Regulatory Network In Breast Cancer Research Articles

Unraveling the cross-talk between N6-methyladenosine modification and non-coding RNAs in breast cancer: Mechanisms and clinical implications.

In recent years, breast cancer (BC) has surpassed lung cancer as the most common malignant tumor worldwide and remains the leading cause of cancer death in women. The etiology of BC usually involves dysregulation of epigenetic mechanisms and aberrant expression of certain non-coding RNAs (ncRNAs). N6-methyladenosine (m6A), the most prevalent RNA modification in eukaryotes, widely exists in ncRNAs to affect its biosynthesis and function, and is an important regulator of tumor-related signaling pathways. Interestingly, ncRNAs can also regulate or target m6A modification, playing a key role in cancer progression. However, the m6A-ncRNAs regulatory network in BC has not been fully elucidated, especially the regulation of m6A modification by ncRNAs. Therefore, in this review, we comprehensively summarize the interaction mechanisms and biological significance of m6A modifications and ncRNAs in BC. Meanwhile, we also focused on the clinical application value of m6A modification in BC diagnosis and prognosis, intending to explore new biomarkers and potential therapeutic targets.

Large-scale loss-of-function perturbations reveal a comprehensive epigenetic regulatory network in breast cancer.

Epigenetic abnormalities have a critical role in breast cancer by regulating gene expression; however, the intricate interrelationships and key roles of approximately 400 epigenetic regulators in breast cancer remain elusive. It is important to decipher the comprehensive epigenetic regulatory network in breast cancer cells to identify master epigenetic regulators and potential therapeutic targets. We employed high-throughput sequencing-based high-throughput screening (HTS2) to effectively detect changes in the expression of 2,986 genes following the knockdown of 400 epigenetic regulators. Then, bioinformatics analysis tools were used for the resulting gene expression signatures to investigate the epigenetic regulations in breast cancer. Utilizing these gene expression signatures, we classified the epigenetic regulators into five distinct clusters, each characterized by specific functions. We discovered functional similarities between BAZ2B and SETMAR, as well as CLOCK and CBX3. Moreover, we observed that CLOCK functions in a manner opposite to that of HDAC8 in downstream gene regulation. Notably, we constructed an epigenetic regulatory network based on the gene expression signatures, which revealed 8 distinct modules and identified 10 master epigenetic regulators in breast cancer. Our work deciphered the extensive regulation among hundreds of epigenetic regulators. The identification of 10 master epigenetic regulators offers promising therapeutic targets for breast cancer treatment.

Evaluation of Circ_0000977-Mediated Regulatory Network in Breast Cancer: A Potential Discriminative Biomarker for Triple-Negative Tumors.

Previous investigations have revealed that circular RNAs (circRNAs) play pivotal roles in cancer development and progression by participating in several biological procedures, such as competing endogenous RNA (ceRNA) networks. Recently, circRNAs have been proposed as non-invasive, stable, and affordable cell-free biomarkers for cancer screening and test monitoring. Although, their clinical usefulness vastly remains to be evaluated in breast cancer (BC). Triple-negative breast cancer (TNBC), as the most challenging BC subtype, is an urgent requirement of identifying specific biomarkers and discovering the molecular mechanisms that lead to aggressive behaviors of tumor cells. The therapeutic strategies for TN patients have remained limited due to the impracticality of endocrine therapies and a remarkable portion of patients with TNBC experience recurrence, chemoresistance, and metastasis. TNBC Microarray expression profile analysis found that circ_0000977 is one of the most dysregulated circRNA in TNBC in comparison with non-TNBC. It could be a clue referring to the potential clinical utility of circ_0000977 in TNBC. The current study aims to assess the clinical implications and potential ceRNA regulatory network of circ_0000977 in TNBC. We confirmed circ_0000977 down-regulation in TNBC cell lines and tumors versus non-TNBC samples by real-time PCR. Subsequently, an assessment of the diagnostic value of circ_0000977 in plasma samples from triple-negative patients revealed a potential diagnostic cell-free biomarker in triple-negative BC. Finally, our integrative approach uncovered potential circ-0000977/miR-135b-5p/mRNAs regulatory network in TNBC. The inhibitory effect of miR-135b-5p on its downstream mRNAs was assessed by knocking down it in MDA-MB-231 cells. Functional and correlation analyses revealed APC and GATA3 could be regulated by circ_0000977/miR-135b-5p ceRNA axis, which presents valuable insight into circ-0000977-mediated gene silencing involved in the ceRNA network of TNBC. This study uncovered the potential clinical implication of circ_0000977 for the diagnosis and treatment of TNBC patients.

