Dataset Information

How microRNA and transcription factor co-regulatory networks affect osteosarcoma cell proliferation.

ABSTRACT: Osteosarcomas (OS) are complex bone tumors with various genomic alterations. These alterations affect the expression and function of several genes due to drastic changes in the underlying gene regulatory network. However, we know little about critical gene regulators and their functional consequences on the pathogenesis of OS. Therefore, we aimed to determine microRNA and transcription factor (TF) co-regulatory networks in OS cell proliferation. Cell proliferation is an essential part in the pathogenesis of OS and deeper understanding of its regulation might help to identify potential therapeutic targets. Based on expression data of OS cell lines divided according to their proliferative activity, we obtained 12 proliferation-related microRNAs and corresponding target genes. Therewith, microRNA and TF co-regulatory networks were generated and analyzed regarding their structure and functional influence. We identified key co-regulators comprising the microRNAs miR-9-5p, miR-138, and miR-214 and the TFs SP1 and MYC in the derived networks. These regulators are implicated in NFKB- and RB1-signaling and focal adhesion processes based on their common or interacting target genes (e.g., CDK6, CTNNB1, E2F4, HES1, ITGA6, NFKB1, NOTCH1, and SIN3A). Thus, we proposed a model of OS cell proliferation which is primarily co-regulated through the interactions of the mentioned microRNA and TF combinations. This study illustrates the benefit of systems biological approaches in the analysis of complex diseases. We integrated experimental data with publicly available information to unravel the coordinated (post)-transcriptional control of microRNAs and TFs to identify potential therapeutic targets in OS. The resulting microRNA and TF co-regulatory networks are publicly available for further exploration to generate or evaluate own hypotheses of the pathogenesis of OS (http://www.complex-systems.uni-muenster.de/co_networks.html).

SUBMITTER: Poos K

PROVIDER: S-EPMC3757060 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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How microRNA and transcription factor co-regulatory networks affect osteosarcoma cell proliferation.

Poos Kathrin K Smida Jan J Nathrath Michaela M Maugg Doris D Baumhoer Daniel D Korsching Eberhard E

PLoS computational biology 20130829 8

Osteosarcomas (OS) are complex bone tumors with various genomic alterations. These alterations affect the expression and function of several genes due to drastic changes in the underlying gene regulatory network. However, we know little about critical gene regulators and their functional consequences on the pathogenesis of OS. Therefore, we aimed to determine microRNA and transcription factor (TF) co-regulatory networks in OS cell proliferation. Cell proliferation is an essential part in the pat ...[more]

PMID: 24009496

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Project description:OBJECTIVE:Colorectal cancer (CRC) is an extremely common gastrointestinal malignancy. The present study aimed to identify microRNAs (miRNAs) and transcription factors (TFs) associated with tumor development. METHODS:Three miRNA profile datasets were integrated and analyzed to elucidate the potential key candidate miRNAs in CRC. The starBase database was used to identify the potential targets of common differentially expressed miRNAs (DEMs). Transcriptional Regulatory Element Database and Transcriptional Regulatory Relationships Unraveled by Sentence-based Text databases were used to identify cancer-related TFs and the TF-regulated target genes. Functional and pathway enrichment analyses were performed using the Database for Annotation, Visualization and Integration Discovery (DAVID) database, and the miRNA-TF-gene networks were constructed by Cytoscape. Quantitative reverse transcription polymerase chain reaction (qRT-PCR) was used to detect the expression of genes and miRNAs. RESULTS:In total, 14 DEMs were found in CRC. By bioinformatics analysis, 5 DEMs (miR-145, miR-497, miR-30a, miR-31, and miR-20a) and 8 TFs (ELK4 (ETS-family transcription factor), myeloblastosis proto-oncogene like (MYBL)1, MYBL2, CEBPA, PPARA, PPARD, PPARG, and endothelial PAS domain protein (EPAS1)) appeared to be associated with CRC and were therefore used to construct miRNA-TF-gene networks. From the networks, we found that miR-20a might play the most important role as an miRNA in the networks. By qRT-PCR, we demonstrated that miR-20a was significantly upregulated in CRC tissues. We also performed qRT-PCR to identify the expression of miR-20a-related TFs (PPARA, PPARD, PPARG, EPAS1). Three of them, PPARA, PPARG, and EPAS1, were downregulated in CRC tissues, with statistically significant differences, while the downregulation of PPARD in CRC tissues was not significantly different. Pathway enrichment analyses indicated that the phosphoinositide 3-kinase (PI3K)-Akt signaling pathway was the most significantly enriched pathway. Two main elements of the PI3K-Akt signaling pathway, phosphatase and tensin homolog deleted on chromosome 10 and B-cell lymphoma 2-associated agonist of cell death, were demonstrated to be downregulated in CRC. CONCLUSION:The present study identified hub miRNAs and miRNA-related TF regulatory networks in CRC, which might be potential targets for the diagnosis and treatment of CRC.

