- Research article
- Open Access
Reconstruction of temporal activity of microRNAs from gene expression data in breast cancer cell line
© Jayavelu and Bar. 2016
- Received: 2 June 2015
- Accepted: 30 November 2015
- Published: 18 December 2015
MicroRNAs (miRNAs) are small non-coding RNAs that regulate genes at the post-transcriptional level in spatiotemporal manner. Several miRNAs are identified as prognostic and diagnostic markers in many human cancers. Estimation of the temporal activities of the miRNAs is an important step in the way to understand the complex interactions of these important regulatory elements with transcription factors (TFs) and target genes (TGs). However, current research on miRNA activities excludes network dynamics from the studies, disregarding the important element of time in the regulatory network analysis.
In the current study, we combined experimentally verified miRNA-TG interactions with breast cancer microarray TG expression data to identify key miRNAs and compute their temporal activity using network component analysis (NCA). The computed activities showed that miRNAs were regulated in a time dependent manner. Our results allowed constructing a synergistic network of miRNAs using the computed miRNA activities and their shared regulation of TGs. We further extended this network by incorporating miRNA-TG, miRNA-TF, TF-miRNA and TF-TG regulations in the context of breast cancer. Our integrated network identified several miRNAs known to be involved in breast cancer regulation and revealed several novel miRNAs. Our further analysis detected substantial involvement of the miRNAs miR-324, miR-93, miR-615 and miR-1 in breast cancer, which was not known previously. Next, combining our integrated networks with functional annotation of differentially expressed genes resulted in new sub-networks. These sub-networks allowed us to identify the key miRNAs and their interactions with TFs and TGs of several biological processes involved in breast cancer. The identified markers are validated for their potential as prognostic markers for breast cancer through survival analysis.
Our dynamical analysis of the miRNA interactions greatly helps to discover new network based markers, and is highly applicable (but not limited) to cancer research.
- Network component analysis
- Breast cancer
- Data decomposition
- Cancer markers
- EGFR signaling
- Survival analysis
- Kaplan-Meier plots
Cell functions are exerted through gene regulation in response to external cues. MicroRNAs (miRNAs) and transcription factors (TFs) are key regulators in the gene regulation process [1, 2]. miRNAs are small (~22 nucleotides in length) non-coding RNAs that regulate gene expression post transcriptionally in a sequence-specific manner . Many miRNAs are shown to be involved in cancer related biological processes, such as cell division, growth, development, apoptosis, proliferation and differentiation [4–7]. Therefore, constructing the miRNAs mediated gene regulation networks by utilizing gene expression data has become a regular practice in today’s miRNA research. However, all these studies of miRNA regulatory networks focused on static reconstruction of the miRNA regulatory activities. By doing so, they excluded the important element of time from the network analysis. However, since we know regulatory networks are dynamic (time dependent) by nature, important network information in those studies may have been lost.
Several studies applied statistical methods to investigate the role of miRNAs in gene regulatory networks. Madden et al identified key miRNAs associated with diseases through time–independent multivariate statistical analysis . _ENREF_47Liang et al developed a web based tool to compute the microRNA activity from its TG expression data based on the negative regulatory relationship between miRNAs and TGs . Mezlini et al developed a regression model to identify key miRNAs and their activity from TG expression and miRNA-TG network . _ENREF_47_ENREF_44 The approach proposed by Cheng et al  computed a series of static miRNA activities using the differential expression values of the TGs at each time point. Although their approach appears to construct a time-series miRNA activity profiles, it considers each time point regardless of the expression levels in the other time points. Recently, Schulz et al extended the DREM (Dynamic Regulatory Events Miner) model to mirDREM to reconstruct the dynamic miRNA regulated interaction networks . This model presents the list of significantly pivotal miRNAs and TFs at each time point. However, none of these methods computed the changes in miRNA activity with time.
Network component analysis (NCA) [13, 14] is a data decomposition approach that has been successfully employed in several species and in numerous research studies to compute the temporal activity profiles of TFs and construction of dynamic networks [14–28]. The method integrates temporal TG expression data and known network topology. _ENREF_24 In the current study, we exploited this approach for computing the temporal activities of the key miRNAs using only TG expression data (no miRNA data) and experimentally verified miRNA-TG relations. Using the NCA, we identified the key miRNAs, TFs and their activities in epidermal growth factor receptor (EGFR) signaling in breast cancer cells._ENREF_22_ENREF_23 We used the computed miRNAs temporal activities to identify co-regulating miRNAs (synergistic network) that show similar activity patterns and co-regulating common TGs, and validated these miRNAs with a literature study. Additionally, we built an integrated network of miRNAs, TFs and their TGs by retrieving miRNA-miRNA, miRNA-TG, TF-TG and TF-miRNA interactions from literature and combining these with the results of the NCA. With this approach, we identified several miRNAs that were known to be involved in regulation in breast cancer cells, and we revealed several novel miRNAs that are most likely to be involved in breast cancer, but were not known previously. These miRNAs can potentially serve as breast cancer markers.
