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

• Published in: BMC systems biology Vol. 6 Suppl 3; no. Suppl 3; p. S17
• Main Authors: Pradhan, Meeta P, Nagulapalli, Kshithija, Palakal, Mathew J
• Format: Journal Article
• Language: English
• Published: England BioMed Central 17.12.2012
• Subjects: Algorithms; Biology; Biomarkers; Cell cycle; China; Colorectal cancer; Colorectal Neoplasms - genetics; Colorectal Neoplasms - pathology; Computational Biology - methods; Conferences; Databases, Genetic; Differentiation; Drug development; Environmental conditions; Feeding Behavior; Gene Expression Profiling; Gene Expression Regulation, Neoplastic; Gene Regulatory Networks; Genetics, Population - methods; Germany; Humans; Hypotheses; Microarray Analysis; Ontology; Population studies; Saudi Arabia; Signal Transduction; Studies; Transcriptome; United States
• ISSN: 1752-0509; 1752-0509
• DOI: 10.1186/1752-0509-6-S3-S17

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. 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. 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.