Novel therapeutic targets identification in breast cancer by systems biology approach

Abstract

Background: The complexity of the cancer forms a major obstacle for a comprehensive understanding of molecular mechanisms of oncogenesis. In a single cell, genes and proteins are interrelated to work together and take part in many biological processes. The goal of systems biology is to combine molecular information of models to understand biological systems and their complexity, and finally attempt to predict biological function at the cellular, tissue, organ and whole organism levels. The Human Genome Project and high-throughput experimental methodologies such as Gene and Protein Microarrays with the Information Technology provides a powerful approach to address and dissect the complexity of cancer in a systems manner (1.Joyce A.R. Palsson B.O. The model organism as a system: integrating ‘omics’ data sets.Nat. Rev. Mol. Cell. Bio. 2006; 7: 198-210Crossref PubMed Scopus (560) Google Scholar & 2.Ideker T. Bafna V. Lemberger T. Integrating scientific cultures.Mol.Syst. Biol. 2007; 3: 105-106Crossref Scopus (12) Google Scholar). Breast cancer is diagnosed in nearly 1.4 million women and a leading cause of cancer-related deaths in women with more than 450,000 deaths every year in both developed and developing countries(3.Siegel R. Naishadham D. Jemal A. Cancer statistics, 2013.CancerJ Clin. 2013; 63: 11-30Crossref PubMed Scopus (11518) Google Scholar). According to the WHO release, there has been a 20% increase in the number of reported worldwide breast cancer patients which resulted in 522,000 deaths since 2008. Methods: In this study, I attempted a systems biology approach to predict disease-associated genes that are either not previously reported (novel) or poorly characterized and using breast cancer as a case study. Results: First the analysis made of gene expression data was used to obtain a list of differentially expressed and condition specific genes. Next, public databases are mined to compile a list of cancer-associated genes, non-cancer associated genes and functional attributes that are of relevance in the context of cancer. Considered were a total of six functional attributes. These features were also selected based on the fact that there is a strong functional interconnection among them and therefore we see the overlapping of these genes across attributes. The resulting set of variables are each binarized and used in a Boolean logic framework. When applied to non-cancer associated genes, the algorithm preferentially ranks those genes whose behavior across all variables most mimics that of cancer-associated genes. Finally, in order to gain a global understanding of the novel candidate genes, generated a series of gene co-expression networks. Conclusions: Candidate genes described here are classified based on individual attributes. This approach systematically predicts breast cancer-associated candidate genes and advancement of breast cancer research and are surveyed with a focus on the interacting partners of candidate genes and within the context of the original functional attributes. Legal entity responsible for the study: NA Funding: NA Disclosure: The author has declared no conflicts of interest.