Doxorubicin resistance in breast cancer xenografts and cell lines can be counterweighted by microRNA-140-3p, through PD-L1 suppression.

Doxorubicin, a first-line chemotherapeutic drug for breast cancer, kills cancer cells by inducing DNA-crosslinking damage. Dysregulated micro-RNA (miRNA) is associated with the drug resistance of tumors. However, little is known about the effect of miRNA-140-3p on DOX resistance of breast cancer. The miRNA microarray was used to sequence the transcripts of DOX-chemoresistant breast cancer tissues and DOX-chemosensitive tissues. Then, the breast cancer tissue chip in the GEO database was also analyzed to screen the target gene. Flow cytometry, in situ hybridisation (ISH), immunohistochemistry (IHC), Western blot, cell proliferation assay, real-time PCR analyses (qRT-PCR), and pull-down assay were used to explore the effects of miRNA-140-3p and programmed death ligand-1 (PD-L1) on the chemoresistance of DOX-resistant breast cancer cells treated with DOX. In vivo, the DOX-resistant breast cancer cell lines treated with miRNA-140-3p overexpression were injected subcutaneously into mice to construct breast cancer subcutaneous xenograft tumor models. Based on miRNA microarray, GEO database, and bioinformatics analysis, it was found that miRNA-140-3p and PD-L1 are the core molecules in the DOX resistance regulatory network in breast cancer, and lower miRNA-140-3p and higher PD-L1 expression levels were observed in DOX-resistant breast cancer tissues and cells. IHC results showed that compared with breast cancer tissues with high miRNA-140-3p expression, PD-L1 protein expression levels in breast cancer tissues with low miRNA-140-3p were significantly higher (P<0.01). Moreover, compared with DOX-sensitive tissues, the levels of PD-L1 protein expression in DOX-resistant tissues were significantly higher (P<0.01). In in vitro and in vivo experiments, the introduction of miRNA-140-3p decreased PD-L1 expression. Mechanically, we found that the MCF-7/DOX and HS598T/DOX cells pretreated with miRNA-140-3p inhibitor or exosomes containing PD-L1 have higher stemness and lower apoptosis rate, which can be abrogated by co-treating cells with anti-PD-L1 antibody or miRNA-140-3p mimic. MiRNA-140-3p can suppress PD-L1 expression in breast cancer cell-derived exosomes, thereby attenuating the chemoresistance induced by DOX in breast cancer.

Dissecting super-enhancer heterogeneity: time to re-examine cancer subtypes?

The heterogeneity of transcriptional regulations by super-enhancers (SEs) is poorly understood in human cancers. Herein, we summarize a bioinformatics workflow for genome-wide SE profiling and identification of subtype-specific SEs and regulatory networks. Dissecting SE heterogeneity provides new insights into cancer biology and alternative therapeutic strategies for cancer precision medicine.

Multiple types of noncoding RNA are involved in potential modulation of PTTG1's expression and function in breast cancer

Breast cancer is a malignant type with morbidity ranking the first of women globally. As widely acknowledged, there exist close links between ncRNA-mRNA axis and breast cancer. In this study, we first overviewed expression and prognostic values of pituitary tumor transforming gene (PTTGs) in breast cancer. Next, two binding miRNAs (miR-186-5p and miR-655-3p) of PTTG1 in breast cancer were identified. Subsequently, several potential upstream ncRNAs of PTTG1-miR-186-5p/miR-655-3p axis in breast cancer were successively screened out, consisting of 11 lncRNAs, 17 circRNAs and 12 pseudogene-derived RNAs. Enrichment analysis for downstream target genes of PTTG1-miR-186-5p/miR-655-3p axis revealed that this axis is associated with TGF-beta signaling and MAPK signaling pathways. Further investigation demonstrated AURKA was one of the most key hub genes. Collectively, we established a potential PTTG1-related ncRNA-mRNA regulatory network in breast cancer.

Prediction of Disease Genes Based on Stage-Specific Gene Regulatory Networks in Breast Cancer.

