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      Tumor suppressor genes and their underlying interactions in paclitaxel resistance in cancer therapy

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          Abstract

          Objectives

          Paclitaxel (PTX) is frequently used in the clinical treatment of solid tumors. But the PTX-resistance is a great obstacle in cancer treatment. Exploration of the mechanisms of drug resistance suggests that tumor suppressor genes (TSGs) play a key role in the response of chemotherapeutic drugs. TSGs, a set of genes that are often inactivated in cancers, can regulate various biological processes. In this study, an overview of the contribution of TSGs to PTX resistance and their underlying relationship in cancers are reported by using GeneMANIA, a web-based tool for gene/protein function prediction.

          Methods

          Using PubMed online database and Google web site, the terms “paclitaxel resistance” or “taxol resistance” or “drug resistance” or “chemotherapy resistance”, and “cancer” or “carcinoma”, and “tumor suppressor genes” or “TSGs” or “negative regulated protein” or “antioncogenes” were searched and analyzed. GeneMANIA data base was used to predict gene/protein interactions and functions.

          Results

          We identified 22 TSGs involved in PTX resistance, including BRCA1, TP53, PTEN, APC, CDKN1A, CDKN2A, HIN- 1, RASSF1, YAP, ING4, PLK2, FBW7, BLU, LZTS1, REST, FADD, PDCD4, TGFBI, ING1, Bax, PinX1 and hEx. The TSGs were found to have direct and indirect relationships with each other, and thus they could contribute to PTX resistance as a group. The varied expression status and regulation function of the TSGs on cell cycle in different cancers might play an important role in PTX resistance.

          Conclusion

          A further understanding of the roles of tumor suppressor genes in drug resistance is an important step to overcome chemotherapy tolerance. Tumor suppressor gene therapy targets the altered genes and signaling pathways and can be a new strategy to reverse chemotherapy resistance.

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          Most cited references 87

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          Tumour stem cells and drug resistance.

          The contribution of tumorigenic stem cells to haematopoietic cancers has been established for some time, and cells possessing stem-cell properties have been described in several solid tumours. Although chemotherapy kills most cells in a tumour, it is believed to leave tumour stem cells behind, which might be an important mechanism of resistance. For example, the ATP-binding cassette (ABC) drug transporters have been shown to protect cancer stem cells from chemotherapeutic agents. Gaining a better insight into the mechanisms of stem-cell resistance to chemotherapy might therefore lead to new therapeutic targets and better anticancer strategies.
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            The GeneMANIA prediction server: biological network integration for gene prioritization and predicting gene function

            GeneMANIA (http://www.genemania.org) is a flexible, user-friendly web interface for generating hypotheses about gene function, analyzing gene lists and prioritizing genes for functional assays. Given a query list, GeneMANIA extends the list with functionally similar genes that it identifies using available genomics and proteomics data. GeneMANIA also reports weights that indicate the predictive value of each selected data set for the query. Six organisms are currently supported (Arabidopsis thaliana, Caenorhabditis elegans, Drosophila melanogaster, Mus musculus, Homo sapiens and Saccharomyces cerevisiae) and hundreds of data sets have been collected from GEO, BioGRID, Pathway Commons and I2D, as well as organism-specific functional genomics data sets. Users can select arbitrary subsets of the data sets associated with an organism to perform their analyses and can upload their own data sets to analyze. The GeneMANIA algorithm performs as well or better than other gene function prediction methods on yeast and mouse benchmarks. The high accuracy of the GeneMANIA prediction algorithm, an intuitive user interface and large database make GeneMANIA a useful tool for any biologist.
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              CDK inhibitors: positive and negative regulators of G1-phase progression.

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                Author and article information

                Contributors
                15056952858@163.com
                hushilian@126.com
                +86 551 6228 3589 , gdshen@ustc.edu.cn
                shenganustc@163.com
                Journal
                Cancer Cell Int
                Cancer Cell Int
                Cancer Cell International
                BioMed Central (London )
                1475-2867
                20 February 2016
                20 February 2016
                2016
                : 16
                Affiliations
                [ ]Department of Geriatrics, Anhui Provincial Hospital affiliated to Anhui Medical University, 17 Lujiang Road, Hefei, 230001 China
                [ ]Anhui Provincial Key Laboratory of Tumor Immunotherapy and Nutrition Therapy, Hefei, 230001 China
                Article
                290
                10.1186/s12935-016-0290-9
                4761208
                26900348
                © Xu et al. 2016

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                Funding
                Funded by: Anhui Provincial Natural Science Foundation
                Award ID: 1408085MH167
                Award Recipient :
                Funded by: Anhui Provincial science and technology key project
                Award ID: 1301042094
                Award Recipient :
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                © The Author(s) 2016

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