Cliques for the identification of gene signatures for colorectal cancer across population

Autor: Meeta Pradhan, Kshithija Nagulapalli, Mathew J. Palakal
Jazyk: angličtina
Rok vydání: 2012
Předmět:
China
Systems biology
Population
Gene regulatory network
Saudi Arabia
Disease
Computational biology
Clique (graph theory)
Biology
Bioinformatics
03 medical and health sciences
0302 clinical medicine
Structural Biology
Modelling and Simulation
Germany
Databases
Genetic
Humans
Gene Regulatory Networks
education
Molecular Biology
lcsh:QH301-705.5
030304 developmental biology
0303 health sciences
education.field_of_study
Microarray analysis techniques
Applied Mathematics
Research
Gene Expression Profiling
Computational Biology
Feeding Behavior
Microarray Analysis
United States
3. Good health
Computer Science Applications
Gene expression profiling
Gene Expression Regulation
Neoplastic
Genetics
Population
lcsh:Biology (General)
030220 oncology & carcinogenesis
Modeling and Simulation
Identification (biology)
Colorectal Neoplasms
Transcriptome
Signal Transduction
Zdroj: BMC Systems Biology, Vol 6, Iss Suppl 3, p S17 (2012)
BMC Systems Biology
ISSN: 1752-0509
Popis: Background Colorectal cancer (CRC) is one of the most commonly diagnosed cancers worldwide. Studies have correlated risk of CRC development with dietary habits and environmental conditions. Gene signatures for any disease can identify the key biological processes, which is especially useful in studying cancer development. Such processes can be used to evaluate potential drug targets. Though recognition of CRC gene-signatures across populations is crucial to better understanding potential novel treatment options for CRC, it remains a challenging task. Results We developed a topological and biological feature-based network approach for identifying the gene signatures across populations. In this work, we propose a novel approach of using cliques to understand the variability within population. Cliques are more conserved and co-expressed, therefore allowing identification and comparison of cliques across a population which can help researchers study gene variations. Our study was based on four publicly available expression datasets belonging to four different populations across the world. We identified cliques of various sizes (0 to 7) across the four population networks. Cliques of size seven were further analyzed across populations for their commonality and uniqueness. Forty-nine common cliques of size seven were identified. These cliques were further analyzed based on their connectivity profiles. We found associations between the cliques and their connectivity profiles across networks. With these clique connectivity profiles (CCPs), we were able to identify the divergence among the populations, important biological processes (cell cycle, signal transduction, and cell differentiation), and related gene pathways. Therefore the genes identified in these cliques and their connectivity profiles can be defined as the gene-signatures across populations. In this work we demonstrate the power and effectiveness of cliques to study CRC across populations. Conclusions We developed a new approach where cliques and their connectivity profiles helped elucidate the variation and similarity in CRC gene profiles across four populations with unique dietary habits.
Databáze: OpenAIRE
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