Breast cancer is one of the most common malignant tumors in women, which seriously endangers women’s health. Great advances have been made over the last decades, however, most studies predict driver genes of breast cancer using biological experiments and/or computational methods, regardless of stage information. In this study, we propose a computational framework to predict the disease genes of breast cancer based on stage-specific gene regulatory networks. Firstly, we screen out differentially expressed genes and hypomethylated/hypermethylated genes by comparing tumor samples with corresponding normal samples. Secondly, we construct three stage-specific gene regulatory networks by integrating RNA-seq profiles and TF-target pairs, and apply WGCNA to detect modules from these networks. Subsequently, we perform network topological analysis and gene set enrichment analysis. Finally, the key genes of specific modules for each stage are screened as candidate disease genes. We obtain seven stage-specific modules, and identify 20, 12, and 22 key genes for three stages, respectively. Furthermore, 55%, 83%, and 64% of the genes are associated with breast cancer, for example E2F2, E2F8, TPX2, BUB1, and CKAP2L. So it may be of great importance for further verification by cancer experts.

Identification of Seven Cell Cycle-Related Genes with Unfavorable Prognosis and Construction of their TF-miRNA-mRNA regulatory network in Breast Cancer.

Breast cancer (BC), with complex tumorigenesis and progression, remains the most common malignancy in women. We aimed to explore some novel and significant genes with unfavorable prognoses and potential pathways involved in BC initiation and progression via bioinformatics methods. BC tissue-specific microarray datasets of GSE42568, GSE45827 and GSE54002, which included a total of 651 BC tissues and 44 normal breast tissues, were obtained from the Gene Expression Omnibus (GEO) database, and 124 differentially expressed genes (DEGs) were identified between BC tissues and normal breast tissues via R software and an online Venn diagram tool. Database for Annotation, Visualization and Integration Discovery (DAVID) software showed that 65 upregulated DEGs were mainly enriched in the regulation of the cell cycle, and Search Tool for the Retrieval of Interacting Genes (STRING) software identified the 39 closest associated upregulated DEGs in protein-protein interactions (PPIs), which validated the high expression of genes in BC tissues by the Gene Expression Profiling Interactive Analysis (GEPIA) tool. In addition, 36 out of 39 BC patients showed significantly worse outcomes by Kaplan-Meier plotter (KM plotter), and an additional Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis revealed that seven genes (cyclin E2 (CCNE2), cyclin B1 (CCNB1), cyclin B2 (CCNB2), mitotic checkpoint serine/threonine kinase B (BUB1B), dual-specificity protein kinase (TTK), cell division cycle 20 (CDC20), and pituitary tumor transforming gene 1 (PTTG1)) were markedly enriched in the cell cycle pathway. Analysis of the clinicopathological characteristics of hub genes revealed that seven cell cycle-related genes (CCRGs) were significantly highly expressed in four BC subtypes (luminal A, luminal B, HER2-positive and triple-negative (TNBC)), and except for the CCNE2 gene, high expression levels were significantly associated with tumor pathological grade and stage and metastatic events of BC. Furthermore, genetic mutation analysis indicated that genetic alterations of CCRGs could also significantly affect BC patients' prognosis. A quantitative real-time polymerase chain reaction (qRT-PCR) assay found that the seven CCRGs were significantly differentially expressed in BC cell lines. Integration of published multilevel expression data and a bioinformatics computational approach were used to predict and construct a regulation mechanism: a transcription factor (TF)-microRNA (miRNA)-messenger RNA (mRNA) regulation network. The present work is the first to construct a regulatory network of TF-miRNA-mRNA in BC for CCRGs and provides new insights into the molecular mechanism of BC.

Construction of a circRNA-Related ceRNA Prognostic Regulatory Network in Breast Cancer.

PurposeAccumulating evidence has indicated that circRNAs are closely involved in tumorigenesis and progression of human cancers. However, the molecular mechanism underlying function of circRNAs in breast cancer has not been thoroughly elucidated. Currently, we aimed to characterize the circRNA-related competing endogenous RNA (ceRNA) regulatory network in breast cancer and to construct prognostic model.Materials and MethodsFirst, we constructed circRNA expression profiles for paired breast cancer in a Chinese population using a human circRNA microarray. Expression profiles of circRNAs, miRNAs, and mRNAs were retrieved from our circRNA dataset, the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. We applied the limma and edgeR packages to identify differentially expressed RNAs. Weighted gene correlation network analysis (WGCNA) was used to identify critical modules of mRNAs. Next, a ceRNA network was established based on circRNA–miRNA and miRNA–mRNA intersections. Both Cox regression analysis and ROC curve analysis were performed to generate prognostic model. Additionally, we performed Gene Set Enrichment Analysis (GSEA) on prognostic signatures.ResultsTotal of 59 circRNAs, 98 miRNAs and 3966 mRNAs were identified as differentially expressed RNAs. We first identified 38 miRNA-mRNA pairs and 38 circRNA-miRNA pairs to construct the circRNA–miRNA-mRNA regulatory network and then generated a prognostic model based on 7 signatures (MMD, SLC29A4, CREB5, FOS, ANKRD29, MYOCD, and PIGR), and patients with high-risk scores presented poor prognosis. Several cancer-related pathways were enriched, including the TGF-β pathway, the focal adhesion pathway, and the JAK-STAT signaling pathway, and 20 prognostic ceRNA regulatory networks were subsequently identified.ConclusionIn all, we screened a series of dysregulated circRNAs, miRNAs, and mRNAs, and constructed circRNA-related ceRNA network in breast cancer. Our findings may help to deepen the understanding of circRNA-related regulatory mechanisms. Moreover, we generated a prognostic model that provided new insight into postoperative management for breast cancer.