Dynamics of miRNA activity
The activity profiles (normalized) of several miRNAs which are already known to be involved in breast cancer cells are presented in Fig. 1b. Although the miRNAs showed activity at all-time points, peak activity is demonstrated at 1 or 2 time points. The miRNA let-7a-5p displayed increasing repressing activity with time. This miRNA is known to be a tumor suppressor regulating many genes that inhibit cell migration in breast cancer . The miRNA miR-18b-5p is also involved in breast cancer, regulating genes involved in cell migration and metastasis . This miRNA showed peak activation at 8 h in the current study. The miRNA let-7d-5p showed a peak repressing activity around 2 h after EGF treatment and it is aberrantly expressed in breast cancer cells in previous study . The miRNA miR-20a-5p displayed a peak repressing activity at 10 min and this miRNA also involved in previous breast cancer studies . Yu et al showed that miR-20a-5p and miR-17-5p suppressed the breast cancer cell proliferation by negatively regulating the gene cyclin D1 . The miRNA miR-30a-5p is identified to be a novel prognostic marker in breast cancer in several past studies [33–35] and it showed a peak repression very late (around 36 h) in the current study. The miRNA miR-200c-3p also displayed a very late activation at 36 h and it is involved in regulating epithelial to mesenchymal transition (EMT) by targeting the genes ZEB1 and SIP1 in breast cancer in response to transforming growth factor (TGF) . The miRNA miR-155-5p demonstrated periodic peak activations at 10 min, 2 and 24 h. This miRNA is also known to be involved in the previous breast cancer studies with roles in cell survival, growth and chemosensitivity [37, 38]. The miRNA miR-210-3p exhibited peak repression activities very early at 15 min and late during 8–36 h time period and this miRNA has been identified as prognostic marker in breast cancer . Next, we used hierarchical clustering to explore groups of miRNAs with similar activity profiles (Fig. 1c). We found two distinctive groups of miRNAs that activates or repress at all time, and two smaller groups of miRNAs that alternate between activation and repression.
miRNA-miRNA synergistic network
Analysis of the integrated network in breast cancer
The top 20 miRNAs with highest degree in breast cancer integrated regulatory network. The degree of a node is the sum of in-coming and out-going connections with other nodes in the network. PMIDs denote the ‘pubmed’ identification numbers
Breast cancer related
The top 20 TFs or TGs with highest degree in breast cancer integrated regulatory network. The degree of a node is the sum of in-coming and out-going connections with other nodes in the network. PMIDs denote the ‘pubmed’ identification numbers
Breast cancer related
Breast cancer database (www.breastcancerdatabase.org)
Breast cancer database (www.breastcancerdatabase.org)
Functional annotation and pathway analyses of differentially expressed TGs
Statistically significant biological pathways affected by differentially expressed TGs in breast cancer cells identified from the KEGG database using DAVID
Pathways in cancer
TGF-beta signaling pathway
MAPK signaling pathway
Wnt signaling pathway
Notch signaling pathway
B cell receptor signaling pathway
Insulin signaling pathway
T cell receptor signaling pathway
ErbB signaling pathway
Statistically significant biological processes affected by differentially expressed TGs in breast cancer cells identified using DAVID
positive regulation of gene expression
positive regulation of transcription
positive regulation of nitrogen compound metabolic process
negative regulation of transcription
negative regulation of gene expression
negative regulation of cell differentiation
positive regulation of cell differentiation
regulation of apoptosis
negative regulation of cell proliferation
positive regulation of cell proliferation
regulation of transcription factor activity
positive regulation of cell cycle
regulation of signal transduction
posttranscriptional regulation of gene expression
regulation of cell migration
Understanding regulation and precise control of gene expression in higher organisms is a complex process, and miRNAs and TFs are two key regulators of this process. In the current study, we used the well-studied NCA approach to compute the temporal activities of the TFs, and for the first time, for the miRNAs as well. Although several previous studies demonstrated the construction of miRNA mediated gene networks, their approaches required the expression data of both miRNAs and TGs. In contrast, our reconstruction approach needs only expression data of TGs. With the publicly available large volumes of the microarray and RNA-sequencing (RNA-seq) TG expression data and experimentally verified miRNA-TG data, the NCA approach may serve as a powerful tool to study and understand the miRNA mediated gene regulation. With the computed temporal activities and gene expression data, we are able to identify the time specific active miRNAs, TFs and TGs. This analysis resulted in the identification of EGF stimulation’s dominant response at selective time points. Another interesting observation from this analysis is that the number of activated TGs are strongly correlated with their active regulators, TFs (Pearson correlation = 0.815) and miRNAs (Pearson correlation = 0.867) over entire time period.
We constructed the miRNA-miRNA synergistic network based on similar temporal activity of miRNAs and their shared TGs. There are several past studies constructed miRNA-miRNA networks but they mostly are based on combinations of shared TGs of miRNA pair, enriched in same gene ontology term, sequence, secondary structure and shared pathways [48–51]. However, none of these studies were used the temporal information knowing that miRNA-TG regulation is highly dynamic. Therefore, the synergistic network constructed in this study is one of the first attempts to incorporate temporal information. This network not only captured synergistic interactions between miRNAs but also identified novel miRNA regulators in breast cancer.
