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  • 1
    Online Resource
    Online Resource
    Afatinib Reverses EMT via Inhibiting CD44-Stat3 Axis to Promote Radiosensitivity in Nasopharyngeal Carcinoma
    MDPI AG ; 2022
    In:  Pharmaceuticals Vol. 16, No. 1 ( 2022-12-27), p. 37-
    Details
    In: Pharmaceuticals, MDPI AG, Vol. 16, No. 1 ( 2022-12-27), p. 37-
    Abstract: Background: Afatinib, a second-generation tyrosine kinase inhibitor (TKI), exerts its radiosensitive effects in nasopharyngeal carcinoma (NPC). However, the detailed mechanism of afatinib-mediated sensitivity to radiation is still obscure in NPC. Methods: Quantitative phosphorylated proteomics and bioinformatics analysis were performed to illustrate the global phosphoprotein changes. The activity of the CD44-Stat3 axis and Epithelial-Mesenchymal Transition (EMT)-linked markers were evaluated by Western blotting. Wound healing and transwell assays were used to determine the levels of cell migration upon afatinib combined IR treatment. Cell proliferation was tested by CCK-8 assay. A pharmacological agonist by IL-6 was applied to activate Stat3. The xenograft mouse model was treated with afatinib, radiation or a combination of afatinib and radiation to detect the radiosensitivity of afatinib in vivo. Results: In the present study, we discovered that afatinib triggered global protein phosphorylation alterations in NPC cells. Further, bioinformatics analysis indicated that afatinib inhibited the CD44-Stat3 signaling and subsequent EMT process. Moreover, functional assays demonstrated that afatinib combined radiation treatment remarkably impeded cell viability, migration, EMT process and CD44-Stat3 activity in vitro and in vivo. In addition, pharmacological stimulation of Stat3 rescued radiosensitivity and biological functions induced by afatinib in NPC cells. This suggested that afatinib reversed the EMT process by blocking the activity of the CD44-Stat3 axis. Conclusion: Collectively, this work identifies the molecular mechanism of afatinib as a radiation sensitizer, thus providing a potentially useful combination treatment and drug target for NPC radiosensitization. Our findings describe a new function of afatinib in radiosensitivity and cancer treatment.
    Type of Medium: Online Resource
    ISSN: 1424-8247
    URL: Article
    DOI: 10.3390/ph16010037
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2193542-7
    SSG: 15,3
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  • 2
    Online Resource
    Online Resource
    CapG promoted nasopharyngeal carcinoma cell motility involving Rho motility pathway independent of ROCK
    Springer Science and Business Media LLC ; 2022
    In:  World Journal of Surgical Oncology Vol. 20, No. 1 ( 2022-10-19)
    Details
    In: World Journal of Surgical Oncology, Springer Science and Business Media LLC, Vol. 20, No. 1 ( 2022-10-19)
    Abstract: Gelsolin-like capping actin protein (CapG) modulates actin dynamics and actin-based motility with a debatable role in tumorigenic progression. The motility-associated functions and potential molecular mechanisms of CapG in nasopharyngeal carcinoma (NPC) remain unclear. Methods CapG expression was detected by immunohistochemistry in a cohort of NPC tissue specimens and by Western blotting assay in a variety of NPC cell lines. Loss of function and gain of function of CapG in scratch wound-healing and transwell assays were performed. Inactivation of Rac1 and ROCK with the specific small molecular inhibitors was applied to evaluate CapG’s role in NPC cell motility. GTP-bound Rac1 and phosphorylated-myosin light chain 2 (p-MLC2) were measured in the ectopic CapG overexpressing cells. Finally, CapG-related gene set enrichment analysis was conducted to figure out the significant CapG-associated pathways in NPC. Results CapG disclosed increased level in the poorly differentiated NPC tissues and highly metastatic cells. Knockdown of CapG reduced NPC cell migration and invasion in vitro, while ectopic CapG overexpression showed the opposite effect. Ectopic overexpression of CapG compensated for the cell motility loss caused by simultaneous inactivation of ROCK and Rac1 or inactivation of ROCK alone. GTP-bound Rac1 weakened, and p-MLC2 increased in the CapG overexpressing cells. Bioinformatics analysis validated a positive correlation of CapG with Rho motility signaling, while Rac1 motility pathway showed no significant relationship. Conclusions The present findings highlight the contribution of CapG to NPC cell motility independent of ROCK and Rac1. CapG promotes NPC cell motility at least partly through MLC2 phosphorylation and contradicts with Rac1 activation.
