathways.
References
Barabási, A.L., Oltvai, Z.N.: Network biology: understanding the cell’s functional organization. Nat. Rev. Genet. 5(2), 101–113 (2004)
Korb, K.B., Nicholson, A.E.: Introducing Bayesian networks. In: Bayesian Artificial Intelligence, 2nd edn., pp. 29–54. Taylor and Francis Group, LLC (2011)
Oniésko, A., Lucas, P., Druzdzel, M.J.: Comparison of rule-based and Bayesian network approaches in medical diagnostic systems. In: Quaglini, S., Barahona, P., Andreassen, S. (eds.) AIME 2001. LNCS (LNAI), vol. 2101, pp. 283–292. Springer, Heidelberg (2001). doi:10.1007/3-540-48229-6_40
Sfakianakis, S., Bei, E.S., Zervakis, M., Vassou, D., Kafetzopoulos, D.: On the identification of circulating tumor cells in breast cancer. IEEE J. Biomed. Health Inform. 18(3), 773–782 (2014). IEEE Press
Tusher, V.G., Tibshirani, R., Chu, G.: Significance analysis of microarrays applied to the ionizing radiation response. Proc. Nat. Acad. Sci. 98(9), 5116–5121 (2001)
Chlis, N.K., Sfakianakis, S., Bei, E.S., Zervakis, M.: A generic framework for the elicitation of stable and reliable gene expression signatures. Ιn: Proceedings of IEEE BIBE, Chania, Greece, pp. 1–4. IEEE Press (2013)
Needham, C.J., Bradford, J.R., Bulpitt, A.J., Westhead, D.R.: A primer on learning in Bayesian networks for computational biology. PLoS Comput. Biol. 3(8), e129 (2007)
The biological general repository for interaction datasets. http://thebiogrid.org/
Cytoscape: a software environment for integrated models of biomolecular interaction networks. http://www.cytoscape.org/
Expression Atlas update - a database of gene and transcript expression from microarray and sequencing-based functional genomics experiments. Nucleic Acids Res. (2014). http://www.ebi.ac.uk/gxa
Schummer, M., Green, A., Beatty, J.D., Karlan, B.Y., Karlan, S., Gross, J., Thornton, S., McIntosh, M., Urban, N.: Comparison of breast cancer to healthy control tissue discovers novel markers with potential for prognosis and early detection. PLoS ONE 5(2), e9122 (2010)
Marko, N.F., Weil, R.J.: Non-Gaussian distributions affect identification of expression patterns, functional annotation, and prospective classification in human cancer genomes. PLoS ONE 7(10), e46935 (2012)
Cowley, M., Ying, K.: LogTransform Documentation - a GenePattern module for applying a log transformation on GCT files (not published). Garvan Institute (2011)
Bouhamed, H., Masmoudi, A., Lecroq, T., Rebaï, A.: A new approach for Bayesian classifier learning structure via K2 Algorithm. In: Huang, D.-S., Gupta, P., Zhang, X., Premaratne, P. (eds.) ICIC 2012. CCIS, vol. 304, pp. 387–393. Springer, Heidelberg (2012). doi:10.1007/978-3-642-31837-5_56
Wei, Z., Xu, H., Li, W., Gui, X., Wu, X.: Improved Bayesian network structure learning with node ordering via K2 Algorithm. In: Huang, D.-S., Jo, K.-H., Wang, L. (eds.) ICIC 2014. LNCS (LNAI), vol. 8589, pp. 44–55. Springer, Heidelberg (2014). doi:10.1007/978-3-319-09339-0_5
Al-Akwaa, F.M., Alkhawlani, M.M.: Comparison of the Bayesian network structure learning algorithms. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 2(3), 404–408 (2012)
Watts, D.J., Strogatz, S.H.: Collective dynamics of “small-world” networks. Nature 393, 440–442 (1998)
Gomez-Vela, F., Diaz, N.: Gene network biological validity based on gene-gene interaction relevance. Sci. World J. 2014(2014), 1–11 (2014)
Zhuang, D.Y., Jiang, L., He, Q.Q., Zhou, P., Yue, T.: Identification of hub subnetwork based on topological features of genes in breast cancer. Int. J. Mol. Med. 35(3), 664–674 (2015)
HIPPIE, Human Integrated Protein-Protein Interaction rEference. http://cbdm-01.zdv.uni-mainz.de/~mschaefer/hippie/
The Genes-to-Systems Breast Cancer (G2SBC) Database. http://www.itb.cnr.it/breastcancer/
Wang, J., Duncan, D., Shi, Z., Zhang, B.: WEB-based GEne SeT AnaLysis Toolkit (WebGestalt): update 2013. Nucleic Acids Res. 41(Web Server issue), W77–W83 (2013)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Chalepakis Ntellis, D.A., S. Bei, E., Kafetzopoulos, D., Zervakis, M. (2017). Assembly of Gene Expression Networks Based on a Breast Cancer Signature. In: Rojas, I., Ortuño, F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2017. Lecture Notes in Computer Science(), vol 10209. Springer, Cham. https://doi.org/10.1007/978-3-319-56154-7_7
Download citation
DOI: https://doi.org/10.1007/978-3-319-56154-7_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-56153-0
Online ISBN: 978-3-319-56154-7
eBook Packages: Computer ScienceComputer Science (R0)