1
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      β-Carboline dimers inhibit the tumor proliferation by the cell cycle arrest of sarcoma through intercalating to Cyclin-A2

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          β-Carbolines are potentially strong alkaloids with a wide range of bioactivities, and their dimers exhibit stronger antitumor activity other than the monomers. However, the detailed mechanisms of the β-carboline dimers in inhibiting sarcoma (SARC) remain unclear. The results showed that β-carboline-3-carboxylic acid dimers Comp1 and Comp2, which were synthesized in our lab and modified at the N 9 position and linked at the C 3 position, exhibited effective inhibition activity on MG-63 proliferation (IC 50 = 4.6μM). Meanwhile, the large scale transcriptome profiles of SARC from The Cancer Genome Atlas (TCGA) were analyzed, and found that abnormal expression of genes relevant to apoptosis, cell cycle, and signaling pathways of Hedgehog, HIF, Ras involved in the SARC pathogenesis. Interestingly, both dimers could promote the apoptosis and arrest the cell cycle in S phase to inhibit proliferation of MG-63. Moreover, Comp1 and Comp2 inhibited the expression CDK2, CCNA2, DBF4, and PLK1 associated with various immune cells and cell cycle in MG-63. Remarkably, drug-target interaction network analysis showed that numerous proteins involved in cell cycle were the potential targets of Comp1 and Comp2, especially CCNA2. Further molecular docking, isothermal titration calorimetry (ITC) and Cellular Thermal Shift Assay (CETSA) confirmed that both dimers could directly interact with CCNA2, which is significantly correlated with CD4+ T cells, by strong hydrophobic interactions (K d=5.821 ×10 6 N). Meanwhile, the levels of CCNA2 and CDK2 were inhibited to decrease in MG-63 by both dimer treatments at transcription and protein levels, implying that Comp1 and Comp2 blocked the interaction between CCNA2 and CDK2 through competitive binding with CCNA2 to arrest the cell cycle of MG-63 cells in the S phase. Additionally, the transcriptome profiles of β-carboline-treated mice from Gene Expression Omnibus (GEO) were obtained, and found that similar antitumor mechanism was shared among β-carboline derivatives. Overall, our results elucidated the antitumor mechanisms of Comp1 and Comp2 through dual-suppressing the function of CCNA2 to profoundly arrest cell cycle of MG-63, then effectively inhibited cell proliferation of MG-63. These results provide new insights into the antitumor mechanism of β-carboline dimers and new routes of various novel cancer-related drug targets for future possible cancer therapy.

          Related collections

          Most cited references50

          • Record: found
          • Abstract: found
          • Article: not found

          Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

          Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: found
            Is Open Access

            GEPIA: a web server for cancer and normal gene expression profiling and interactive analyses

            Abstract Tremendous amount of RNA sequencing data have been produced by large consortium projects such as TCGA and GTEx, creating new opportunities for data mining and deeper understanding of gene functions. While certain existing web servers are valuable and widely used, many expression analysis functions needed by experimental biologists are still not adequately addressed by these tools. We introduce GEPIA (Gene Expression Profiling Interactive Analysis), a web-based tool to deliver fast and customizable functionalities based on TCGA and GTEx data. GEPIA provides key interactive and customizable functions including differential expression analysis, profiling plotting, correlation analysis, patient survival analysis, similar gene detection and dimensionality reduction analysis. The comprehensive expression analyses with simple clicking through GEPIA greatly facilitate data mining in wide research areas, scientific discussion and the therapeutic discovery process. GEPIA fills in the gap between cancer genomics big data and the delivery of integrated information to end users, thus helping unleash the value of the current data resources. GEPIA is available at http://gepia.cancer-pku.cn/.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              STRING v10: protein–protein interaction networks, integrated over the tree of life

              The many functional partnerships and interactions that occur between proteins are at the core of cellular processing and their systematic characterization helps to provide context in molecular systems biology. However, known and predicted interactions are scattered over multiple resources, and the available data exhibit notable differences in terms of quality and completeness. The STRING database (http://string-db.org) aims to provide a critical assessment and integration of protein–protein interactions, including direct (physical) as well as indirect (functional) associations. The new version 10.0 of STRING covers more than 2000 organisms, which has necessitated novel, scalable algorithms for transferring interaction information between organisms. For this purpose, we have introduced hierarchical and self-consistent orthology annotations for all interacting proteins, grouping the proteins into families at various levels of phylogenetic resolution. Further improvements in version 10.0 include a completely redesigned prediction pipeline for inferring protein–protein associations from co-expression data, an API interface for the R computing environment and improved statistical analysis for enrichment tests in user-provided networks.
                Bookmark

                Author and article information

                Contributors
                Journal
                Front Immunol
                Front Immunol
                Front. Immunol.
                Frontiers in Immunology
                Frontiers Media S.A.
                1664-3224
                17 October 2022
                2022
                : 13
                : 922183
                Affiliations
                [1] 1 College of Chemistry and Pharmacy, Northwest A&F University , Yangling, China
                [2] 2 College of Plant Protection, Northwest A&F University , Yangling, China
                [3] 3 Instrumental Analysis Center, Xi’an Jiaotong University , Xi’an, China
                Author notes

                Edited by: Joao P.B. Viola, National Cancer Institute (INCA), Brazil

                Reviewed by: Ganesan Ramamoorthi, Moffitt Cancer Center, United States; Guan-Jun Yang, Ningbo University, China

                This article was submitted to Cancer Immunity and Immunotherapy, a section of the journal Frontiers in Immunology

                Article
                10.3389/fimmu.2022.922183
                9618858
                36325324
                6825026d-09f8-401c-906f-d13b7c58af7e
                Copyright © 2022 Ma, Yu, Li, Cao, Du, Dai, Zhi, Xu, Li and Wang

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 17 April 2022
                : 30 September 2022
                Page count
                Figures: 8, Tables: 2, Equations: 0, References: 50, Pages: 17, Words: 7834
                Categories
                Immunology
                Original Research

                Immunology
                β-carboline-3-carboxylic acid dimers,ccna2,cdk2,cell cycle,apoptosis,tumor-infiltrating cell

                Comments

                Comment on this article