Synergetic regulatory networks mediated by oncogene-driven microRNAs and transcription factors in serous ovarian cancer

Abstract

Although high-grade serous ovarian cancer (OVC) is the most lethal gynecologic malignancy in women, little is known about the regulatory mechanisms in the cellular processes that lead to this cancer. Recently, accumulated lines of evidence have shown that the interplay between transcription factors (TFs) and microRNAs (miRNAs) is critical in cellular regulation during tumorigenesis. A comprehensive investigation of TFs and miRNAs, and their target genes, may provide a deeper understanding of the regulatory mechanisms in the pathology of OVC. In this study, we have integrated three complementary algorithms into a framework, aiming to infer the regulation by miRNAs and TFs in conjunction with gene expression profiles. We demonstrated the utility of our framework by inferring 67 OVC-specific regulatory feed-forward loops (FFL) initiated by miRNAs or TFs in high-grade serous OVC. By analyzing these regulatory behaviors, we found that all the 67 FFLs are consistent in their regulatory effects on genes that jointly targeted by miRNAs and TFs. Remarkably, we unveiled an unbalanced distribution of FFLs with different oncogenic effects. In total, 31 of the 67 coherent FFLs were mainly initiated by oncogenes. On the contrary, only 4 of the FFLs were initiated by tumor suppressor genes. These overwhelmingly observed oncogenic genes were further detected in a sub-network with 32 FFLs centered by miRNA let-7b and TF TCF7L1 to regulate cell differentiation. Closer inspection of 32 FFLs revealed that 75% of the miRNAs reportedly play functional roles in cell differentiation, especially when enriched in epithelial–mesenchymal transitions. This study provides a comprehensive pathophysiological overview of recurring coherent circuits in OVC that are co-regulated by miRNAs and TFs. The prevalence of oncogenic coherent FFLs in serous OVC suggests that oncogene-driven regulatory motifs could cooperatively act upon critical cellular process such as cell differentiation in a highly efficient and consistent manner.

Systematic dissection of dysregulated transcription factor–miRNA feed-forward loops across tumor types

“…TF–miRNA FFLs influence many biological processes mainly as noise buffering [13]. Over the past decade, TF–miRNA FFLs have been widely used to identify the cancer-associated genes or miRNAs in many tumor types, such as NSCLC [16], glioblastoma [14], ovarian cancer [15] and T-cell acute lymphoblastic leukemia [17]. However, such studies have focused on the individual tumor type.…”

“…The TF–miRNA FFL has been reported as motif that is overrepresented in regulatory network, and can minimize expression fluctuation against signaling noise [11]. During the past decade, network approach based on TF–miRNA FFLs has been demonstrated as a promising tool to dissect the etiology of many tumors [13], such as glioblastoma [14], ovarian cancer [15], non-small-cell lung cancer (NSCLC) [16] and T-cell acute lymphoblastic leukemia [17]. For example, Mitra et al identified the disrupted FFLs in NSCLC from a predicted, but reproducible, TF and miRNA regulatory network, and found that miR-9-5p and miR-130b-3p could inhibit the tumor-suppressive activity of TGF-β pathway by targeting a core regulatory molecule TGFBR2 [16].…”

Reproducible combinatorial regulatory networks elucidate novel oncogenic microRNAs in non-small cell lung cancer

“…Therefore, miRNA(s) and TF(s) may regulate each other reciprocally and form a feed-back loop (FBL), or both can jointly regulate the expression of target genes and form a feed-forward loop (FFL) (Guo et al 2010). FFL-based combinatorial regulatory network approaches recently emerged as promising tools to elucidate complex diseases such as schizophrenia (Guo et al 2010), glioblastoma multiforme (Setty et al 2012;Sun et al 2012), ovarian cancer (Zhao et al 2013), and osteosarcoma (Poos et al 2013). However, current FFL studies mostly rely on the predicted regulation information, leading to high false positive rates.…”

EDdb: A web resource for eating disorder and its application to identify an extended adipocytokine signaling pathway related to eating disorder

“…HSD11B2 is the enzyme that regulates the glucocorticoid metabolism. The interaction between glucocorticoid and insulin is a very important signal for regulating long-term energy metabolism [64]. In particular, NR3C1, the downstream gene of HSD11B2, has been reported to be associated with glucocorticoid resistance, eating disorder and obesity [58,66].…”