An ERα/modulator regulatory network in the breast cancer cells

Estrogens control multiple functions of hormone-responsive breast cancer (BC) cells [1]. They regulate diverse physiological processes in various tissues through genomic and non-genomic mechanisms that result in activation or repression of gene expression. Transcription regulation upon estrogen stimulation is a critical biological process underlying the onset and progress of the majority of breast cancer [2]. However, ERα requires distinct co-regulator complex or modulators for efficient transcriptional regulation. To have insight into the regulatory network of ERα and discover the novel modulators of ERα which acted by distinct mechanisms, we proposed an analytical method based on a linear regression model to identify translational modulators and the relationship between genes for network. To comprehend the network associated with ERα, a dynamic gene expression profile and ChIP-Seq data shown to characterize the breast cancer cell response to estrogens are utilized. The role of modulators within molecular mechanism can be learned from the exploration of these two data sets. Based on the active or repressive function of the ERα, active or repressive function of a modulator, and agonist or antagonist effect of a modulator on the ERα, the ERα/modulator/target relationships were categorized into 27 classes.

[1]  Laura Uusitalo,et al.  Advantages and challenges of Bayesian networks in environmental modelling , 2007 .

[2]  K. Dahlman-Wright,et al.  The gene regulatory networks controlled by estrogens , 2011, Molecular and Cellular Endocrinology.

[3]  Xiaobo Zhou,et al.  Construction of genomic networks using mutual-information clustering and reversible-jump Markov-chain-Monte-Carlo predictor design , 2003, Signal Process..

[4]  Yadong Wang,et al.  A modulated empirical Bayes model for identifying topological and temporal estrogen receptor α regulatory networks in breast cancer , 2011, BMC Systems Biology.

[5]  Chris Wiggins,et al.  ARACNE: An Algorithm for the Reconstruction of Gene Regulatory Networks in a Mammalian Cellular Context , 2004, BMC Bioinformatics.

[6]  Xiaobo Zhou,et al.  Gene Clustering Based on Clusterwide Mutual Information , 2004, J. Comput. Biol..

[7]  D. Botstein,et al.  Cluster analysis and display of genome-wide expression patterns. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[8]  P. D’haeseleer,et al.  Mining the gene expression matrix: inferring gene relationships from large scale gene expression data , 1998 .

[9]  Alvis Brazma,et al.  Current approaches to gene regulatory network modelling , 2007, BMC Bioinformatics.

[10]  Nicole Radde,et al.  Inferring Gene Regulatory Networks from Expression Data , 2019 .

[11]  Wei Wu,et al.  TREEGL: reverse engineering tree-evolving gene networks underlying developing biological lineages , 2011, Bioinform..

[12]  Oded Maimon,et al.  Evaluation of gene-expression clustering via mutual information distance measure , 2007, BMC Bioinformatics.

[13]  R. Lanz,et al.  Nuclear receptor coregulators: cellular and molecular biology. , 1999, Endocrine reviews.

[14]  Lorenzo Ferraro,et al.  Estrogen receptor alpha controls a gene network in luminal-like breast cancer cells comprising multiple transcription factors and microRNAs. , 2010, The American journal of pathology.

[15]  Julie A. Dickerson,et al.  Reconstructing genome-wide regulatory network of E. coli using transcriptome data and predicted transcription factor activities , 2011, BMC Bioinformatics.

[16]  Min Zou,et al.  A new dynamic Bayesian network (DBN) approach for identifying gene regulatory networks from time course microarray data , 2005, Bioinform..

[17]  I S Kohane,et al.  Mutual information relevance networks: functional genomic clustering using pairwise entropy measurements. , 1999, Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing.

[18]  Xiaodong Wang,et al.  Gene Regulatory Network Reconstruction Using Conditional Mutual Information , 2008, EURASIP J. Bioinform. Syst. Biol..