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: Abstract Recent advances in pharmacogenomics technologies allow bold steps to be taken towards personalized medicine, more accurate health planning, and personalized drug development. In this framework, systems pharmacology network-based approaches offer an appealing way for integrating multi-omics data and set the basis for defining systems-level drug response biomarkers. On the road to individualized tamoxifen treatment in estrogen receptor-positive breast cancer patients, we examine the dynamics of the attendant pharmacological response mechanisms. By means of an "integromics" network approach, we assessed the tamoxifen effect through the way the high-order organization of interactome (i.e., the modules) is perturbed. To accomplish that, first we integrated the time series transcriptome data with the human protein interaction data, and second, an efficient module-detecting algorithm was applied onto the composite graphs. Our findings show that tamoxifen induces severe modular transformations on specific areas of the interactome. Our modular biomarkers in response to tamoxifen attest to the immunomodulatory role of tamoxifen, and further reveal that it deregulates cell cycle and apoptosis pathways, while coordinating the proteasome and basal transcription factors. To the best of our knowledge, this is the first report that informs the fields of personalized medicine and clinical pharmacology about the actual dynamic interactome response to tamoxifen administration.Omics: a journal of integrative biology 12/2013; · 2.29 Impact Factor
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ABSTRACT: STUDY QUESTION: What is the profile of miRNAs in seminal plasma of patients with non-obstructive azoospermia (NOA)? SUMMARY ANSWER: miR-141, miR-429 and miR-7-1-3p are significantly increased in seminal plasma of patients with NOA compared with fertile controls. WHAT IS KNOWN ALREADY: There is currently an urgent need to develop a noninvasive diagnostic test for NOA. Altered microRNA (miRNA) profiles have been proposed as potential biomarkers for the diagnosis of disease states. STUDY DESIGN, SIZE, DURATION: A total of 200 subjects (n = 100 for NOA, n = 100 for fertile control) were recruited to participate in this study. Recruitment took place from May 2008 to June 2010. PARTICIPANTS/MATERIALS, SETTING, METHODS: We employed a strategy consisting of initial screening by TaqMan Low Density Array then further validation with a TaqMan quantitative RT-PCR assay. Validation of the profiling results was conducted in two independent phases. In addition, the expression of the three validated seminal plasma miRNAs (sp-miRNAs) was examined in testicular tissues of patients with NOA and of fertile controls. Methylation status and functional analyses were also performed for the identified sp-miRNAs. MAIN RESULTS AND THE ROLE OF CHANCE: miR-141, miR-429 and miR-7-1-3p were significantly increased in seminal plasma of patients with NOA compared with fertile controls. As sensitive and specific biomarkers, the profiling of these three identified sp-miRNAs provides a novel noninvasive, semen-based test for NOA diagnosis. The methylation status of these sp-miRNAs was inversely associated with their expression patterns. Additionally, we found that Cbl and Tgfβ2 were down-regulated by miR-141, while Rb1 and Pik3r3 were down-regulated by miR-7-1-3p. LIMITATIONS, REASONS FOR CAUTION: miRNA expression profile was investigated in seminal plasma samples from only a small number of NOA patients. In future investigations, a larger sample size should be adopted and the functional role of the three sp-miRNAs should be further characterized in animal models. WIDER IMPLICATIONS OF THE FINDINGS: Given that sp-miRNAs show reproducible and stable expression levels, they are potentially novel noninvasive biomarkers for the diagnosis of NOA. We propose that the three sp-miRNAs described above may participate in a methylation-miRNA-gene network related to NOA development. This work provides a foundation for interpretation of miRNA changes associated with pathogenesis of NOA and extends the current understanding of human NOA pathogenesis. STUDY FUNDING/COMPETING INTEREST(S): This work was supported by the following grants: Key Project of National Natural Science Foundation of China (No. 30930079), National Basic Research Program of China (973 Program) (No. 2009CB941703, 2011CB944304), National Natural Science Foundation of China (No. 81072328 and 30901232); Science and Technology Development Fund Key Project of Nanjing Medical University (No. 2012NJMU002) and Priority Academic Program Development of Jiangsu Higher Education Institutions. The funding organizations played no role in the design and conduct of the study, in collection, management, analysis and interpretation of the data, or in the presentation, review or approval of the manuscript. There are no conflicts of interest to be declared.Human Reproduction 04/2013; · 4.67 Impact Factor
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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.