Identification of DGUOK-AS1 as a Prognostic Factor in Breast Cancer by Bioinformatics Analysis

Background: Significant developments have been made in breast cancer diagnosis and treatment, yet the prognosis remains unsatisfactory. Accumulating evidence indicates that long non-coding RNAs (lncRNAs) play pivotal roles in the development and progression of human tumors. However, the regulatory mechanisms and clinical significance of most lncRNAs in breast cancer remain poorly understood.Methods: The lncRNA, miRNA, and mRNA expression profiles were obtained from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. A lncRNA-miRNA-mRNA regulatory network was constructed and visualized using Cytoscape. The protein-protein interaction (PPI) network was constructed using the STRING database and hub genes were extracted using the cytoHubba plugin. Gene Ontology and Kyoto Encyclopedia of Gene and Genomes analyses identified the functions and signaling pathways associated with these differentially expressed mRNAs (DEmRNAs). Expression of the key lncRNA and the relationship with prognosis of patients with breast cancer were evaluated.Results: Six differentially expressed lncRNAs (DElncRNAs), 29 differentially expressed miRNAs (DEmiRNAs), and 253 DEmRNAs were selected to construct the regulatory network. A PPI network was established and seven hub genes were identified. A lncRNA-miRNA-hub gene regulatory sub-network was established containing two DElncRNAs, five DEmiRNAs, and seven DEmRNAs. Hub genes were associated with breast cancer onset and progression. The upregulated DGUOK-AS1 was identified as the key lncRNA in breast cancer based on the competing endogenous RNA network. High DGUOK-AS1 expression was associated with adverse prognosis in patients with breast cancer and a prognostic nomogram built on Grade, LN status, and DGUOK-AS1 expression shows significant prognostic value.Conclusions: Our results reveal the significant roles of lncRNA/miRNA/mRNA regulatory networks in breast cancer and identified a novel prognosis predictor and promising therapeutic target for patients with breast cancer.

Identification of epithelial-mesenchymal transition-related circRNA-miRNA-mRNA ceRNA regulatory network in breast cancer

BackgroundCircular RNAs (circRNAs) have attracted lots of attention in tumorigenesis and progression. However, circRNAs as crucial regulators in epithelial-mesenchymal transition have not been systematically identified in breast cancer. The purpose of our research was to investigate the circRNA network associated with epithelial-mesenchymal transition in breast cancer. MethodsExpression profiling data of circRNAs were identified by circRNA microarray in transfected ZEB1 and control breast cancer cells. The differentially expressed circRNAs, miRNAs, and mRNAs were determined via fold change filtering. The competing endogenous RNAs (ceRNAs) network was established on the foundation of the relationship between circular RNAs, miRNAs and mRNAs. The CytoHubba was used to determine the hub genes from the protein-protein interaction (PPI) regulatory network. The GEPIA database was used to observe the expression of the hub genes mRNA between breast cancer tissues and normal tissues. The HPA database was applied to investigate the expression of six hub genes at the protein level. Morever, we further used Kaplan–Meier plotter to perform survival analysis of these hub genes. ResultsThe top three up-regulated differential expressed circRNAs were identified by circRNA microarray. Following the Real-time PCR validation of the three circRNAs, two circRNAs (hsa_circRNA_002082 and hsa_circRNA_400031) were selected for further analysis. After the predicted target miRNA, ten circRNA-miRNA interactions including two circRNAs and ten miRNAs were determined. Furthermore, the Venn diagram was used to intersect the predicted target genes and the differentially expressed genes, and screened 174 overlapped genes. Subsequently, we constructed a PPI network, and selecting six hub genes, containing KIF4A, CENPF, OIP5, ZWINT, DEPDC1, BUB1B. The mRNA expression levels of the six hub genes were obviously up-regulated in breast cancer. The protein expression levels of KIF4A, CENPF, OIP5, and DEPDC1 were significantly increased in breast cancer tissues. Moreover, the survival analysis results revealed that high expression of the six hub genes were obviously correlated with poor prognosis of breast cancer patients. ConclusionsOur study constructed and analyzed a circRNA-associated ceRNA regulatory network and discovered that hsa_circRNA_002082 and hsa_circRNA_400031 may mechanism as ceRNAs to serve key roles in breast cancer epithelial-mesenchymal transition.

