The analysis of sophisticated interplays between cell cycle-dependent genes in a disease condition is one of the largely unexplored areas in modern tumor biology research. Many cell cycle-dependent genes are either oncogenes or suppressor genes, or are closely associated with the transition of a cell cycle. However, it is unclear how the complicated relationships between these cell cycle-dependent genes are, especially in cancers. Here, we sought to identify significant expression relationships between cell cycle-dependent genes by analyzing a HeLa microarray dataset using a local alignment algorithm and constructed a gene transcriptional network specific to the cancer by assembling these newly identified gene-gene relationships. We further characterized this global network by partitioning the whole network into several cell cycle phase-specific sub-networks. All generated networks exhibited the power-law node-degree distribution, and the average clustering coefficients of these networks were remarkably higher than those of pure scale-free networks, indicating a property of hierarchical modularity. Based on the known protein-protein interactions and Gene Ontology annotation data, the proteins encoded by cell cycle-dependent interacting genes tended to share the same biological functions or to be involved in the same biological processes, rather than interacting by physical means. Finally, we identified the hub genes related to cancer based on the topological importance that maintain the basic structure of cell cycle-dependent gene networks.
Xiao, Y., Li, X., Yang, Y., Wang, Y., & Rao, S. (2008). Cell cycle-dependent gene networks relevant to cancer. Progress in Natural Science, 18(8), 945–952. https://doi.org/10.1016/j.pnsc.2008.03.003