Integrative Identification of Deregulated MiRNA/TF-Mediated Gene Regulatory Loops and Networks in Prostate Cancer

Abstract

MicroRNAs (miRNAs) have attracted a great deal of attention in biology and medicine. It has been hypothesized that miRNAs interact with transcription factors (TFs) in a coordinated fashion to play key roles in regulating signaling and transcriptional pathways and in achieving robust gene regulation. Here, we propose a novel integrative computational method to infer certain types of deregulated miRNA-mediated regulatory circuits at the transcriptional, post-transcriptional and signaling levels. To reliably predict miRNA-target interactions from mRNA/miRNA expression data, our method collectively utilizes sequence-based miRNA-target predictions obtained from several algorithms, known information about mRNA and miRNA targets of TFs available in existing databases, certain molecular structures identified to be statistically over-represented in gene regulatory networks, available molecular subtyping information, and state-of-the-art statistical techniques to appropriately constrain the underlying analysis. In this way, the method exploits almost every aspect of extractable information in the expression data. We apply our procedure on mRNA/miRNA expression data from prostate tumor and normal samples and detect numerous known and novel miRNA-mediated deregulated loops and networks in prostate cancer. We also demonstrate instances of the results in a number of distinct biological settings, which are known to play crucial roles in prostate and other types of cancer. Our findings show that the proposed computational method can be used to effectively achieve notable insights into the poorly understood molecular mechanisms of miRNA-mediated interactions and dissect their functional roles in cancer in an effort to pave the way for miRNA-based therapeutics in clinical settings.

Elucidating MicroRNA Regulatory Networks Using Transcriptional, Post-transcriptional, and Histone Modification Measurements

“…While there are both experimental (Chi et al, 2009; Wen et al, 2011) and computational (Agarwal et al, 2015; Chiu et al, 2015; Garcia et al, 2011) methods to identify miRNA targets, identifying miRNA-regulated transcriptional changes is more challenging. Numerous computational approaches have used computational target prediction algorithms with transcription factor binding prediction tools to model the downstream effects of miRNAs through transcription factors (Afshar et al, 2014; Bisognin et al, 2012; Friard et al, 2010; Naeem et al, 2011; Tu et al, 2009). Recent advances in RNA sequencing efforts have enabled the use of total RNA measurements to capture both intronic and exonic changes.…”

Systems biology study of transcriptional and post-transcriptional co-regulatory network sheds light on key regulators involved in important biological processes in Citrus sinensis

“…Shao et al (2015) developed a pipeline capable of inferring regulatory relationships between miRNAs and transcription factors from Chip-Seq data, and used it to derive coregulatory network in mouse embryonic stem cells. Afshar et al (2014) proposed a method that collectively utilizes various data, algorithms, and statistical techniques to infer miRNA-mediated regulatory circuits at the transcriptional, post-transcriptional, and signaling levels. Naeem (2012) developed a database of miRNA/ Transcription factor-target relationship by means of literature text mining, applied statistical tests on regulator perturbation datasets to predict activity state of miRNA/ Transcription factor, and used gene set enrichment methods to predict regulation cascades.…”

Thrombin generation predicts early recurrence in breast cancer patients

“…Differently, a high TG potential, characterized by reduced lag-time and increased ETP and peak values, indicates a hypercoagulable state. 45 In the setting of malignant disease, this assay represents a suitable tool to quantify the combined and cumulative effects of different factors contributing to cancer-associated coagulopathy. 16 High TG levels have been described in different conditions at risk of venous thromboembolism, including malignancy 16,38,42 and are independently associated with an increased risk of thrombosis in cancer patients.…”