To understand the miRNA regulation more comprehensively, we further extended this synergistic network with TGs and TFs. Further examination of network identified hub miRNAs (hsa-miR-335-5p, hsa-miR-124-3p, hsa-miR-26b-5p, and hsa-miR-16-5p) and TFs (SP1, TCF12, JUN, MYOD1). Most of these hubs are either well-known regulators or are reported to play key roles in breast cancer. For instance, the miRNA miR-335-5p, the top hub node in the network is already known to be a key regulator in suppressing breast cancer metastasis and migration through regulation of targets SOX4 and TNC . The miR-124-3p was shown to be a novel tumor suppressor and a co-regulating EGFR driven cell cycle protein, inhibiting proliferation in breast cancer . The miRNA miR-26b-5p was also shown to be a potential therapeutic target for breast cancer. This miRNA inhibits the cell proliferation by regulating the target PTGS2 . The synthetic growth hormone progestin down regulated the miR-16-5p and cyclin E was identified as one of its targets in breast cancer . Furthermore, this miRNA inhibited the growth of progestin treated breast cancer cells and thus its role as tumor suppressor. The miRNA let-7b-5p was also shown to have a tumor suppressor role in breast cancer patients with lymph node metastasis, by repressing the expressions of the genes PAK1, DIAPH2, RDX and ITGB8 . The miRNA miR-193b-3p was shown to be an important marker in clinical metastasis of human breast cancer cells, which potentially up-regulates the expression of uPA . In addition to those hub miRNAs, we found hub miRNAs with no previous association in breast cancer, including miR-615-3p, miR-1, miR-484, miR-192-5p and miR-324-5p. We suggest that the novel miRNAs found from our integrated network have potential therapeutic outcomes in breast cancer and should be further explored. Similarly, the top hub TFs we found in the integrated network such as SP1, SP2, TCF12, MYC, JUN and EGR2, were also well-known regulators in breast cancer. Yang et al showed that SP1 and HSF1 play an important role in the regulation of FUT4 (Fucosyltransferase IV), which is associated with breast cancer epithelial cell proliferation . Zhang et al identified that oncoprotein HBXIP activates the gene PDGFB through transcription factor SP1, to promote proliferation in breast cancer cells . Chen et al showed that JUN miR-21 activates Bcl-2 expression and thus promotes chemo resistance in triple negative breast cancer cells . The TF ETS1 promotes proliferation, migration and invasion through stimulation of estrogen receptor alpha (ERα). Verschoor et al showed the ETS1 involvement in energy metabolism and oxidative stress in breast and ovarian cancers .
We further analyzed the TGs and TFs from integrated network to find the common KEGG pathways and Gene Ontology (GO) biological process terms they regulated. Several previous studies showed that EGFR signaling is one of the potentially targeted pathways for identifying anticancer drugs and treatment strategies for various cancers [61, 62]. The involvement of Wnt signaling pathway in breast cancers have been described previously. Schlange et al had shown that autocrine Wnt signaling controls proliferation and tumor growth through activation of canonical Wnt pathway and EGFR transactivation . Loh et al had shown the important role of this pathway in inhibiting the effects of Tamoxifen in tumor growth . TGF-beta signaling pathway is also widely studied to identify therapeutic drug targets in many metastatic cancers including breast cancer as this pathway plays a key role in regulating tumor invasion and metastasis [65–67]. Another significantly enriched term was apoptosis. Dysregulation of apoptosis was shown to play key roles in breast cancer . This finding explains the role of EGF as a potential therapeutic target in breast cancers. The MAPK pathway is the central part of the signal transduction initiated by EGF that controls the cellular processes of proliferation and differentiation. This pathway was also highly enriched in the current study and has been widely targeted to find diagnostic and prognostic markers of breast cancer . Although this analysis identified the ErbB as a significantly enriched pathway (as the gene expression data set is obtained from ErbB signaling), to our surprise only 16 out of 1072 differentially expressed TGs were known to be associated with ErbB. This may indicate that our current study identified several new TGs associated with this signaling.
There are few limitations in the current approach used in this study. Firstly, we used only the experimentally verified miRNA-TG regulations from miRTarBase database. This database contains the regulation data retrieved from heterogeneous systems and it may not be accurate for a specific system. Secondly, the NCA approach has very strict criteria on network structure (miRNA-TG, TF-TG) and might have lost few key miRNAs, TFs and their TGs. Thirdly, in spite of the fact that potential prognostic markers for breast cancer in this study were predicted using computational approach only, the validations were based on Kaplan-Meier survival analysis conducted with heterogeneous data sources from clinical trials.
The gene expression data used in this study were obtained by measuring the response of MCF7 breast cancer cells treated with epidermal growth factor (EGF) at 17 time points over a time period of 72 h . The original gene expression data was downloaded from the GEO database with accession number GSE13009. We applied loess normalization within time points and quantile normalization across time points. The expression values were averaged over two replicate measurements. We computed statistical significance, P-values based on t-tests by comparing control versus treatment samples at each time point to identify differentially expressed genes (DEGs). The DEGs with a fold change > 1.5 and P-value < 0.05 at least at two time points were selected for further analysis. To reduce the noise and to smooth the data, we used Fourier transform functions to fit the time-series data . The initial networks were defined using experimentally verified miRNAs, TFs and its interactions with TGs from databases. All the computations were performed using bioinformatics toolbox in MATLAB.
