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      DYRK1A inhibition suppresses STAT3/EGFR/Met signalling and sensitizes EGFR wild‐type NSCLC cells to AZD9291

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          DYRK1A is considered a potential cancer therapeutic target, but the role of DYRK1A in NSCLC oncogenesis and treatment requires further investigation. In our study, high DYRK1A expression was observed in tumour samples from patients with lung cancer compared with normal lung tissues, and the high levels of DYRK1A were related to a reduced survival time in patients with lung cancer. Meanwhile, the DYRK1A inhibitor harmine could suppress the proliferation of NSCLC cells compared to that of the control. As DYRK1A suppression might be effective in treating NSCLC, we next explored the possible specific molecular mechanisms that were involved. We showed that DYRK1A suppression by siRNA could suppress the levels of EGFR and Met in NSCLC cells. Furthermore, DYRK1A siRNA could inhibit the expression and nuclear translocation of STAT3. Meanwhile, harmine could also regulate the STAT3/EGFR/Met signalling pathway in human NSCLC cells. AZD9291 is effective to treat NSCLC patients with EGFR‐sensitivity mutation and T790 M resistance mutation, but the clinical efficacy in patients with wild‐type EGFR remains modest. We showed that DYRK1A repression could enhance the anti‐cancer effect of AZD9291 by inducing apoptosis and suppressing cell proliferation in EGFR wild‐type NSCLC cells. In addition, harmine could enhance the anti‐NSCLC activity of AZD9291 by modulating STAT3 pathway. Finally, harmine could enhance the anti‐cancer activity of AZD9291 in primary NSCLC cells. Collectively, targeting DYRK1A might be an attractive target for AZD9291 sensitization in EGFR wild‐type NSCLC patients.

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          Most cited references 45

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

           P Talalay,  T C Chou (1984)
          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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            Online Survival Analysis Software to Assess the Prognostic Value of Biomarkers Using Transcriptomic Data in Non-Small-Cell Lung Cancer

            In the last decade, optimized treatment for non-small cell lung cancer had lead to improved prognosis, but the overall survival is still very short. To further understand the molecular basis of the disease we have to identify biomarkers related to survival. Here we present the development of an online tool suitable for the real-time meta-analysis of published lung cancer microarray datasets to identify biomarkers related to survival. We searched the caBIG, GEO and TCGA repositories to identify samples with published gene expression data and survival information. Univariate and multivariate Cox regression analysis, Kaplan-Meier survival plot with hazard ratio and logrank P value are calculated and plotted in R. The complete analysis tool can be accessed online at: All together 1,715 samples of ten independent datasets were integrated into the system. As a demonstration, we used the tool to validate 21 previously published survival associated biomarkers. Of these, survival was best predicted by CDK1 (p<1E-16), CD24 (p<1E-16) and CADM1 (p = 7E-12) in adenocarcinomas and by CCNE1 (p = 2.3E-09) and VEGF (p = 3.3E-10) in all NSCLC patients. Additional genes significantly correlated to survival include RAD51, CDKN2A, OPN, EZH2, ANXA3, ADAM28 and ERCC1. In summary, we established an integrated database and an online tool capable of uni- and multivariate analysis for in silico validation of new biomarker candidates in non-small cell lung cancer.
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              Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclasses.

              We have generated a molecular taxonomy of lung carcinoma, the leading cause of cancer death in the United States and worldwide. Using oligonucleotide microarrays, we analyzed mRNA expression levels corresponding to 12,600 transcript sequences in 186 lung tumor samples, including 139 adenocarcinomas resected from the lung. Hierarchical and probabilistic clustering of expression data defined distinct subclasses of lung adenocarcinoma. Among these were tumors with high relative expression of neuroendocrine genes and of type II pneumocyte genes, respectively. Retrospective analysis revealed a less favorable outcome for the adenocarcinomas with neuroendocrine gene expression. The diagnostic potential of expression profiling is emphasized by its ability to discriminate primary lung adenocarcinomas from metastases of extra-pulmonary origin. These results suggest that integration of expression profile data with clinical parameters could aid in diagnosis of lung cancer patients.

                Author and article information

                J Cell Mol Med
                J. Cell. Mol. Med
                Journal of Cellular and Molecular Medicine
                John Wiley and Sons Inc. (Hoboken )
                27 August 2019
                November 2019
                : 23
                : 11 ( doiID: 10.1111/jcmm.v23.11 )
                : 7427-7437
                [ 1 ] Department of Clinical Pharmacology, Affiliated Hangzhou First People's Hospital Zhejiang University School of Medicine Hangzhou, Zhejiang China
                [ 2 ] School of Medicine Zhejiang University City College Hangzhou Zhejiang China
                [ 3 ] College of Pharmaceutical Sciences Zhejiang University Hangzhou, Zhejiang China
                [ 4 ] Hangzhou Translational Medicine Research Center, Affiliated Hangzhou First People's Hospital Zhejiang University School of Medicine Hangzhou, Zhejiang China
                Author notes
                [* ] Correspondence

                Neng‐ming Lin, Department of Clinical Pharmacology, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, No.261 Huansha Road, Hangzhou, Zhejiang 310006, China.

                Email: lnm1013@

                Chong Zhang, School of Medicine, Zhejiang University City College, No.51 Huzhou Street, Hangzhou, Zhejiang 310015, China.

                Email: zhangchong@

                © 2019 The Authors. Journal of Cellular and Molecular Medicine published by Foundation for Cellular and Molecular Medicine and John Wiley & Sons Ltd.

                This is an open access article under the terms of the License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                Page count
                Figures: 7, Tables: 0, Pages: 11, Words: 5706
                Funded by: Hangzhou Major Science and Technology Project
                Award ID: 20172016A01
                Funded by: National Natural Science Foundation of China
                Award ID: 81702887
                Funded by: Zhejiang Provincial Foundation of Natural Science
                Award ID: LY19H310004
                Funded by: Teachers Research Fund of Zhejiang University City College
                Award ID: J‐19006
                Funded by: Hangzhou Medical Key Discipline Construction
                Award ID: 2017‐51‐07
                Funded by: Scientific and Technological Developing Scheme of Hangzhou City
                Award ID: 20191203B49
                Original Article
                Original Articles
                Custom metadata
                November 2019
                Converter:WILEY_ML3GV2_TO_NLMPMC version:5.7.0 mode:remove_FC converted:28.10.2019


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