37 research outputs found
Network view of tumor suppressor genes (TSGs) and oncogenes (OCGs) in ovarian cancer.
<p>(A) Integrated hierarchical network of ovarian cancer (OVC) related tumor suppressor genes (TSGs), oncogenes (OCGs), and transcription factors (TFs). The nodes in red (circle) represent OVC-related TSGs, nodes in yellow (triangle) represent OVC-related OCGs, nodes in green (octagon) represent OVC-related TFs, and nodes in blue (vee) represent target genes. The links in orange represent the regulations from the TSGs or OCGs to their modulating TFs. The green arrow lines represent the regulations from the TFs to their target genes. (B) Plot of in-degree and out-degree of the 15 TFs in the three-layer regulatory network. In-degree is defined as the number of nodes that immediately link to and regulate the node of interest, and out-degree is defined as the number of nodes that immediately link to and are regulated by the node of interest. (C) A subnetwork with three feedback loops centered by ETS1. The color and shape schema of nodes and links are the same as those in (A).</p
Distinct and Competitive Regulatory Patterns of Tumor Suppressor Genes and Oncogenes in Ovarian Cancer
<div><h3>Background</h3><p>So far, investigators have found numerous tumor suppressor genes (TSGs) and oncogenes (OCGs) that control cell proliferation and apoptosis during cancer development. Furthermore, TSGs and OCGs may act as modulators of transcription factors (TFs) to influence gene regulation. A comprehensive investigation of TSGs, OCGs, TFs, and their joint target genes at the network level may provide a deeper understanding of the post-translational modulation of TSGs and OCGs to TF gene regulation.</p> <h3>Methodology/Principal Findings</h3><p>In this study, we developed a novel computational framework for identifying target genes of TSGs and OCGs using TFs as bridges through the integration of protein-protein interactions and gene expression data. We applied this pipeline to ovarian cancer and constructed a three-layer regulatory network. In the network, the top layer was comprised of modulators (TSGs and OCGs), the middle layer included TFs, and the bottom layer contained target genes. Based on regulatory relationships in the network, we compiled TSG and OCG profiles and performed clustering analyses. Interestingly, we found TSGs and OCGs formed two distinct branches. The genes in the TSG branch were significantly enriched in DNA damage and repair, regulating macromolecule metabolism, cell cycle and apoptosis, while the genes in the OCG branch were significantly enriched in the ErbB signaling pathway. Remarkably, their specific targets showed a reversed functional enrichment in terms of apoptosis and the ErbB signaling pathway: the target genes regulated by OCGs only were enriched in anti-apoptosis and the target genes regulated by TSGs only were enriched in the ErbB signaling pathway.</p> <h3>Conclusions/Significance</h3><p>This study provides the first comprehensive investigation of the interplay of TSGs and OCGs in a regulatory network modulated by TFs. Our application in ovarian cancer revealed distinct regulatory patterns of TSGs and OCGs, suggesting a competitive regulatory mechanism acting upon apoptosis and the ErbB signaling pathway through their specific target genes.</p> </div
Schematic view of tumor suppressor genes (TSGs) and oncogenes (OCGs) regulatory network analysis.
<p>This figure shows the TSG and OCG regulatory network construction and identification of critical downstream pathways modulated by TSGs and OCGs. Our pipeline involves four main steps. 1) Collecting ovarian cancer (OVC)-related genes, tumor suppressors (TSGs), oncogenes (OCGs), and transcription factors (TFs) from public databases and literature. 2) Extracting subnetworks centered on OVC TSGs, OCGs, and TFs from protein-protein interaction (PPI) data. 3) Integrating genome-scale expression data to construct a hierarchical regulatory network with OVC-related TSGs, OCGs, TFs and target genes. 4) Analyzing downstream pathways and subnetworks with regulated genes to investigate the interplay of TSGs and OCGs in specific biological processes. Modulator Inference by Network Dynamics (MINDy) is a software tool used for the identification of post-translational modulators of TFs based on expression profiles. Protein Interaction Network Analysis (PINA) is a platform for protein interaction network construction.</p
Downstream target gene profiles clustering with tumor suppressor genes (TSGs) and oncogenes (OCGs).
<p>The heat map shows a two-color representation of the regulatory relationship between modulators (TSGs and OCGs) and downstream target genes. A red colored cell in the grid indicates that the row TSG or OCG is inferred to regulate the column target gene. A blue colored cell in the grid indicates that the row TSG or OCG has no influence on the column target gene. The modulatorsβ dendrogram represents a hierarchical clustering of TSGs and OCGs based on their target gene profiles. The modulatorsβ dendrogram is divided into two branches with six clusters marked with different colors. The most significant enriched functional annotations are marked along the right of each cluster. Take the first maroon cluster in the TSG branch as an example: the enriched genes are involved in DNA damage and repair. The TSG-specific target genes are marked in red and the OCG-specific target genes are marked with yellow in the top panel. In addition, the TSG-specific target genes are also represented in red and the OCG-specific target genes are represented as a whole with yellow in the right panel. The arrow from TSG-specific target genes represents their regulatory effects on the ErbB signaling pathway, and the arrow from OCG-specific target genes represents their anti-apoptosis effects as apoptosis negative regulators.</p
Uncovering MicroRNA and Transcription Factor Mediated Regulatory Networks in Glioblastoma
<div><p>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.</p> </div
Additional file 3: Figure S3. of Disrupted cooperation between transcription factors across diverse cancer types
Transcriptional abundances of TFs and non-TFs across cancers. TFs show comparable expression abundance with non-TFs in both normal and tumor. TFs are coloredΓΒ in red and non-TFs are colored inΓΒ blue respectively. The expression value is log2 transformed. (TIF 193 kb
Computational framework for constructing the comprehensive GBM-specific miRNA-TF regulatory network and its application for identifying critical miRNA components in a given pathway.
<p>This framework involves four main steps. 1) Data collection. We compiled glioblastoma (GBM)-related genes, GBM-related microRNAs (miRNAs) and known human transcription factors (TFs) from public databases and literature. 2) Regulation prediction. We predicted five types of regulation (TF-gene, TF-miRNA, miRNA-gene, miRNA-TF, and gene-gene coexpression) by integrating TF binding profiles, miRNA target profiles, and gene expression profiles. 3) Identification of significant feed-forward loops (FFLs). Based on the regulation data in step 2, we assembled two types of feed-forward loops (FFLs): 3-node FFLs and 4-node FFLs. 4) Construction of a GBM-specific miRNA-TF regulatory network and performing further subnetwork analyses. By merging the FFLs identified in step 3, we constructed a GBM-specific miRNA-TF regulatory network, which consists of three types of nodes and five types of edges. Furthermore, we extracted subnetworks for core pathways reported for GBM from the GBM-specific regulatory network and predicted the miRNA components involved in these pathways.</p
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Canonical pathways overrepresented in genes involved in the composite glioblastoma-specific regulatory network.
a<p>Adjusted <i>P</i>-value was calculated by Fisher's exact test following by Benjamini-Hochberg multiple testing correction.</p
Summary of relationships among GBM-related genes, GBM-related miRNAs, and TFs.
a<p>miRNA: microRNA.</p>b<p>TF: transcription factor.</p>c<p>miRNA-gene: miRNA repression of gene expression.</p>d<p>miRNA-TF: miRNA repression of TF expression.</p>e<p>TF-gene: TF regulation of gene expression.</p>f<p>TF-miRNA: TF regulation of to miRNA expression.</p>g<p>Gene-gene: gene-gene coexpression.</p