Although several databases are available for predicting miRNA-TG interactions, we chose miRTarBase because it contains manually curated and experimentally verified regulations . We downloaded the regulation data as an adjacency list, which was used in NCA analysis to predict the temporal dynamic activity of miRNAs.
We collected the experimentally verified TF-TG regulations from TFacts , a database containing 6401 experimentally validated regulations between 2720 TGs and 330 TFs. This database includes integrated information from different resources, such as TRED, TRDD, PAZAR NFIregulomeDB and their own experimental predictions. In addition, we retrieved TF-TG interactions from the Chip-X experiments of Transcriptome Browser . This list includes 312 TFs, 13133 TGs and the 173156 interactions among them.
Network component analysis (NCA)
where the matrix [E] represents the expression values of genes at various time points, the matrix [C] is the control strength of each miRNA on a target gene (TG), and the matrix [T] represents the activities of all of the miRNAs. The dimensions of [E], [C] and [T] are N X M, N X L and L X M, respectively. Where, N is the number of TGs, M is the number of time points or measurement conditions, and L is the number of miRNAs or TFs.
The connectivity matrix [C] must have full-column rank
When a node in the regulatory layer is removed along with all of the output nodes connected to it, the resulting network must be characterized by a connectivity matrix that still has full-column rank
The [T] matrix must have full row rank
Using NCA as the reconstruction method, we predicted significant miRNAs, TFs and their temporal activity profiles. The NCA toolbox can be downloaded from here (http://www.seas.ucla.edu/~liaoj/downloads.html).
The miRNA-miRNA networks
We computed the pairwise Pearson correlation coefficient between reconstructed activity profiles of all the miRNAs and the number of common TGs between each pair of miRNAs. We assumed a synergistic interaction between a pair of miRNAs if the correlation is greater than 0.7 and common TGs are greater than 3. The constructed network has 112 miRNAs and 314 synergistic interactions between them. Schematic of the approach is presented in Fig. 3.
The random networks for the comparison purposes are generated in Cytoscape software using ‘Randomnetworks’ plugin. These networks are created keeping the number of nodes and connections same as the original network.
Pathway and biological processes
We used DAVID (Database for Annotation, Visualization and Integrated Discovery) with the default settings to find statistically enriched biological pathways. Information related to the pathways was identified from DAVID [75, 76]. DAVID is a comprehensive set of functional annotation tools for investigators to understand the biological meaning behind a large list of genes. DAVID uses the biological information retrieved from various resources and databases. For instance, information related to pathways is retrieved from KEGG (Kyoto Encyclopedia of Genes and Genomes), PANTHER, BioCarta and REACTOME pathway databases. Pathways and biological processes that had at least 10 DEG members and a P-value < 0.001 were considered significant. P-values are computed using modified Fisher’s exact test based on hyper geometric distribution.
The networks are created using the Cytoscape software tool . All statistical calculations, NCA and clustering were done in Matlab, Mathworks.
We conducted survival analysis of miRNAs and TFs using the tools ‘MIRUMIR’  and ‘PPISURV’  respectively, both developed by Antonov AV et al. These tools integrate publicly available clinical data such as the GEO repository. Briefly, these tools utilize the rank information of expression profiles of miRNAs and TFs. Patients are divided into low and high expression groups, based on the average expression of the selected miRNAs or TFs. Then, the two distinguished groups of patients along with their survival information are used to identify any significant statistical differences in survival outcome using the statistical packages in R program. The survival outcomes are represented through Kaplan-Meier plots using R. The information about the clinical data source for survival analysis for miRNAs and TFs are provided in Additional file 6B.
The analytical method we presented here was able to predict the involvement of several key miRNA regulators in processes related to breast cancer. It has also allowed us to explore the role of these regulators in the network and their interactions with TGs and TFs. We demonstrated that this dynamic miRNA-TF network analysis identifies regulation pathways, processes and connections that significantly involved in breast cancer. Furthermore, the identified markers are validated for their potential as prognostic markers for breast cancer though publicly available clinical data and survival analysis. We propose that this analysis can be applied to assist understanding miRNA regulation in other systems as well, suggesting individual miRNAs and entire pathways as target for cancer research.
