Network-based identification of key proteins involved in apoptosis and cell cycle regulation.

objectivesCancer cells differ from normal body cells in their ability to divide indefinitely and to evade programmed cell death. Crosstalk between apoptosis and cell cycle processes promotes balance between proliferation and death, and limits population growth and survival of cells. However, intricate relationships between them and how they are able to manipulate the fate of cancer cells still remain to be clarified. Identification of key factors involved in both apoptosis and cell cycle regulation may help to address this problem. MATERIALS AND

methodsIdentification of such key proteins was carried out, using a series of bioinformatics methods, such as network construction and key protein identification.

resultsIn this study, we computationally constructed human apoptotic/cell cycle-related protein-protein interactions (PPIs) networks from five experimentally supported protein interaction databases, and further integrated these high-throughput data sets into a Naïve Bayesian model to predict protein functional connections. On the basis of modified apoptotic/cell cycle related PPI networks, we calculated and ranked all protein members involved in apoptosis and cell cycle regulation. Our results not only identified some already known key proteins such as p53, Rb, Myc and Src but also found that the proteasome, Cullin family members, kinases and transcriptional repressors play important roles in regulating apoptosis and the cell cycle. Furthermore, we found that the top 100 proteins ranked by PeC were enriched in some pathways such as those of cancer, the proteasome, the cell cycle and Wnt signalling.

conclusionsWe constructed the global human apoptotic/cell cycle related PPI network based on five online databases, and a Naïve Bayesian model. In addition, we systematically identified apoptotic/cell cycle related key proteins in cancer cells. These findings may uncover intricate relationships between apoptosis and cell cycle processes and thus provide further new clues towards future anticancer drug discovery.