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

Summary of 3-node and 4-node feed-forward loops based on glioblastoma related data.

a<p>FFL: feed-forward loop.</p>b<p>Definitions of the nodes and links were provided in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002488#pcbi-1002488-t001" target="_blank">Table 1</a>.</p

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

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

Notch-specific miRNA-TF regulatory network and its subnetworks related to GBM.

<p>A) Notch-specific miRNA-TF regulatory network related to GBM. B) GBM gene-centered subnetwork. The subnetwork includes most of the GBM-related genes involved in the Notch-specific miRNA-TF regulatory network. C) Centered subnetwork. The subnetwork links the GBM gene-centered subnetwork and the GBM regulator-centered subnetwork. D) GBM regulator-centered subnetwork. Except for two nodes, 33 nodes are GBM-related miRNAs and human TFs. Definition of colors and shapes for nodes and edges is the same as in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002488#pcbi-1002488-g003" target="_blank">Figure 3</a>.</p

Graphical representations of the composite glioblastoma-related miRNA-TF regulatory network and its network characteristics.

<p>A) Graphical representation of the composite glioblastoma miRNA-TF regulatory network. The network was generated from 3-node and 4-node composite-FFL motifs. B) Degree distribution of all nodes (genes, miRNAs and TFs) in the network. The Y-axis represents the proportion of nodes with a specific degree. C–E) Three higher-order subnetworks. In each subfigure, nodes in red correspond to GBM-related miRNAs, nodes in green correspond to GBM-related genes, and nodes in blue correspond to transcription factors. The edge colors represent different relationships: red for the repression of miRNAs to genes or TFs, blue for the regulation of TFs to genes or miRNAs, and black for the coexpression of GBM-related genes.</p

A catalogue of mixed feed-forward regulatory loops (FFLs).

<p>According to the relationship between the transcription factor (TF) and microRNA (miRNA), the mixed FFLs were classified as the TF-FFL model (the TF directly regulates the miRNA), miRNA-FFL model (the miRNA only directly regulates the TF) or composite-FFL model (the TF and the miRNA regulate each other). The relationships represented by solid lines are required while the relationships represented by dot lines are not required. B) Five types of putative regulations involved in these FFLs: miRNA-gene represents that the miRNA represses gene expression; miRNA-TF represents that the miRNA represses the TF gene expression; TF-gene represents the regulation by TF of the expression of the gene; TF-miRNA represents the regulation of TF to expression of miRNAs; and, gene-gene represents gene coexpression.</p

Enriched biological pathways associated with prognostic lncRNAs by guilt-by-association analysis.

<p>Enriched biological pathways associated with prognostic lncRNAs by guilt-by-association analysis.</p

Many lncRNAs are associated with common mutations in GBMs and LGGs.

<p>(A) Stacked bar graph of mutation-associated lncRNAs in GBMs. (B) Minimal overlap between mutation-associated lncRNAs in GBMs (red = upregulated, blue = downregulated, grey = no change). (C) Stacked bar graph of mutation-associated lncRNAs in LGGs shows robust deregulation depending on tumor mutational background. (D) Moderate overlap between mutation-associated lncRNA expression trends in GBMs; however, each group of mutation-associated lncRNAs represents a distinct set of GBMs (red = upregulated, blue = downregulated, grey = no change). GBM, glioblastoma multiforme; LGG, lower grade glioma; lncRNA, long noncoding RNA.</p