Authors would like to thank Pavan K Sriram for critical reading of the manuscript. This work was supported by Research Council of Norway through grant number: 70174300.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
- Chen K, Rajewsky N. The evolution of gene regulation by transcription factors and microRNAs. Nat Rev Genet. 2007;8(2):93–103.PubMedView ArticleGoogle Scholar
- He L, Hannon GJ. MicroRNAs: small RNAs with a big role in gene regulation. Nat Rev Genet. 2004;5(7):522–31.PubMedView ArticleGoogle Scholar
- Pasquinelli AE, Ruvkun G. Control of developmental timing by microRNAs and their targets. Annu Rev Cell Dev Biol. 2002;18(1):495–513.PubMedView ArticleGoogle Scholar
- Cheng AM, Byrom MW, Shelton J, Ford LP. Antisense inhibition of human miRNAs and indications for an involvement of miRNA in cell growth and apoptosis. Nucleic Acids Res. 2005;33(4):1290–7.PubMedPubMed CentralView ArticleGoogle Scholar
- Chen C-Z, Li L, Lodish HF, Bartel DP. MicroRNAs Modulate Hematopoietic Lineage Differentiation. Science. 2004;303(5654):83–6.PubMedView ArticleGoogle Scholar
- Karp X, Ambros V. Encountering MicroRNAs in Cell Fate Signaling. Science. 2005;310(5752):1288–9.PubMedView ArticleGoogle Scholar
- Xu P, Guo M, Hay BA. MicroRNAs and the regulation of cell death. Trends Genet. 2004;20(12):617–24.PubMedView ArticleGoogle Scholar
- Madden S, Carpenter S, Jeffery I, Björkbacka H, Fitzgerald K, O'Neill L, et al. Detecting microRNA activity from gene expression data. BMC Bioinformatics. 2010;11(1):257.PubMedPubMed CentralView ArticleGoogle Scholar
- Liang Z, Zhou H, He Z, Zheng H, Wu J. mirAct: a web tool for evaluating microRNA activity based on gene expression data. Nucleic Acids Res. 2011;39(suppl 2):W139–W144.PubMedPubMed CentralView ArticleGoogle Scholar
- Mezlini AM, Wang B, Deshwar A, Morris Q, Goldenberg A. Identifying Cancer Specific Functionally Relevant miRNAs from Gene Expression and miRNA-to-Gene Networks Using Regularized Regression. PLoS One. 2013;8(10):e73168.PubMedPubMed CentralView ArticleGoogle Scholar
- Cheng C, Li LM. Inferring MicroRNA Activities by Combining Gene Expression with MicroRNA Target Prediction. PLoS One. 2008;3(4):e1989.PubMedPubMed CentralView ArticleGoogle Scholar
- Schulz MH, Pandit KV, Lino Cardenas CL, Ambalavanan N, Kaminski N, Bar-Joseph Z. Reconstructing dynamic microRNA-regulated interaction networks. Proc Natl Acad Sci. 2013;110(39):15686–91.PubMedPubMed CentralView ArticleGoogle Scholar
- Liao JC, Boscolo R, Yang Y-L, Tran LM, Sabatti C, Roychowdhury VP. Network component analysis: Reconstruction of regulatory signals in biological systems. Proc Natl Acad Sci. 2003;100(26):15522–7.PubMedPubMed CentralView ArticleGoogle Scholar
- Tran LM, Brynildsen MP, Kao KC, Suen JK, Liao JC. gNCA: a framework for determining transcription factor activity based on transcriptome: identifiability and numerical implementation. Metab Eng. 2005;7(2):128–41.PubMedView ArticleGoogle Scholar
- Shao L, Wang L, Wei Z, Xiong Y, Wang Y, Tang K, et al. Dynamic Network of Transcription and Pathway Crosstalk to Reveal Molecular Mechanism of MGd-Treated Human Lung Cancer Cells. PLoS One. 2012;7(5):e31984.PubMedPubMed CentralView ArticleGoogle Scholar
- Seok J, Xiao W, Moldawer LL, Davis RW, Covert MW. A dynamic network of transcription in LPS-treated human subjects. BMC Syst Biol. 2009;3(78):1–14.Google Scholar
- Wang J, Qiu X, Li Y, Deng Y, Shi T. A transcriptional dynamic network during Arabidopsis thaliana pollen development. BMC Syst Biol. 2011;5(Supp 3):S8.PubMedPubMed CentralView ArticleGoogle Scholar
- Fu Y, Jarboe LR, Dickerson JA. Reconstructing genome-wide regulatory network of E. coli using transcriptome data and predicted transcription factor activities. BMC Bioinformatics. 2011;12:233.PubMedPubMed CentralView ArticleGoogle Scholar
- Hyduke DR, Jarboe LR, Tran LM, Chou KJ, Liao JC. Integrated network analysis identifies nitric oxide response networks and dihydroxyacid dehydratase as a crucial target in Escherichia coli. Proc Natl Acad Sci U S A. 2007;104(20):8484–9.PubMedPubMed CentralView ArticleGoogle Scholar
- Yang YL, Suen J, Brynildsen MP, Galbraith SJ, Liao JC. Inferring yeast cell cycle regulators and interactions using transcription factor activities. BMC Genomics. 2005;6(90):1–15.Google Scholar
