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      Over-expression of CKS1B activates both MEK/ERK and JAK/STAT3 signaling pathways and promotes myeloma cell drug-resistance

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          Abstract

          Here we demonstrate the crucial role of CKS1B in multiple myeloma (MM) progression and define CKS1B-mediated SKP2/p27 Kip1-independent down-stream signaling pathways. Forced-expression of CKS1B in MM cells increased cell multidrug-resistance. CKS1B activates STAT3 and MEK/ERK pathways. In contrast, SKP2 knockdown or p27 Kip1 over-expression resulted in activation of the STAT3 and MEK/ERK pathways. Further investigations showed that BCL2 is a downstream target of MEK/ERK signaling. Stimulation of STAT3 and MEK/ERK signaling pathways partially abrogated CKS1B knockdown induced MM cell death and growth inhibition. Targeting STAT3 and MEK/ ERK signaling pathways by specific inhibitors induced significant MM cell death and growth inhibition in CKS1B-overexpressing MM cells and their combinations resulted in synergy. Thus, our findings provide a rationale for targeting STAT3 and MEK/ERK/ BCL2 signaling in aggressive CKS1B-overexpressing MM.

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          Most cited references44

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          Quantitative analysis of dose-effect relationships: the combined effects of multiple drugs or enzyme inhibitors.

          A generalized method for analyzing the effects of multiple drugs and for determining summation, synergism and antagonism has been proposed. The derived, generalized equations are based on kinetic principles. The method is relatively simple and is not limited by whether the dose-effect relationships are hyperbolic or sigmoidal, whether the effects of the drugs are mutually exclusive or nonexclusive, whether the ligand interactions are competitive, noncompetitive or uncompetitive, whether the drugs are agonists or antagonists, or the number of drugs involved. The equations for the two most widely used methods for analyzing synergism, antagonism and summation of effects of multiple drugs, the isobologram and fractional product concepts, have been derived and been shown to have limitations in their applications. These two methods cannot be used indiscriminately. The equations underlying these two methods can be derived from a more generalized equation previously developed by us (59). It can be shown that the isobologram is valid only for drugs whose effects are mutually exclusive, whereas the fractional product method is valid only for mutually nonexclusive drugs which have hyperbolic dose-effect curves. Furthermore, in the isobol method, it is laborious to find proper combinations of drugs that would produce an iso-effective curve, and the fractional product method tends to give indication of synergism, since it underestimates the summation of the effect of mutually nonexclusive drugs that have sigmoidal dose-effect curves. The method described herein is devoid of these deficiencies and limitations. The simplified experimental design proposed for multiple drug-effect analysis has the following advantages: It provides a simple diagnostic plot (i.e., the median-effect plot) for evaluating the applicability of the data, and provides parameters that can be directly used to obtain a general equation for the dose-effect relation; the analysis which involves logarithmic conversion and linear regression can be readily carried out with a simple programmable electronic calculator and does not require special graph paper or tables; and the simplicity of the equation allows flexibility of application and the use of a minimum number of data points. This method has been used to analyze experimental data obtained from enzymatic, cellular and animal systems.
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            The molecular classification of multiple myeloma.

            To better define the molecular basis of multiple myeloma (MM), we performed unsupervised hierarchic clustering of mRNA expression profiles in CD138-enriched plasma cells from 414 newly diagnosed patients who went on to receive high-dose therapy and tandem stem cell transplants. Seven disease subtypes were validated that were strongly influenced by known genetic lesions, such as c-MAF- and MAFB-, CCND1- and CCND3-, and MMSET-activating translocations and hyperdiploidy. Indicative of the deregulation of common pathways by gene orthologs, common gene signatures were observed in cases with c-MAF and MAFB activation and CCND1 and CCND3 activation, the latter consisting of 2 subgroups, one characterized by expression of the early B-cell markers CD20 and PAX5. A low incidence of focal bone disease distinguished one and increased expression of proliferation-associated genes of another novel subgroup. Comprising varying fractions of each of the other 6 subgroups, the proliferation subgroup dominated at relapse, suggesting that this signature is linked to disease progression. Proliferation and MMSET-spike groups were characterized by significant overexpression of genes mapping to chromosome 1q, and both exhibited a poor prognosis relative to the other groups. A subset of cases with a predominating myeloid gene expression signature, excluded from the profiling analyses, had more favorable baseline characteristics and superior prognosis to those lacking this signature.
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              The SCF ubiquitin ligase: insights into a molecular machine.

              Ubiquitin ligases are well suited to regulate molecular networks that operate on a post-translational timescale. The F-box family of proteins - which are the substrate-recognition components of the Skp1-Cul1-F-box-protein (SCF) ubiquitin ligase - are important players in many mammalian functions. Here we explore a unifying and structurally detailed view of SCF-mediated proteolytic control of cellular processes that has been revealed by recent studies.
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                Author and article information

                Journal
                Oncotarget
                Oncotarget
                ImpactJ
                Oncotarget
                Impact Journals LLC
                1949-2553
                May 2010
                15 May 2010
                : 1
                : 1
                : 22-33
                Affiliations
                1 Division of Hematology/BMT/myeloma Program, University of Utah School of Medicine, University of Utah, Salt Lake City, UT,USA
                2 Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA
                3 Cancer Research Institute, Central South University, Changsha, China
                4 The Second Affiliated Hospital of Xiang Ya School of Medicine, Central South University, Changsha, China
                5 Department of Pathology, ARUP Reference Laboratory, University of Utah, Salt Lake City, UT, USA
                Author notes
                Correspondence to: Fenghuang Zhan, M.D., Ph.D., fenghuang.zhan@ 123456hsc.utah.edu
                Guido Tricot, M.D., Ph.D., guido.tricot@ 123456hsc.utah.edu
                Article
                10.18632/oncotarget.105
                2949973
                20930946
                85714c6e-609c-4310-ab54-b6b44e345953
                Copyright: © 2010 Shi et al.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 28 April 2010
                : 30 April 2010
                Categories
                Research Paper

                Oncology & Radiotherapy
                myeloma,cks1b,erk1/2,stat3,drug resistance
                Oncology & Radiotherapy
                myeloma, cks1b, erk1/2, stat3, drug resistance

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