Abstract
BackgroundBoth transcriptional control and microRNA (miRNA) control are critical regulatory mechanisms for cells to direct their destinies. At present, the combinatorial regulatory network composed of transcriptional regulations and post-transcriptional regulations is often constructed through a forward engineering strategy that is based solely on searching of transcriptional factor binding sites or miRNA seed regions in the putative target sequences. If the reverse engineering strategy is integrated with the forward engineering strategy, a more accurate and more specific combinatorial regulatory network will be obtained.ResultsIn this work, utilizing both sequence-matching information and parallel expression datasets of miRNAs and mRNAs, we integrated forward engineering with reverse engineering strategies and as a result built a hypothetical combinatorial gene regulatory network in human cancer. The credibility of the regulatory relationships in the network was validated by random permutation procedures and supported by authoritative experimental evidence-based databases. The global and local architecture properties of the combinatorial regulatory network were explored, and the most important tumor-regulating miRNAs and TFs were highlighted from a topological point of view.ConclusionsBy integrating the forward engineering and reverse engineering strategies, we manage to sketch a genome-scale combinatorial gene regulatory network in human cancer, which includes transcriptional regulations and miRNA regulations, allowing systematic study of cancer gene regulation. Our work establishes a pipeline that can be extended to reveal conditional combinatorial regulatory landscapes correlating to specific cellular contexts.
Highlights
Both transcriptional control and microRNA control are critical regulatory mechanisms for cells to direct their destinies
We demonstrated an efficient integration of the forward-predicted candidate regulatory relationships with the NCI60 panel of parallel miRNA and mRNA expression datasets, giving rise to a genomescale combinatorial network of transcriptional regulations and miRNA regulations in human cancer
A genome-scale combinatorial gene regulatory network in human cancer By integrating the forward-predicted regulatory relationships and the miRNA/mRNA expression data with the linear regression models (Equations 1, 2, and 3), we culled a subset of regulatory relationships that were hopefully more plausible than the beginning set of forwardpredicted regulatory relationships
Summary
Both transcriptional control and microRNA (miRNA) control are critical regulatory mechanisms for cells to direct their destinies. TFs activate or repress gene transcription by binding to specific sites (transcription factor binding sites, or TFBSs) in promoter regions, regulating gene expression at the transcription level; miRNAs inhibit mRNA translation by inducing mRNA degradation and/or blocking the translation machinery, Databases, such as TransFAC [2] on TF-to-mRNA regulation, TransmiR [3] on TF-to-miRNA regulation, and TarBase [4] on miRNA-to-mRNA regulation, provide experimentally validated regulation relationships between regulators and their targets. Such data alone are too limited to enable large-scaled studies. The reverse engineering strategy comes into use where the regulatory relationships between TFs or miRNAs and their putative targets (cause) are inferred from the observed expression correlations (consequence) (for a review see [10])
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