Project description:BACKGROUND: Transcription factors (TFs) and microRNAs (miRNAs) are primary metazoan gene regulators. Regulatory mechanisms of the two main regulators are of great interest to biologists and may provide insights into the causes of diseases. However, the interplay between miRNAs and TFs in a regulatory network still remains unearthed. Currently, it is very difficult to study the regulatory mechanisms that involve both miRNAs and TFs in a biological lab. Even at data level, a network involving miRNAs, TFs and genes will be too complicated to achieve. Previous research has been mostly directed at inferring either miRNA or TF regulatory networks from data. However, networks involving a single type of regulator may not fully reveal the complex gene regulatory mechanisms, for instance, the way in which a TF indirectly regulates a gene via a miRNA. RESULTS: We propose a framework to learn from heterogeneous data the three-component regulatory networks, with the presence of miRNAs, TFs, and mRNAs. This method firstly utilises Bayesian network structure learning to construct a regulatory network from multiple sources of data: gene expression profiles of miRNAs, TFs and mRNAs, target information based on sequence data, and sample categories. Then, in order to produce more meaningful results for further biological experimentation and research, the method searches the learnt network to identify the interplay between miRNAs and TFs and applies a network motif finding algorithm to further infer the network.We apply the proposed framework to the data sets of epithelial-to-mesenchymal transition (EMT). The results elucidate the complex gene regulatory mechanism for EMT which involves both TFs and miRNAs. Several discovered interactions and molecular functions have been confirmed by literature. In addition, many other discovered interactions and bio-markers are of high statistical significance and thus can be good candidates for validation by experiments. Moreover, the results generated by our method are compact, involving a small number of interactions which have been proved highly relevant to EMT. CONCLUSIONS: We have designed a framework to infer gene regulatory networks involving both TFs and miRNAs from multiple sources of data, including gene expression data, target information, and sample categories. Results on the EMT data sets have shown that the proposed approach is able to produce compact and meaningful gene regulatory networks that are highly relevant to the biological conditions of the data sets. This framework has the potential for application to other heterogeneous datasets to reveal the complex gene regulatory relationships.

Project description:Glioblastoma multiforme (GBM) is the most common and lethal brain tumor in humans. Recent studies revealed that patterns of microRNA (miRNA) expression in GBM tissue samples are different from those in normal brain tissues, suggesting that a number of miRNAs play critical roles in the pathogenesis of GBM. However, little is yet known about which miRNAs play central roles in the pathology of GBM and their regulatory mechanisms of action. To address this issue, in this study, we systematically explored the main regulation format (feed-forward loops, FFLs) consisting of miRNAs, transcription factors (TFs) and their impacting GBM-related genes, and developed a computational approach to construct a miRNA-TF regulatory network. First, we compiled GBM-related miRNAs, GBM-related genes, and known human TFs. We then identified 1,128 3-node FFLs and 805 4-node FFLs with statistical significance. By merging these FFLs together, we constructed a comprehensive GBM-specific miRNA-TF mediated regulatory network. Then, from the network, we extracted a composite GBM-specific regulatory network. To illustrate the GBM-specific regulatory network is promising for identification of critical miRNA components, we specifically examined a Notch signaling pathway subnetwork. Our follow up topological and functional analyses of the subnetwork revealed that six miRNAs (miR-124, miR-137, miR-219-5p, miR-34a, miR-9, and miR-92b) might play important roles in GBM, including some results that are supported by previous studies. In this study, we have developed a computational framework to construct a miRNA-TF regulatory network and generated the first miRNA-TF regulatory network for GBM, providing a valuable resource for further understanding the complex regulatory mechanisms in GBM. The observation of critical miRNAs in the Notch signaling pathway, with partial verification from previous studies, demonstrates that our network-based approach is promising for the identification of new and important miRNAs in GBM and, potentially, other cancers.

Dataset Information

How microRNA and transcription factor co-regulatory networks affect osteosarcoma cell proliferation.

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How microRNA and transcription factor co-regulatory networks affect osteosarcoma cell proliferation.

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