Effects of CORO2A on Cell Migration and Proliferation and Its Potential Regulatory Network in Breast Cancer.

Coronin 2A (CORO2A) is a novel component of the N-CoR (nuclear receptor co-repressor) complex. Abnormal CORO2A expression is associated with carcinogenesis. We used databases from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), and analyzed CORO2A expression and gene regulation networks in breast cancer. Expression was analyzed using GEO and TCGA database and further validated in breast cancer samples collected in our clinic. The prognostic value of CORO2A was explored by using the Kaplan–Meier survival analysis and Cox proportional hazards regression analysis. LinkedOmics was used to identify coexpressed genes associated with CORO2A. After analyzing the intersection of coexpressed genes correlated with CORO2A and differentially expressed genes after CORO2A silencing, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses of the intersecting genes were conducted by using FunRich software. Transwell assays were performed in breast cancer cells to determine the effect of CORO2A on cell migration. MTS, colony formation, and cell cycle distribution assays were performed in breast cancer cells to determine the effect of CORO2A on cell proliferation. Gene enrichment analysis was employed to explore the target networks of transcription factors and miRNAs. We found that CORO2A was upregulated and that the elevated expression of CORO2A was associated with poor overall survival (OS) and relapse-free survival (RFS) in TNBC patients. Further bioinformatics analysis of public sequencing data and our own RNA-Seq data revealed that CORO2A was probably involved in the epithelial-to-mesenchymal transition process and might have a significant effect on the migration of breast cancer cells, which might be mediated via pathways involving several miRNAs and MYC transcription factors. Functionally, the knockdown of CORO2A inhibited cell migration, decreased viability, and colony formation and induced cell cycle arrest in the G0/G1 phase in breast cancer cells. These results demonstrate that bioinformatics-based analysis efficiently reveals information about CORO2A expression and its potential regulatory networks in breast cancer, laying a foundation for further mechanistic research on the role of CORO2A in carcinogenesis. Moreover, CORO2A promotes the migration and proliferation of breast cancer cells and may have an important function in breast cancer progression. CORO2A is a potential prognostic predictor for TNBC patients. Targeting CORO2A may provide promising therapy strategies for breast cancer treatment.

Regulatory network reconstruction of five essential microRNAs for survival analysis in breast cancer by integrating miRNA and mRNA expression datasets

Although many of the genetic loci associated with breast cancer risk have been reported, there is a lack of systematic analysis of regulatory networks composed of different miRNAs and mRNAs on survival analysis in breast cancer. To reconstruct the microRNAs-genes regulatory network in breast cancer, we employed the expression data from The Cancer Genome Atlas (TCGA) related to five essential miRNAs including miR-21, miR-22, miR-210, miR-221, and miR-222, and their associated functional genomics data from the GEO database. Then, we performed an integration analysis to identify the essential target factors and interactions for the next survival analysis in breast cancer. Based on the results of our integrated analysis, we have identified significant common regulatory signatures including differentially expressed genes, enriched pathways, and transcriptional regulation such as interferon regulatory factors (IRFs) and signal transducer and activator of transcription 1 (STAT1). Finally, a reconstructed regulatory network of five miRNAs and 34 target factors was established and then applied to survival analysis in breast cancer. When we used expression data for individual miRNAs, only miR-21 and miR-22 were significantly associated with a survival change. However, we identified 45 significant miRNA-gene pairs that predict overall survival in breast cancer out of 170 one-on-one interactions in our reconstructed network covering all of five miRNAs, and several essential factors such as PSMB9, HLA-C, RARRES3, UBE2L6, and NMI. In our study, we reconstructed regulatory network of five essential microRNAs for survival analysis in breast cancer by integrating miRNA and mRNA expression datasets. These results may provide new insights into regulatory network-based precision medicine for breast cancer.

