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      MDM2 is a Potential Target Gene of Glycyrrhizic Acid for Circumventing Breast Cancer Resistance to Tamoxifen: Integrative Bioinformatics Analysis

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          Abstract

          Background:

          Tamoxifen is the drug of choice for treating breast cancer, particularly the estrogen receptor-positive luminal A subtype. However, the increased occurrence of Tamoxifen resistance highlights the need to develop an agent to enhance the effectiveness of this drug.

          Objective:

          Although glycyrrhizic acid (GA) is known to exhibit cytotoxic effects on Michigan Cancer Foundation-7 cells, the specific gene targets and pathways it employs to overcome Tamoxifen resistance are incompletely understood. Therefore, the goal of the present research is to discover the potential targets and pathways of GA by using a bioinformatics approach.

          Methods:

          Differentially expressed genes (DEGs) were identified in the Gene Expression Omnibus NCBI database using microarray data from GSE67916 and GSE85871. Further analyses were performed on these DEGs by using DAVID v6.8, STRING-DB v11.0, and Cytoscape v3.8.0. Analysis of gene alterations was performed using cBioPortal for target validation, and the relevant interaction process was examined via the molecular docking method.

          Results:

          Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses identified the PI3K-AKT signaling as the potential target mechanism. Construction of the protein–protein interaction network and analysis of hub genes identified the top 25 hub genes. Genetic alterations were observed in six potential target genes, such as CDK2, MDM2, NF1, SMAD3, PTPN11, and CALM1. Molecular docking analysis demonstrated that the docking score of GA is lower than that of the native ligand of p53. More importantly, 3n the PI3K-AKT signaling pathway is a potential target for overcoming Tamoxifen resistance in breast cancer. C

          Conclusion:

          MDM2 may be a potential gene target of GA and the PI3K-AKT signaling may be a prospective mechanism for overcoming Tamoxifen resistance in breast cancer cells. Additional research is required to validate the findings of this study.

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          Most cited references39

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          • Article: found
          Is Open Access

          Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists

          Functional analysis of large gene lists, derived in most cases from emerging high-throughput genomic, proteomic and bioinformatics scanning approaches, is still a challenging and daunting task. The gene-annotation enrichment analysis is a promising high-throughput strategy that increases the likelihood for investigators to identify biological processes most pertinent to their study. Approximately 68 bioinformatics enrichment tools that are currently available in the community are collected in this survey. Tools are uniquely categorized into three major classes, according to their underlying enrichment algorithms. The comprehensive collections, unique tool classifications and associated questions/issues will provide a more comprehensive and up-to-date view regarding the advantages, pitfalls and recent trends in a simpler tool-class level rather than by a tool-by-tool approach. Thus, the survey will help tool designers/developers and experienced end users understand the underlying algorithms and pertinent details of particular tool categories/tools, enabling them to make the best choices for their particular research interests.
            • Record: found
            • Abstract: found
            • Article: found
            Is Open Access

            The STRING database in 2017: quality-controlled protein–protein association networks, made broadly accessible

            A system-wide understanding of cellular function requires knowledge of all functional interactions between the expressed proteins. The STRING database aims to collect and integrate this information, by consolidating known and predicted protein–protein association data for a large number of organisms. The associations in STRING include direct (physical) interactions, as well as indirect (functional) interactions, as long as both are specific and biologically meaningful. Apart from collecting and reassessing available experimental data on protein–protein interactions, and importing known pathways and protein complexes from curated databases, interaction predictions are derived from the following sources: (i) systematic co-expression analysis, (ii) detection of shared selective signals across genomes, (iii) automated text-mining of the scientific literature and (iv) computational transfer of interaction knowledge between organisms based on gene orthology. In the latest version 10.5 of STRING, the biggest changes are concerned with data dissemination: the web frontend has been completely redesigned to reduce dependency on outdated browser technologies, and the database can now also be queried from inside the popular Cytoscape software framework. Further improvements include automated background analysis of user inputs for functional enrichments, and streamlined download options. The STRING resource is available online, at http://string-db.org/.
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              Cytoscape stringApp: Network analysis and visualization of proteomics data

              Protein networks have become a popular tool for analyzing and visualizing the often long lists of proteins or genes obtained from proteomics and other high-throughput technologies. One of the most popular sources of such networks is the STRING database, which provides protein networks for more than 2000 organisms, including both physical interactions from experimental data and functional associations from curated pathways, automatic text mining, and prediction methods. However, its web interface is mainly intended for inspection of small networks and their underlying evidence. The Cytoscape software, on the other hand, is much better suited for working with large networks and offers greater flexibility in terms of network analysis, import, and visualization of additional data. To include both resources in the same workflow, we created stringApp, a Cytoscape app that makes it easy to import STRING networks into Cytoscape, retains the appearance and many of the features of STRING, and integrates data from associated databases. Here, we introduce many of the stringApp features and show how they can be used to carry out complex network analysis and visualization tasks on a typical proteomics data set, all through the Cytoscape user interface. stringApp is freely available from the Cytoscape app store: http://apps.cytoscape.org/apps/stringapp .

                Author and article information

                Journal
                Asian Pac J Cancer Prev
                Asian Pac J Cancer Prev
                APJCP
                Asian Pacific Journal of Cancer Prevention : APJCP
                West Asia Organization for Cancer Prevention (Iran )
                1513-7368
                2476-762X
                July 2022
                : 23
                : 7
                : 2341-2350
                Affiliations
                [1 ] Laboratory of Macromolecular Engineering, Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Universitas Gadjah Mada Sekip Utara II, 55281 Yogyakarta, Indonesia.
                [2 ] Cancer Chemoprevention Research Center, Faculty of Pharmacy, Universitas Gadjah Mada Sekip Utara II, 55281 Yogyakarta, Indonesia.
                Author notes
                [* ]For Correspondence: adam_apt@ugm.ac.id
                Article
                10.31557/APJCP.2022.23.7.2341
                9727350
                35901340
                3951539f-d546-4c7a-954e-38273068f307

                This work is licensed under a Creative Commons Attribution-Non Commercial 4.0 International License. https://creativecommons.org/licenses/by-nc/4.0/

                History
                : 8 December 2021
                : 18 July 2022
                Categories
                Research Article

                breast cancer,tamoxifen resistance,glycyrrhizic acid,bioinformatics

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