CBL enhances breast tumor formation by inhibiting tumor suppressive activity of TGF-β signaling.
ABSTRACT Casitas B-lineage lymphoma (CBL) protein family functions as multifunctional adaptor proteins and E3 ubiquitin ligases that are implicated as regulators of signaling in various cell types. Recent discovery revealed mutations of proto-oncogenic CBL in the linker region and RING finger domain in human acute myeloid neoplasm, and these transforming mutations induced carcinogenesis. However, the adaptor function of CBL mediated signaling pathway during tumorigenesis has not been well characterized. Here, we show that CBL is highly expressed in breast cancer cells and significantly inhibits transforming growth factor-β (TGF-β) tumor suppressive activity. Knockdown of CBL expression resulted in the increased expression of TGF-β target genes, PAI-I and CDK inhibitors such as p15(INK4b) and p21(Cip1). Furthermore, we demonstrate that CBL is frequently overexpressed in human breast cancer tissues, and the loss of CBL decreases the tumorigenic activity of breast cancer cells in vivo. CBL directly binds to Smad3 through its proline-rich motif, thereby preventing Smad3 from interacting with Smad4 and blocking nuclear translocation of Smad3. CBL-b, one of CBL protein family, also interacted with Smad3 and knockdown of both CBL and CBL-b further enhanced TGF-β transcriptional activity. Our findings provide evidence for a previously undescribed mechanism by which oncogenic CBL can block TGF-β tumor suppressor activity.Oncogene advance online publication, 6 February 2012; doi:10.1038/onc.2012.18.
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ABSTRACT: Analysis of the biological gene networks involved in a disease may lead to the identification of therapeutic targets. Such analysis requires exploring network properties, in particular the importance of individual network nodes (i.e., genes). There are many measures that consider the importance of nodes in a network and some may shed light on the biological significance and potential optimality of a gene or set of genes as therapeutic targets. This has been shown to be the case in cancer therapy. A dilemma exists, however, in finding the best therapeutic targets based on network analysis since the optimal targets should be nodes that are highly influential in, but not toxic to, the functioning of the entire network. In addition, cancer therapeutics targeting a single gene often result in relapse since compensatory, feedback and redundancy loops in the network may offset the activity associated with the targeted gene. Thus, multiple genes reflecting parallel functional cascades in a network should be targeted simultaneously, but require the identification of such targets. We propose a methodology that exploits centrality statistics characterizing the importance of nodes within a gene network that is constructed from the gene expression patterns in that network. We consider centrality measures based on both graph theory and spectral graph theory. We also consider the origins of a network topology, and show how different available representations yield different node importance results. We apply our techniques to tumor gene expression data and suggest that the identification of optimal therapeutic targets involving particular genes, pathways and sub-networks based on an analysis of the nodes in that network is possible and can facilitate individualized cancer treatments. The proposed methods also have the potential to identify candidate cancer therapeutic targets that are not thought to be oncogenes but nonetheless play important roles in the functioning of a cancer-related network or pathway.Frontiers in Genetics 01/2014; 5:12.
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ABSTRACT: Background and PurposeTo investigate the in vivo antitumor efficacy of YC-1 in an MDA-MB-468 xenograft model and elucidate the mechanism of downregulation of enhancer of zeste homology 2 (EZH2) by YC-1 in breast cancer cells.Experimental ApproachIn YC-1-treated breast cancer cells and tumor specimens from YC-1-treated MDA-MB-468 xenografts, EZH2 expression was analyzed by western blotting. Pharmacological inhibitors and short hairpin RNA–mediated knockdown were applied to identify possible signaling pathways involved in EZH2 downregulation by YC-1.Key ResultsYC-1 reduced viability of breast cancer cells and tumor growth in MDA-MB-468 xenografts. In breast cancer cells, YC-1 downregulated EZH2 expression in a concentration- and time-dependent manner. Depletion of EZH2 reduced the proliferation and the susceptibility of breast cancer cells to YC-1-induced apoptosis. EZH2 expression was suppressed in tumor specimens from YC-1-treated MDA-MB-468 xenograft mice. Both the degradation rate and ubiquitination of EZH2 were enhanced by YC-1. Downregulation of EZH2 by YC-1 was associated with the activation of protein kinase A and Src–Raf–ERK-mediated signaling pathways. Furthermore, depletion of Casitas B-lineage lymphoma (c-Cbl), an E3 ubiquitin ligase, abolished YC-1-induced-EZH2 suppression and apoptosis. YC-1 rapidly activated c-Cbl to induce signaling associated with ERK and EZH2.Conclusions and ImplicationsIn this study, we discovered that YC-1 induces apoptosis and inhibits tumor growth of breast cancer cells via downregulation of EZH2 by activating c-Cbl and ERK. These data suggest that YC-1 is a potential anticancer drug candidate in triple-negative breast cancer.British Journal of Pharmacology 04/2014; · 5.07 Impact Factor
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ABSTRACT: Introduction Recent studies have shown that miR-31 could play a potential role as diagnostic and prognostic biomarkers of several cancers including lung cancer. The aim of this study is to globally summarize the predicting targets of miR-31 and their potential function, pathways and networks, which are involved in the biological behavior of lung cancer. Methods We have conducted the natural language processing (NLP) analysis to identify lung cancer-related molecules in our previous work. In this study, miR-31 targets predicted by combinational computational methods. All target genes were characterized by gene ontology (GO), pathway and network analysis. In addition, miR-31 targets analysis were integrated with the results from NLP analysis, followed by hub genes interaction analysis. Result We identified 27 hub genes by the final integrative analysis and suggested that miR-31 may be involved in the initiation, progression and treatment response of lung cancer through cell cycle, cytochrome P450 pathway, metabolic pathways, apoptosis, chemokine signaling pathway, MAPK signaling pathway, as well as others. Conclusion Our data may help researchers to predict the molecular mechanisms of miR-31 in the molecular mechanism of lung cancer comprehensively. Moreover, the present data indicate that the interaction of miR-31 targets may be promising candidates as biomarkers for the diagnosis, prognosis and personalized therapy of lung cancer.Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie 01/2014; · 2.24 Impact Factor