Abstract
We present a new model of ESR1 network regulation based on analysis of Doxorubicin, Estradiol, and TNFα combination treatment in MCF-7. We used Doxorubicin as a therapeutic agent, TNFα as marker and mediator of an inflammatory microenvironment and 17β-Estradiol (E2) as an agonist of Estrogen Receptors, known predisposing factor for hormone-driven breast cancer, whose pharmacological inhibition reduces the risk of breast cancer recurrence. Based on the results of transcriptomics analysis, we found 71 differentially expressed genes that are specific for the combination treatment with Doxorubicin + Estradiol + TNFα in comparison with single or double treatments. The responsiveness to the triple treatment was examined for seven genes by qPCR, of which six were validated, and then extended to four additional cell lines differing for p53 and/or ER status. The results of differential regulation enrichment analysis highlight the role of the ESR1 network that included 36 of 71 specific differentially expressed genes. We propose that the combined activation of p53 and NF-kB transcription factors significantly influences ligand-dependent, ER-driven transcriptional responses, also of the ESR1 gene itself. These results provide a model of coordinated interaction of TFs to explain the Doxorubicin, E2 and TNFα induced repression mechanisms.
Highlights
Cells respond differently to estrogen and TNFα treatment[1]
The main result of this study is a new model of ESR1 network repression as an exclusive gene expression program of MCF-7 responding to the combinatorial treatment with Doxorubicin, TNFα and E2
We showed that the combinatorial activation of p53, NF-kB and ER transcription factors in MCF-7 induce exclusive gene expression program
Summary
Cells respond differently to estrogen and TNFα treatment[1]. reports vary, as TNFα and other cytokines were shown to activate ER, independently from the presence of the E2 ligand[13,14]. NASFinder follows a two-step procedure: (1) it tests an entire generic network to identify significant sub-networks with their associated topology and transcription factors that correlate with the expression of DEGs; (2) it functionally analyses the identified sub-networks and ranks them in term of the magnitude of the expression fold changes by using the network activity score index (NAS – see the Methods section for a precise definition) We used this method to understand the regulatory mechanisms that operate changes in gene expression due to the simultaneous combination treatments leading to activation of p53, ER and NF-kB transcription factors in the MCF-7 breast cancer cell line
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