- Ye C, Galbraith SJ, Liao JC, Eskin E. Using network component analysis to dissect regulatory networks mediated by transcription factors in yeast. PLoS Comput Biol. 2009;5(3):e1000311.PubMedPubMed CentralView ArticleGoogle Scholar
- Kao KC, Yang YL, Boscolo R, Sabatti C, Roychowdhury V, Liao JC. Transcriptome-based determination of multiple transcription regulator activities in Escherichia coli by using network component analysis. Proc Natl Acad Sci U S A. 2004;101(2):641–6.PubMedPubMed CentralView ArticleGoogle Scholar
- Rahib L, Sriram G, Harada MK, Liao JC, Dipple KM. Transcriptomic and network component analysis of glycerol kinase in skeletal muscle using a mouse model of glycerol kinase deficiency. Mol Genet Metab. 2009;96(3):106–12.PubMedPubMed CentralView ArticleGoogle Scholar
- Tolle I, Huang X, Akpalu YA, Martin LL. A Modified Network Component Analysis (NCA) Methodology for the Decomposition of X-ray Scattering Signatures. Ind Eng Chem Res. 2009;48(13):6137–44.View ArticleGoogle Scholar
- Brynildsen MP, Liao JC. An integrated network approach identifies the isobutanol response network of Escherichia coli. Mol Syst Biol. 2009;5(277):277.PubMedPubMed CentralGoogle Scholar
- Buescher JM, Liebermeister W, Jules M, Uhr M, Muntel J, Botella E, et al. Global network reorganization during dynamic adaptations of Bacillus subtilis metabolism. Science. 2012;335(6072):1099–103.PubMedView ArticleGoogle Scholar
- Barah P, Jayavelu ND, Mundy J, Bones AM. Genome scale transcriptional response diversity among ten ecotypes of Arabidopsis thaliana during heat stress. Front Plant Sci. 2013;4:532.Google Scholar
- Doni Jayavelu N, Bar N. Dynamics of Regulatory Networks in Gastrin-Treated Adenocarcinoma Cells. PLoS One. 2014;9(1):e78349.PubMedPubMed CentralView ArticleGoogle Scholar
- Hu X, Guo J, Zheng L, Li C, Zheng TM, Tanyi JL, et al. The Heterochronic microRNA let-7 Inhibits Cell Motility by Regulating the Genes in the Actin Cytoskeleton Pathway in Breast Cancer. Mol Cancer Res. 2013;11(3):240–50.PubMedPubMed CentralView ArticleGoogle Scholar
- Fonseca-Sanchez MA, Perez-Plasencia C, Fernandez-Retana J, Arechaga-Ocampo E, Marchat LA, Rodriguez-Cuevas S, et al. microRNA-18b is upregulated in breast cancer and modulates genes involved in cell migration. Oncol Rep. 2013;30(5):2399–410.PubMedGoogle Scholar
- Iorio MV, Ferracin M, Liu C-G, Veronese A, Spizzo R, Sabbioni S, et al. MicroRNA Gene Expression Deregulation in Human Breast Cancer. Cancer Res. 2005;65(16):7065–70.PubMedView ArticleGoogle Scholar
- Yu Z, Wang C, Wang M, Li Z, Casimiro MC, Liu M, et al. A cyclin D1/microRNA 17/20 regulatory feedback loop in control of breast cancer cell proliferation. J Cell Biol. 2008;182(3):509–17.PubMedPubMed CentralView ArticleGoogle Scholar
- Zeng R-c, Zhang W, Yan X-q, Ye Z-q, Chen E-d, Huang D-p, et al. Down-regulation of miRNA-30a in human plasma is a novel marker for breast cancer. Med Oncol. 2013;30(1):1–8.Google Scholar
- Zhang N, Wang X, Huo Q, Sun M, Cai C, Liu Z, et al. MicroRNA-30a suppresses breast tumor growth and metastasis by targeting metadherin. Oncogene. 2013.Google Scholar
- Cheng C-W, Wang H-W, Chang C-W, Chu H-W, Chen C-Y, Yu J-C, et al. MicroRNA-30a inhibits cell migration and invasion by downregulating vimentin expression and is a potential prognostic marker in breast cancer. Breast Cancer Res Treat. 2012;134(3):1081–93.PubMedView ArticleGoogle Scholar
- Gregory PA, Bert AG, Paterson EL, Barry SC, Tsykin A, Farshid G, et al. The miR-200 family and miR-205 regulate epithelial to mesenchymal transition by targeting ZEB1 and SIP1. Nat Cell Biol. 2008;10(5):593–601.PubMedView ArticleGoogle Scholar
- Jiang S, Zhang H-W, Lu M-H, He X-H, Li Y, Gu H, et al. MicroRNA-155 Functions as an OncomiR in Breast Cancer by Targeting the Suppressor of Cytokine Signaling 1 Gene. Cancer Res. 2010;70(8):3119–27.PubMedView ArticleGoogle Scholar
- Kong W, He L, Coppola M, Guo J, Esposito NN, Coppola D, et al. MicroRNA-155 Regulates Cell Survival, Growth, and Chemosensitivity by Targeting FOXO3a in Breast Cancer. J Biol Chem. 2010;285(23):17869–79.PubMedPubMed CentralView ArticleGoogle Scholar
- Camps C, Buffa FM, Colella S, Moore J, Sotiriou C, Sheldon H, et al. hsa-miR-210 Is Induced by Hypoxia and Is an Independent Prognostic Factor in Breast Cancer. Clin Cancer Res. 2008;14(5):1340–8.PubMedView ArticleGoogle Scholar
- Jiang Q, Wang Y, Hao Y, Juan L, Teng M, Zhang X, et al. miR2Disease: a manually curated database for microRNA deregulation in human disease. Nucleic Acids Res. 2009;37 suppl 1:D98–D104.PubMedPubMed CentralView ArticleGoogle Scholar
