A Boolean-based systems biology approach to predict novel genes associated with cancer: Application to colorectal cancer
| Autor: | Antonio Reverter, Shivashankar H. Nagaraj |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2011 |
| Předmět: |
Candidate gene
060114 Systems Biology Systems biology Gene regulatory network Pancreatitis-Associated Proteins colorectal cancer Biology Structural Biology Modelling and Simulation Biomarkers Tumor medicine Humans Gene Regulatory Networks Genetic Predisposition to Disease Genetic Testing Epigenetics Boolean-based systems biology Gene 111203 Cancer Genetics Molecular Biology lcsh:QH301-705.5 111201 Cancer Cell Biology Genetics Systems Biology Applied Mathematics Cancer DNA Methylation medicine.disease Computer Science Applications prediction of novel genes lcsh:Biology (General) Organ Specificity Modeling and Simulation DNA methylation Human genome Colorectal Neoplasms Protein Kinases Algorithms Research Article Transcription Factors |
| Zdroj: | BMC Systems Biology, Vol 5, Iss 1, p 35 (2011) BMC Systems Biology |
| ISSN: | 1752-0509 |
| Popis: | Background Cancer has remarkable complexity at the molecular level, with multiple genes, proteins, pathways and regulatory interconnections being affected. We introduce a systems biology approach to study cancer that formally integrates the available genetic, transcriptomic, epigenetic and molecular knowledge on cancer biology and, as a proof of concept, we apply it to colorectal cancer. Results We first classified all the genes in the human genome into cancer-associated and non-cancer-associated genes based on extensive literature mining. We then selected a set of functional attributes proven to be highly relevant to cancer biology that includes protein kinases, secreted proteins, transcription factors, post-translational modifications of proteins, DNA methylation and tissue specificity. These cancer-associated genes were used to extract 'common cancer fingerprints' through these molecular attributes, and a Boolean logic was implemented in such a way that both the expression data and functional attributes could be rationally integrated, allowing for the generation of a guilt-by-association algorithm to identify novel cancer-associated genes. Finally, these candidate genes are interlaced with the known cancer-related genes in a network analysis aimed at identifying highly conserved gene interactions that impact cancer outcome. We demonstrate the effectiveness of this approach using colorectal cancer as a test case and identify several novel candidate genes that are classified according to their functional attributes. These genes include the following: 1) secreted proteins as potential biomarkers for the early detection of colorectal cancer (FXYD1, GUCA2B, REG3A); 2) kinases as potential drug candidates to prevent tumor growth (CDC42BPB, EPHB3, TRPM6); and 3) potential oncogenic transcription factors (CDK8, MEF2C, ZIC2). Conclusion We argue that this is a holistic approach that faithfully mimics cancer characteristics, efficiently predicts novel cancer-associated genes and has universal applicability to the study and advancement of cancer research. |
| Databáze: | OpenAIRE |
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