Identification of breast cancer mechanism based on weighted gene coexpression network analysis.

Our gene expression-profiling analysis aimed to explain the mechanism of breast cancer development by identifying key pathways and constructing networks of related transcription factors (TFs) and microRNAs (miRNAs) in breast cancer tissues. Gene expression profiles of normal and breast cancer tissues were downloaded to identify differentially expressed genes (DEGs). Coexpression modules were explored using weighted gene coexpression network analysis (WGCNA). Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses were performed to discover the enriched functionally associated gene groups and define pathways in breast cancer, respectively. miRNAs-DEGs and TF-DEG regulatory networks were constructed using Cytoscape. CDK6(cyclin-dependent kinase), miR-124, EGF(epidermal growth factor) and NF-κB(nuclear factor of kappa light polypeptide gene enhancer in B-cells 1) expression was also analyzed using real-time quantitative PCR. Totally, 7713 DEGs were identified for WGCNA. The results revealed that 1388 upregulated DEGs were associated with protein transport, protein localization and organic substance transport, whereas 1819 downregulated DEGs were associated with cancer and Wnt signaling pathways. Five miRNAs (miR-760, miR-1276, miR-124, miR-124-3p and miR-506-3p) with a degree of ⩾15 and one important TF (NF-κB) were identified in miRNA and TF regulatory networks. CDK6 mRNA and miR-124 expression was significantly reduced and EGF mRNA expression was clearly enhanced in cancer tissues compared with those in normal breast tissues. The CDK6 gene could be regulated by miR-124, which is involved in Wnt signaling and cancer pathways. NF-κB might initiate the breast cancer pathway by targeting EGF in human breast cancer tissues. This putative information on regulatory networks in breast cancer will be beneficial for future researches on mechanisms underlying its development.

Gene regulatory pattern analysis reveals essential role of core transcriptional factors' activation in triple-negative breast cancer.

BackgroundTriple-negative breast cancer (TNBC) is an aggressive breast cancer subtype. Genome-scale molecular characteristics and regulatory mechanisms that distinguish TNBC from other subtypes remain incompletely characterized.ResultsBy combining gene expression analysis and PANDA network, we defined three different TF regulatory patterns. A core TNBC-Specific TF Activation Driven Pattern (TNBCac) was specifically identified in TNBC by computational analysis. The essentialness of core TFs (ZEB1, MZF1, SOX10) in TNBC was highlighted and validated by cell proliferation analysis. Furthermore, 13 out of 35 co-targeted genes were also validated to be targeted by ZEB1, MZF1 and SOX10 in TNBC cell lines by real-time quantitative PCR. In three breast cancer cohorts, non-TNBC patients could be stratified into two subgroups by the 35 co-targeted genes along with 5 TFs, and the subgroup that more resembled TNBC had a worse prognosis.MethodsWe constructed gene regulatory networks in breast cancer by Passing Attributes between Networks for Data Assimilation (PANDA). Co-regulatory modules were specifically identified in TNBC by computational analysis, while the essentialness of core translational factors (TF) in TNBC was highlighted and validated by in vitro experiments. Prognostic effects of different factors were measured by Log-rank test and displayed by Kaplan-Meier plots.ConclusionsWe identified a core co-regulatory module specifically existing in TNBC, which enabled subtype re-classification and provided a biologically feasible view of breast cancer.

Formal modeling and analysis of ER-α associated Biological Regulatory Network in breast cancer.

BackgroundBreast cancer (BC) is one of the leading cause of death among females worldwide. The increasing incidence of BC is due to various genetic and environmental changes which lead to the disruption of cellular signaling network(s). It is a complex disease in which several interlinking signaling cascades play a crucial role in establishing a complex regulatory network. The logical modeling approach of René Thomas has been applied to analyze the behavior of estrogen receptor-alpha (ER-α) associated Biological Regulatory Network (BRN) for a small part of complex events that leads to BC metastasis.MethodsA discrete model was constructed using the kinetic logic formalism and its set of logical parameters were obtained using the model checking technique implemented in the SMBioNet software which is consistent with biological observations. The discrete model was further enriched with continuous dynamics by converting it into an equivalent Petri Net (PN) to analyze the logical parameters of the involved entities.ResultsIn-silico based discrete and continuous modeling of ER-α associated signaling network involved in BC provides information about behaviors and gene-gene interaction in detail. The dynamics of discrete model revealed, imperative behaviors represented as cyclic paths and trajectories leading to pathogenic states such as metastasis. Results suggest that the increased expressions of receptors ER-α, IGF-1R and EGFR slow down the activity of tumor suppressor genes (TSGs) such as BRCA1, p53 and Mdm2 which can lead to metastasis. Therefore, IGF-1R and EGFR are considered as important inhibitory targets to control the metastasis in BC.ConclusionThe in-silico approaches allow us to increase our understanding of the functional properties of living organisms. It opens new avenues of investigations of multiple inhibitory targets (ER-α, IGF-1R and EGFR) for wet lab experiments as well as provided valuable insights in the treatment of cancers such as BC.