- Xie B, Ding Q, Han H, Wu D. miRCancer: a microRNA–cancer association database constructed by text mining on literature. Bioinformatics. 2013;29(5):638–44.PubMedView ArticleGoogle Scholar
- Sengupta D, Bandyopadhyay S. Participation of microRNAs in human interactome: extraction of microRNA-microRNA regulations. Mol Biosyst. 2011;7(6):1966–73.PubMedView ArticleGoogle Scholar
- Albert R, Barabási A-L. Statistical mechanics of complex networks. Rev Mod Phys. 2002;74(1):47–97.View ArticleGoogle Scholar
- Jeong Y-J, Choi Y, Shin J-M, Cho H-J, Kang J-H, Park K-K, et al. Melittin suppresses EGF-induced cell motility and invasion by inhibiting PI3K/Akt/mTOR signaling pathway in breast cancer cells. Food and Chemical Toxicology 2014(0)Google Scholar
- Ginsburg E, Vonderhaar BK. Stimulation of growth of human breast cancer cells (T47D) by platelet derived growth factor. Cancer Lett. 1991;58(1–2):137–44.PubMedView ArticleGoogle Scholar
- Lev DC, Kim SJ, Onn A, Stone V, Nam D-H, Yazici S, et al. Inhibition of Platelet-Derived Growth Factor Receptor Signaling Restricts the Growth of Human Breast Cancer in the Bone of Nude Mice. Clin Cancer Res. 2005;11(1):306–14.PubMedGoogle Scholar
- Uluer ET, Aydemir I, Inan S, Ozbilgin K, Vatansever HS. Effects of 5-fluorouracil and gemcitabine on a breast cancer cell line (MCF-7) via the JAK/STAT pathway. Acta Histochem. 2012;114(7):641–6.PubMedView ArticleGoogle Scholar
- Xu J, Li C-X, Li Y-S, Lv J-Y, Ma Y, Shao T-T, et al. MiRNA–miRNA synergistic network: construction via co-regulating functional modules and disease miRNA topological features. Nucleic Acids Res. 2010.Google Scholar
- Xu J, Li Y, Li X, Li C, Shao T, Bai J, et al. Dissection of the potential characteristic of miRNA-miRNA functional synergistic regulations. Mol Biosyst. 2013;9(2):217–24.PubMedView ArticleGoogle Scholar
- Wu B, Li C, Zhang P, Yao Q, Wu J, Han J, et al. Dissection of miRNA-miRNA Interaction in Esophageal Squamous Cell Carcinoma. PLoS One. 2013;8(9):e73191.PubMedPubMed CentralView ArticleGoogle Scholar
- Na Y-J, Kim JH. Understanding cooperativity of microRNAs via microRNA association networks. BMC Genomics. 2013;14 Suppl 5:S17.PubMedPubMed CentralView ArticleGoogle Scholar
- Tavazoie SF, Alarcon C, Oskarsson T, Padua D, Wang Q, Bos PD, et al. Endogenous human microRNAs that suppress breast cancer metastasis. Nature. 2008;451(7175):147–52.PubMedPubMed CentralView ArticleGoogle Scholar
- Uhlmann S, Mannsperger H, Zhang JD, Horvat E-A, Schmidt C, Kublbeck M, et al. Global microRNA level regulation of EGFR-driven cell-cycle protein network in breast cancer. Mol Syst Biol. 2012;8.Google Scholar
- Li J, Kong X, Zhang J, Luo Q, Li X, Fang L. MiRNA-26b inhibits proliferation by targeting PTGS2 in breast cancer. Cancer Cell Int. 2013;13(1):7.PubMedPubMed CentralView ArticleGoogle Scholar
- Rivas M, Venturutti L, Huang Y-W, Schillaci R, Huang T, Elizalde P. Downregulation of the tumor-suppressor miR-16 via progestin-mediated oncogenic signaling contributes to breast cancer development. Breast Cancer Res. 2012;14(3):R77.PubMedPubMed CentralView ArticleGoogle Scholar
- Li XF, Yan PJ, Shao ZM. Downregulation of miR-193b contributes to enhance urokinase-type plasminogen activator (uPA) expression and tumor progression and invasion in human breast cancer. Oncogene. 2009;28(44):3937–48.PubMedView ArticleGoogle Scholar
- Yang X, Wang J, Liu S, Yan Q. HSF1 and Sp1 Regulate FUT4 Gene Expression and Cell Proliferation in Breast Cancer Cells. J Cell Biochem. 2014;115(1):168–78.PubMedView ArticleGoogle Scholar
- Zhang Y, Zhao Y, Li L, Shen Y, Cai X, Zhang X, et al. The oncoprotein HBXIP} upregulates PDGFB} via activating transcription factor Sp1 to promote the proliferation of breast cancer cells. Biochem Biophys Res Commun. 2013;434(2):305–10.PubMedView ArticleGoogle Scholar
- Chen L, Bourguignon LY. Hyaluronan-CD44 interaction promotes c-Jun signaling and miRNA21 expression leading to Bcl-2 expression and chemoresistance in breast cancer cells. Mol Cancer. 2014;13(1):52.PubMedPubMed CentralView ArticleGoogle Scholar
- Verschoor ML, Verschoor CP, Singh G. Ets-1 global gene expression profile reveals associations with metabolism and oxidative stress in ovarian and breast cancers. Cancer Metab. 2013;1(1):17.PubMedPubMed CentralView ArticleGoogle Scholar
