Abstract
Gene dynamic analysis is essential in identifying target genes involved pathogenesis of various diseases, including cancer. Cancer prognosis is often influenced by hypoxia. We apply a multi-step pipeline to study dynamic gene expressions in response to hypoxia in three cancer cell lines: prostate (DU145), colon (HT29), and breast (MCF7) cancers. We identified 26 distinct temporal expression patterns for prostate cell line, and 29 patterns for colon and breast cell lines. The module-based dynamic networks have been developed for all three cell lines. Our analyses improve the existing results in multiple ways. It exploits the time-dependence nature of gene expression values in identifying the dynamically significant genes; hence, more key significant genes and transcription factors have been identified. Our gene network returns significant information regarding biologically important modules of genes. Furthermore, the network has potential in learning the regulatory path between transcription factors and the downstream genes. In addition, our findings suggest that changes in genes BMP6 and ARSJ expression might have a key role in the time-dependent response to hypoxia in breast cancer.