    Type of Medium: Online Resource
    ISSN: 1477-7819
    URL: Article
    DOI: 10.1186/s12957-022-02808-7
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2022
    detail.hit.zdb_id: 2118383-1
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  • 3
    Online Resource
    Online Resource
    Quantitative proteomic profiling of tumor-associated vascular endothelial cells in colorectal cancer
    The Company of Biologists ; 2019
    In:  Biology Open
    Details
    In: Biology Open, The Company of Biologists
    Abstract: To investigate the global proteomic profiles of vascular endothelial cells (VECs) in the tumor microenvironment and antiangiogenic therapy for colorectal cancer (CRC), matched pairs of normal (NVECs) and tumor-associated VECs (TVECs) were purified from CRC tissues by laser capture microdissection and subjected to iTRAQ based quantitative proteomics analysis. Here, 216 differentially expressed proteins (DEPs) were identified and performed bioinformatics analysis. Interestingly, these proteins were implicated in epithelial mesenchymal transition (EMT), ECM-receptor interaction, focal adhesion, PI3K-Akt signaling pathway, angiogenesis and HIF-1 signaling pathway, which may play important roles in CRC angiogenesis. Among these DEPs, we found that Tenascin-C (TNC) was upregulated in TVECs of CRC and correlated with CRC multistage carcinogenesis and metastasis. Furthermore, the reduction of tumor-derived TNC could attenuate human umbilical vein endothelial cell (HUVEC) proliferation, migration and tube formation through ITGB3/FAK/Akt signaling pathway. Based on the present work, we provided a large-scale proteomic profiling of VECs in CRC with quantitative information, a certain number of potential antiangiogenic targets and a novel vision in the angiogenesis bio-mechanism of CRC.
    Type of Medium: Online Resource
    ISSN: 2046-6390
    URL: Article
    DOI: 10.1242/bio.042838
    Language: English
    Publisher: The Company of Biologists
    Publication Date: 2019
    detail.hit.zdb_id: 2632264-X
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  • 4
    Online Resource
    Online Resource
    LRSSL: predict and interpret drug–disease associations based on data integration using sparse subspace learning
    Oxford University Press (OUP) ; 2017
    In:  Bioinformatics Vol. 33, No. 8 ( 2017-04-15), p. 1187-1196
    Details
    In: Bioinformatics, Oxford University Press (OUP), Vol. 33, No. 8 ( 2017-04-15), p. 1187-1196
    Abstract: Exploring the potential curative effects of drugs is crucial for effective drug development. Previous studies have indicated that integration of multiple types of information could be conducive to discovering novel indications of drugs. However, how to efficiently identify the mechanism behind drug–disease associations while integrating data from different sources remains a challenging problem. Results In this research, we present a novel method for indication prediction of both new drugs and approved drugs. This method is based on Laplacian regularized sparse subspace learning (LRSSL), which integrates drug chemical information, drug target domain information and target annotation information. Experimental results show that the proposed method outperforms several recent approaches for predicting drug–disease associations. Some drug therapeutic effects predicted by the method could be validated by database records or literatures. Moreover, with L1-norm constraint, important drug features have been extracted from multiple drug feature profiles. Case studies suggest that the extracted drug features could be beneficial to interpretation of the predicted results. Availability and Implementation https://github.com/LiangXujun/LRSSL Supplementary information Supplementary data are available at Bioinformatics online.
    Type of Medium: Online Resource
    ISSN: 1367-4803 , 1367-4811
    URL: Article
    DOI: 10.1093/bioinformatics/btw770
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2017
    detail.hit.zdb_id: 1468345-3
    SSG: 12
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  • 5
    Online Resource
    Online Resource
    Learning important features from multi-view data to predict drug side effects
    Springer Science and Business Media LLC ; 2019
    In:  Journal of Cheminformatics Vol. 11, No. 1 ( 2019-12)
    Details
    In: Journal of Cheminformatics, Springer Science and Business Media LLC, Vol. 11, No. 1 ( 2019-12)
    Abstract: The problem of drug side effects is one of the most crucial issues in pharmacological development. As there are many limitations in current experimental and clinical methods for detecting side effects, a lot of computational algorithms have been developed to predict side effects with different types of drug information. However, there is still a lack of methods which could integrate heterogeneous data to predict side effects and select important features at the same time. Here, we propose a novel computational framework based on multi-view and multi-label learning for side effect prediction. Four different types of drug features are collected and graph model is constructed from each feature profile. After that, all the single view graphs are combined to regularize the linear regression functions which describe the relationships between drug features and side effect labels. L1 penalties are imposed on the regression coefficient matrices in order to select features relevant to side effects. Additionally, the correlations between side effect labels are also incorporated into the model by graph Laplacian regularization. The experimental results show that the proposed method could not only provide more accurate prediction for side effects but also select drug features related to side effects from heterogeneous data. Some case studies are also supplied to illustrate the utility of our method for prediction of drug side effects.
    Type of Medium: Online Resource
    ISSN: 1758-2946
    URL: Article
    DOI: 10.1186/s13321-019-0402-3
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2019
    detail.hit.zdb_id: 2486539-4
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  • 6
    Online Resource
    Online Resource
    Proteomics Profiling Reveals the Molecular Signatures and Potential Therapeutic Targets of Human Nasopharyngeal Carcinoma
    Elsevier BV ; 2023
    In:  Molecular & Cellular Proteomics Vol. 22, No. 6 ( 2023-06), p. 100567-
    Details
    In: Molecular & Cellular Proteomics, Elsevier BV, Vol. 22, No. 6 ( 2023-06), p. 100567-
    Type of Medium: Online Resource
    ISSN: 1535-9476
    URL: Article
    DOI: 10.1016/j.mcpro.2023.100567
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2023
    detail.hit.zdb_id: 2071375-7
    SSG: 12
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