Abstract PR06: Analysis of enhancer transcription reveals novel gene regulatory networks in breast cancer

Abstract Enhancer transcription is a defining feature of active enhancers. We and others have shown that enhancers that produce transcripts (so called “eRNAs”) are more likely to (1) be associated with active chromatin marks, such as H3K4me1 and K3K27ac, (2) loop to target gene promoters, and (3) be associated with target gene activation. Thus, enhancer transcription is a good predictor of active enhancers. In this regard, we have shown that enhancer transcription can be used in the absence of any other genomic information to predict enhancers. We have used Global Run-On coupled deep sequencing (GRO-seq) in 14 different breast cancer cell lines representing the five distinct molecular subtypes of breast cancer, coupled with a computational pipeline that we have developed, to predict enhancers based solely on enhancer transcription. We found both common and unique sites of enhancer transcription across the cell lines. In addition, we observed that enhancer transcription correlates with nearest gene transcription. Unsupervised hierarchical clustering of enhancer transcription was sufficient to segregate the breast cancer cell lines into their specific molecular subtypes. Transcription factor motif analysis performed at the sites of enhancer transcription identified transcription factors that may be important for the transcriptional programs of each cell type. Transcription factors whose motifs were uniquely enriched in a specific cell type were observed to be bound at the enhancers using locus-specific ChIP-qPCR assay. siRNA-mediated knockdown of the cognate transcription factors reduced enhancer transcription and cell proliferation in those cell types. The GRO-seq data were integrated with ChIP-seq data for several histone modifications typically enriched at promoters, gene bodies, enhancers, and repressive regions of the genome. The results from these analyses provide additional support for our enhancer identification pipeline. Taken together, our analyses have revealed novel gene regulatory networks that underlie breast cancer subtype-specific biology. This work was supported by a postdoctoral fellowship from the American Cancer Society - Lee National Denim Day Fellowship to H.L.F., grants from the NIH/NIDDK and CPRIT to W.L.K. and a grant from CPRIT to the LONESTAR Consortium. Citation Format: Hector L. Franco, Anusha Nagari, Yuanxin Xi, Wenqian Li, Dana Richardson, Kaori Tanaka, Jing Li, The CPRIT LONESTAR Consortium, Michelle C. Barton, Xiaobing Shi, Khandan Keyomarsi, Mark T. Bedford, Wei Li, Sharon Y.R. Dent, W. Lee Kraus. Analysis of enhancer transcription reveals novel gene regulatory networks in breast cancer. [abstract]. In: Proceedings of the AACR Special Conference on Chromatin and Epigenetics in Cancer; Sep 24-27, 2015; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2016;76(2 Suppl):Abstract nr PR06.

