Abstract
Cancer is produced by perturbations affecting several genes and pathways. Environmental stimuli trigger uncontrolled cell growth and invasion into other tissues. Understanding cancer progression requires a profound knowledge of the pathways involved in the communication between proteins and genes at a systems level. Consequently, protein-protein interaction networks play an important role in delineating cancer related pathways. Our understanding of cancer has evolved towards the co-operation of groups of genes that constitute pathways. In this chapter, we describe the characteristics of genes involved in cancer and the relationships between them in the context of the protein-protein interaction network. We also explain several methods to predict novel candidates that are potentially involved in cancer and its progression using topological information encoded in the protein-protein interaction network. Towards developing effective network-based therapeutics, we give details of identifying dysregulation patterns in cancer using protein-protein interaction networks with an emphasis on the underlying mechanisms of progression in metastatic breast cancer.
Keywords
- Protein-protein interaction
- Network biology
- Network medicine
- Active subnetwork
- Metastasis
- Metastatic breast cancer
- Guilt-by-association
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Abbreviations
- PPI:
-
Protein-protein interaction
- GO:
-
Gene Ontology
- OMIM:
-
Online Mendelian Inheritance in Man
- ROC:
-
Receiver-operating characteristic
- AUC:
-
Area under (ROC) curve
- GRP:
-
Glucose regulated proteins
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Acknowledgments
EG is supported through FI fellowship granted by “Departament d’Educació i Universitats de la Generalitat de Catalunya i del Fons Social Europeu”. BO acknowledges grants from the Spanish Ministry of Science and Innovation (MICINN), FEDER BIO2011-22568, and PSE-0100000-2009. AS and RS acknowledge MetaBre consortium (LSHC-CT-2004-506049).
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Guney, E., Sanz-Pamplona, R., Sierra, A., Oliva, B. (2012). Understanding Cancer Progression Using Protein Interaction Networks. In: Azmi, A.S. (eds) Systems Biology in Cancer Research and Drug Discovery. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-4819-4_7
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