Benchmarking pathway interaction network for colorectal cancer to identify dysregulated pathways

• Autor(a) principal: Wang,Q.
• Data de Publicação: 2017
• Outros Autores: Shi,C.-J., Lv,S.-H.
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Brazilian Journal of Medical and Biological Research
• Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-879X2017000500602
• topic: Pathway interaction network; Colorectal cancer; Dysregulated pathways; Principal component analysis; Protein-protein interaction

Different pathways act synergistically to participate in many biological processes. Thus, the purpose of our study was to extract dysregulated pathways to investigate the pathogenesis of colorectal cancer (CRC) based on the functional dependency among pathways. Protein-protein interaction (PPI) information and pathway data were retrieved from STRING and Reactome databases, respectively. After genes were aligned to the pathways, each pathway activity was calculated using the principal component analysis (PCA) method, and the seed pathway was discovered. Subsequently, we constructed the pathway interaction network (PIN), where each node represented a biological pathway based on gene expression profile, PPI data, as well as pathways. Dysregulated pathways were then selected from the PIN according to classification performance and seed pathway. A PIN including 11,960 interactions was constructed to identify dysregulated pathways. Interestingly, the interaction of mRNA splicing and mRNA splicing-major pathway had the highest score of 719.8167. Maximum change of the activity score between CRC and normal samples appeared in the pathway of DNA replication, which was selected as the seed pathway. Starting with this seed pathway, a pathway set containing 30 dysregulated pathways was obtained with an area under the curve score of 0.8598. The pathway of mRNA splicing, mRNA splicing-major pathway, and RNA polymerase I had the maximum genes of 107. Moreover, we found that these 30 pathways had crosstalks with each other. The results suggest that these dysregulated pathways might be used as biomarkers to diagnose CRC.