Modelling of hypoxia gene expression for three different cancer cell lines
by Babak Soltanalizadeh; Erika Gonzalez Rodriguez; Vahed Maroufy; W. Jim Zheng; Hulin Wu
International Journal of Computational Biology and Drug Design (IJCBDD), Vol. 13, No. 1, 2020

Abstract: Gene dynamic analysis is essential in identifying target genes involved in the pathogenesis of various diseases, including cancer. Hypoxia often influences cancer prognosis. We applied 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). We identified 26 distinct temporal expression patterns in DU145 and 29 patterns in HT29 and MCF7. Module-based dynamic networks were developed for each cell line. Because our analyses exploited the time-dependent nature of gene expression for identifying significant genes novel significant genes and transcription factors were identified. Our gene network returned significant information regarding biologically important modules of genes. In particular, results suggest that changes expression of BMP6 and ARSJ might play a key role in the time-dependent response to hypoxia in breast cancer. Furthermore, the network can potentially learn the regulatory path between transcription factors and the downstream genes.

Online publication date: Thu, 13-Feb-2020

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