Transcriptional master regulator analysis in breast cancer genetic networks

Tovar, Hugo; García-Herrera, Rodrigo; Espinal-Enríquez, Jesús; Hernández‐Lemus, Enrique

doi:10.1016/j.compbiolchem.2015.08.007

Cited by 52 publications

(36 citation statements)

References 58 publications

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“…This platform contains probes for 18,400 transcripts and variants. Raw microarray data was processed following a pipeline for Robust Multi-array Average (Irizarry et al, 2003), previously implemented in our workgroup (Baca-López et al, 2012; Tovar et al, 2015). Breast cancer samples were classified using the well-validated algorithm (Parker et al, 2009).…”

Section: Methodsmentioning

confidence: 99%

“…A biological network , is a network where nodes represent any kind of biological molecules: genes, transcripts, proteins, metabolites, etc., and links represent physical or chemical interactions between those molecules (Jeong et al, 2000; Hasty et al, 2001; Jeong et al, 2001; Thattai and Van Oudenaarden, 2001; Lee et al, 2002; Maslov and Sneppen, 2002; Davidson and Levin, 2005; Guimera and Amaral, 2005; Levine and Davidson, 2005; Davidson and Erwin, 2006). With gene expression microarray technologies, it is factible to construct transcriptional networks where nodes are transcribed genes, and links represent a correlation between expression values of said genes, which point to a possible interaction between them at the transcriptional level (Tovar et al, 2015). …”

Section: Introductionmentioning

confidence: 99%

“…Global network metrics often provide information regarding the system as a whole; while local parameters provide information regarding the relevance of particular nodes (Barabasi and Oltvai, 2004; Newman, 2010; Barabási et al, 2011; Biane et al, 2016; Robinson and Nielsen, 2016). The transcriptional network approach has proven be useful to unveil transcriptional regulation in cancer (Carro et al, 2010; House et al, 2010; Pe'er and Hacohen, 2011; Madhamshettiwar et al, 2012) and in particular in breast cancer (Van De Vijver et al, 2002; Lim et al, 2009; Cicatiello et al, 2010; Gu et al, 2010; Tovar et al, 2015; Castro et al, 2016). …”

Section: Introductionmentioning

confidence: 99%

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Transcriptional Network Architecture of Breast Cancer Molecular Subtypes

et al. 2016

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Breast cancer heterogeneity is evident at the clinical, histological and molecular level. High throughput technologies allowed the identification of intrinsic subtypes that capture transcriptional differences among tumors. A remaining question is whether said differences are associated to a particular transcriptional program which involves different connections between the same molecules. In other words, whether particular transcriptional network architectures can be linked to specific phenotypes. In this work we infer, construct and analyze transcriptional networks from whole-genome gene expression microarrays, by using an information theory approach. We use 493 samples of primary breast cancer tissue classified in four molecular subtypes: Luminal A, Luminal B, Basal and HER2-enriched. For comparison, a network for non-tumoral mammary tissue (61 samples) is also inferred and analyzed. Transcriptional networks present particular architectures in each breast cancer subtype as well as in the non-tumor breast tissue. We find substantial differences between the non-tumor network and those networks inferred from cancer samples, in both structure and gene composition. More importantly, we find specific network architectural features associated to each breast cancer subtype. Based on breast cancer networks' centrality, we identify genes previously associated to the disease, either, generally (i.e., CNR2) or to a particular subtype (such as LCK). Similarly, we identify LUZP4, a gene barely explored in breast cancer, playing a role in transcriptional networks with subtype-specific relevance. With this approach we observe architectural differences between cancer and non-cancer at network level, as well as differences between cancer subtype networks which might be associated with breast cancer heterogeneity. The centrality measures of these networks allow us to identify genes with potential biomedical implications to breast cancer.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Transcriptional Network Architecture of Breast Cancer Molecular Subtypes

et al. 2016

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“…MR analyses have helped elucidate proteins that regulate tumour-associated phenotypes as diverse as predisposition 40,44 , subtype-specific tumorigenesis 10,43,50,51 , progression to aggressive or metastatic disease 9,11,12,45,47,52 , stroma-specific regulation of tumour outcome 41 and drug resistance 29,30 , most of which have been experimentally validated. In addition, their use in non-cancer phenotypes has helped to elucidate an equivalent disease checkpoint architecture in neurological phenotypes — including amyotrophic lateral sclerosis (ALS) 53 , Alzheimer disease 7,54 , Parkinson disease 55 and alcohol addiction 56 — and in developmental phenotypes, from regulation of germinal centre formation 39 to stem cell pluripotency 57 .…”

Section: Mr and Tumour Checkpoint Elucidationmentioning

confidence: 99%

The recurrent architecture of tumour initiation, progression and drug sensitivity

2016

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Recent studies across multiple tumour types are starting to reveal a recurrent regulatory architecture in which genomic alterations cluster upstream of functional master regulator (MR) proteins, the aberrant activity of which is both necessary and sufficient to maintain tumour cell state. These proteins form small, hyperconnected and autoregulated modules (termed tumour checkpoints) that are increasingly emerging as optimal biomarkers and therapeutic targets. Crucially, as their activity is mostly dysregulated in a post-translational manner, rather than by mutations in their corresponding genes or by differential expression, the identification of MR proteins by conventional methods is challenging. In this Opinion article, we discuss novel methods for the systematic analysis of MR proteins and of the modular regulatory architecture they implement, including their use as a valuable reductionist framework to study the genetic heterogeneity of human disease and to drive key translational applications.

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“…The negative regulatory effect of TFDP3 on E2F1 was speculated to result from a sequence localized to the C-terminus rather than the DNA binding region [13]. Recently, TFDP3 and some other transcription regulators were found to have a critical role in the gene interaction network in breast cancer [14]. The role of TFDP3 in EMT in breast cancer, however, has not been identified yet.…”

Section: Introductionmentioning

confidence: 99%

TFDP3 Regulates Epithelial-Mesenchymal Transition in Breast Cancer

et al. 2017

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Breast cancer remains a lethal disease to women due to lymph node metastasis, the tumor microenvironment, secondary resistance and other unknown factors. Several important transcription factors involved in this disease, such as PTEN, p53 and beta-catenin, have been identified and researched in-depth as candidates for targeted therapy in breast cancer. TFDP3 is a new, promising candidate for transcriptional regulation in breast cancer, although it was first identified in hepatocellular carcinoma. Here, we demonstrate that TFDP3 is expressed in a variety of malignancies, normal testis tissue and breast cancer cell lines and thus provide evidence that TFDP3 is a cancer-testis antigen. We illustrate that overexpression or silencing TFDP3 interferes with epithelial-mesenchymal transition but does not influence cell proliferation, indicating that the TFDP3 protein acts as a transcription factor during epithelial-mesenchymal transition. These data highlight that TFDP3 is expressed in breast cancer, that it is a member of the cancer-testis antigen family and that it functions as a regulator in epithelial-mesenchymal transition.

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Transcriptional master regulator analysis in breast cancer genetic networks

Cited by 52 publications

References 58 publications

Transcriptional Network Architecture of Breast Cancer Molecular Subtypes

Transcriptional Network Architecture of Breast Cancer Molecular Subtypes

The recurrent architecture of tumour initiation, progression and drug sensitivity

TFDP3 Regulates Epithelial-Mesenchymal Transition in Breast Cancer

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