Abstract 363: Planar filtered gene regulatory networks in breast cancer

Abstract Gene co-expression network analysis has been shown to be effective in identifying functional co-expressed gene modules associated with complex human diseases such as cancer, Alzheimers’ disease, obesity and diabetes. However, existing techniques to construct co-expression networks require some critical prior information such as predefined number of modules to be detected and numerical thresholds for defining coexpression/interaction, or do not naturally reproduce the hallmarks of complex systems such as scale-free degree distribution of small-worldness). In order to mitigate these problems as whole or in part, we have developed a novel co-expression network analysis approach, Planar Filtered Network Analysis (PFNA). PFNA utilizes a graph filtering technique to identify “true” interactions by means of embedding candidate interactions on a topological sphere(2, 3). Planar Filtered Networks (PFN) are naturally scale-free, small-world, and are comprised of a number of highly co-expressed gene modules). Furthermore, PFNA allows differential topology analysis of networks from different disease states based on centrality and peripherality metrics). We performed PFNA on the breast cancer data from The Cancer Genome Atlas (TCGA), and identified a number of novel gene modules associated with overall survival of the whole cohort or patient subgroups defined by receptor status (ER, PR, HER2) and PAM50 biomarkers(6). PFNA uncovers not only gene modules enriched for genes in well-known cancer pathways such as cell-cycle and immune response, but also novel modules that prognostically stratify patients with triple negative or basal breast cancers. Notably, several modules involved in G-protein coupled receptor signaling, protocadherin α and β pathways are highly predictive of survival for patients with triple negative disease. Furthermore, the differential topology analysis reveals that breast cancer biomarkers such as PAM50 and poor prognosis gene sets developed by van’t Veer et al(7) are more central in the tumor network than the one based on the matched adjacent normal breast tissues. In summary, PFNA reveals a number of novel high-level molecular features of the highly heterogeneous breast cancer. This novel network analysis method provides a set of unsupervised tools to objectively identify subnetworks that are associated with complex diseases such as breast cancer. 1. R. Albert, A. L. Barabasi. Rev Mod Phys 74, 47. 2. M.Tumminello, T. Aste, T. Di Matteo, R. N. Mantegna. Proc Natl Acad Sci U S A 102,10421. 3. T.Aste, T. Di Matteo, S. T. Hyde. Physica A 346, 20. 4. W.M. Song, T. Di Matteo, T. Aste. Phys RevE Stat Nonlin Soft Matter Phys 85,046115. 5. F.Pozzi, T. Di Matteo, T. Aste. Adv Complex Syst 11, 927. 6. J. S. Parker et al.. Journal of clinical oncology: 27, 1160. 7. L.J. van 't Veer et al.. Nature 415, 530. Citation Format: Won-min Song, Tao Huang, Seungyeul Yoo, EunJee Lee, Yongzhong Zhao, Li Wang, Zhidong Tu, Xudong Dai, Hanna Irie, Jun Zhu, Bin Zhang. Planar filtered gene regulatory networks in breast cancer. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 363. doi:10.1158/1538-7445.AM2014-363

Using graphical adaptive lasso approach to construct transcription factor and microRNA's combinatorial regulatory network in breast cancer.

Discovering the regulation of cancer-related gene is of great importance in cancer biology. Transcription factors and microRNAs are two kinds of crucial regulators in gene expression, and they compose a combinatorial regulatory network with their target genes. Revealing the structure of this network could improve the authors' understanding of gene regulation, and further explore the molecular pathway in cancer. In this article, the authors propose a novel approach graphical adaptive lasso (GALASSO) to construct the regulatory network in breast cancer. GALASSO use a Gaussian graphical model with adaptive lasso penalties to integrate the sequence information as well as gene expression profiles. The simulation study and the experimental profiles verify the accuracy of the authors' approach. The authors further reveal the structure of the regulatory network, and explore the role of feedforward loops in gene regulation. In addition, the authors discuss the combinatorial regulatory effect between transcription factors and microRNAs, and select miR-155 for detailed analysis of microRNA's role in cancer. The proposed GALASSO approach is an efficient method to construct the combinatorial regulatory network. It also provides a new way to integrate different data sources and could find more applications in meta-analysis problem.

A modulated empirical Bayes model for identifying topological and temporal estrogen receptor α regulatory networks in breast cancer

BackgroundEstrogens 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. Dynamic gene expression changes have been shown to characterize the breast cancer cell response to estrogens, the every molecular mechanism of which is still not well understood.ResultsWe developed a modulated empirical Bayes model, and constructed a novel topological and temporal transcription factor (TF) regulatory network in MCF7 breast cancer cell line upon stimulation by 17β-estradiol stimulation. In the network, significant TF genomic hubs were identified including ER-alpha and AP-1; significant non-genomic hubs include ZFP161, TFDP1, NRF1, TFAP2A, EGR1, E2F1, and PITX2. Although the early and late networks were distinct (<5% overlap of ERα target genes between the 4 and 24 h time points), all nine hubs were significantly represented in both networks. In MCF7 cells with acquired resistance to tamoxifen, the ERα regulatory network was unresponsive to 17β-estradiol stimulation. The significant loss of hormone responsiveness was associated with marked epigenomic changes, including hyper- or hypo-methylation of promoter CpG islands and repressive histone methylations.ConclusionsWe identified a number of estrogen regulated target genes and established estrogen-regulated network that distinguishes the genomic and non-genomic actions of estrogen receptor. Many gene targets of this network were not active anymore in anti-estrogen resistant cell lines, possibly because their DNA methylation and histone acetylation patterns have changed.