- Normanno N, De Luca A, Bianco C, Strizzi L, Mancino M, Maiello MR, et al. Epidermal growth factor receptor (EGFR) signaling in cancer. Gene. 2006;366(1):2–16.PubMedView ArticleGoogle Scholar
- Uberall I, Kolar Z, Trojanec R, Berkovcova J, Hajduch M. The status and role of ErbB receptors in human cancer. Exp Mol Pathol. 2008;84(2):79–89.PubMedView ArticleGoogle Scholar
- Schlange T, Matsuda Y, Lienhard S, Huber A, Hynes N. Autocrine WNT signaling contributes to breast cancer cell proliferation via the canonical WNT pathway and EGFR transactivation. Breast Cancer Res. 2007;9(5):R63.PubMedPubMed CentralView ArticleGoogle Scholar
- Loh YN, Hedditch E, Baker L, Jary E, Ward R, Ford C. The Wnt signalling pathway is upregulated in an in vitro model of acquired tamoxifen resistant breast cancer. BMC Cancer. 2013;13(1):174.PubMedPubMed CentralView ArticleGoogle Scholar
- Drabsch Y, He S, Zhang L, Snaar-Jagalska B, ten Dijke P. Transforming growth factor-beta signalling controls human breast cancer metastasis in a zebrafish xenograft model. Breast Cancer Res. 2013;15(6):R106.PubMedPubMed CentralView ArticleGoogle Scholar
- Ganapathy V, Ge R, Grazioli A, Xie W, Banach-Petrosky W, Kang Y, et al. Targeting the Transforming Growth Factor-beta pathway inhibits human basal-like breast cancer metastasis. Mol Cancer. 2010;9(1):122.PubMedPubMed CentralView ArticleGoogle Scholar
- Zhang L, Zhou F, García de Vinuesa A, de Kruijf Esther M, Mesker Wilma E, Hui L, et al. TRAF4 Promotes TGF-β Receptor Signaling and Drives Breast Cancer Metastasis. Mol Cell. 2013;51(5):559–72.PubMedView ArticleGoogle Scholar
- Parton M, Dowsett M, Smith I. Studies of apoptosis in breast cancer. BMJ. 2001;322(7301):1528–32.PubMedPubMed CentralView ArticleGoogle Scholar
- Sparano JA, Moulder S, Kazi A, Vahdat L, Li T, Pellegrino C, et al. Targeted Inhibition of Farnesyltransferase in Locally Advanced Breast Cancer: A Phase I and II Trial of Tipifarnib Plus Dose-Dense Doxorubicin and Cyclophosphamide. J Clin Oncol. 2006;24(19):3013–8.PubMedView ArticleGoogle Scholar
- Saeki Y, Endo T, Ide K, Nagashima T, Yumoto N, Toyoda T, et al. Ligand-specific sequential regulation of transcription factors for differentiation of MCF-7 cells. BMC Genomics. 2009;10(545):1–16.Google Scholar
- Doni Jayavelu N, Bar N. A Noise Removal Algorithm for Time Series Microarray Data. In: Correia L, Reis L, Cascalho J, editors. Progress in Artificial Intelligence, vol. 8154. Berlin: Springer; 2013. p. 152–62.View ArticleGoogle Scholar
- Hsu S-D, Tseng Y-T, Shrestha S, Lin Y-L, Khaleel A, Chou C-H, et al. miRTarBase update 2014: an information resource for experimentally validated miRNA-target interactions. Nucleic Acids Res. 2014;42(D1):D78–85.PubMedPubMed CentralView ArticleGoogle Scholar
- Essaghir A, Toffalini F, Knoops L, Kallin A, Helden J, Demoulin JB. Transcription factor regulation can be accurately predicted from the presence of target gene signatures in micro array gene expression data. Nucleic Acids Res. 2010;38(11):e120.PubMedPubMed CentralView ArticleGoogle Scholar
- Lepoivre C, Bergon A, Lopez F, Perumal N, Nguyen C, Imbert J, et al. TranscriptomeBrowser 3.0: introducing a new compendium of molecular interactions and a new visualization tool for the study of gene regulatory networks. BMC Bioinformatics. 2012;13(1):19.PubMedPubMed CentralView ArticleGoogle Scholar
- Huang DW, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 2008;4:44–57.View ArticleGoogle Scholar
- Dennis G, Sherman B, Hosack D, Yang J, Gao W, Lane HC, et al. DAVID: Database for Annotation, Visualization, and Integrated Discovery. Genome Biol. 2003;4(5):3.View ArticleGoogle Scholar
- Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, et al. Cytoscape: A Software Environment for Integrated Models of Biomolecular Interaction Networks. Genome Res. 2003;13(11):2498–504.PubMedPubMed CentralView ArticleGoogle Scholar
- Antonov AV, Knight RA, Melino G, Barlev NA, Tsvetkov PO. MIRUMIR: an online tool to test microRNAs as biomarkers to predict survival in cancer using multiple clinical data sets. Cell Death Differ. 2013;20(2):367.PubMedPubMed CentralView ArticleGoogle Scholar
- Antonov AV, Krestyaninova M, Knight RA, Rodchenkov I, Melino G, Barlev NA. PPISURV: a novel bioinformatics tool for uncovering the hidden role of specific genes in cancer survival outcome. Oncogene. 2014;33(13):1621–8.PubMedView ArticleGoogle Scholar