2
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Inhibition of high level E2F in a RB1 proficient MYCN overexpressing chicken retinoblastoma model normalizes neoplastic behaviour

      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

          Purpose

          Retinoblastoma, a childhood cancer, is most frequently caused by bi-allelic inactivation of RB1 gene. However, other oncogenic mutations such as MYCN amplification can induce retinoblastoma with proficient RB1. Previously, we established RB1-proficient MYCN-overexpressing retinoblastoma models both in human organoids and chicken. Here, we investigate the regulatory events in MYCN-induced retinoblastoma carcinogenesis based on the model in chicken.

          Methods

          MYCN transformed retinal cells in culture were obtained from in vivo MYCN electroporated chicken embryo retina. The expression profiles were analysed by RNA sequencing. Chemical treatments, qRT-PCR, flow cytometry, immunohisto- and immunocytochemistry and western blot were applied to study the properties and function of these cells.

          Results

          The expression profile of MYCN-transformed retinal cells in culture showed cone photoreceptor progenitor signature and robustly increased levels of E2Fs. This expression profile was consistently observed in long-term culture. Chemical treatments confirmed RB1 proficiency in these cells. The cells were insensitive to p53 activation but inhibition of E2f efficiently induced cell cycle arrest followed by apoptosis.

          Conclusion

          In conclusion, with proficient RB1, MYCN-induced high level of E2F expression dysregulates the cell cycle and contributes to retinoblastoma carcinogenesis. The increased level of E2f renders the cells to adopt a similar mechanistic phenotype to a RB1-deficient tumour.

          Supplementary information

          The online version contains supplementary material available at 10.1007/s13402-023-00863-0.

          Related collections

          Most cited references56

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

          clusterProfiler: an R package for comparing biological themes among gene clusters.

          Increasing quantitative data generated from transcriptomics and proteomics require integrative strategies for analysis. Here, we present an R package, clusterProfiler that automates the process of biological-term classification and the enrichment analysis of gene clusters. The analysis module and visualization module were combined into a reusable workflow. Currently, clusterProfiler supports three species, including humans, mice, and yeast. Methods provided in this package can be easily extended to other species and ontologies. The clusterProfiler package is released under Artistic-2.0 License within Bioconductor project. The source code and vignette are freely available at http://bioconductor.org/packages/release/bioc/html/clusterProfiler.html.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: found
            Is Open Access

            edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

            Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data. Availability: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org). Contact: mrobinson@wehi.edu.au
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              featureCounts: an efficient general purpose program for assigning sequence reads to genomic features.

              Next-generation sequencing technologies generate millions of short sequence reads, which are usually aligned to a reference genome. In many applications, the key information required for downstream analysis is the number of reads mapping to each genomic feature, for example to each exon or each gene. The process of counting reads is called read summarization. Read summarization is required for a great variety of genomic analyses but has so far received relatively little attention in the literature. We present featureCounts, a read summarization program suitable for counting reads generated from either RNA or genomic DNA sequencing experiments. featureCounts implements highly efficient chromosome hashing and feature blocking techniques. It is considerably faster than existing methods (by an order of magnitude for gene-level summarization) and requires far less computer memory. It works with either single or paired-end reads and provides a wide range of options appropriate for different sequencing applications. featureCounts is available under GNU General Public License as part of the Subread (http://subread.sourceforge.net) or Rsubread (http://www.bioconductor.org) software packages.
                Bookmark

                Author and article information

                Contributors
                Finn.Hallbook@igp.uu.se
                Journal
                Cell Oncol (Dordr)
                Cell Oncol (Dordr)
                Cellular Oncology (Dordrecht)
                Springer Netherlands (Dordrecht )
                2211-3428
                2211-3436
                22 August 2023
                22 August 2023
                2024
                : 47
                : 1
                : 209-227
                Affiliations
                [1 ]Department of Immunology, Genetics and Pathology, Uppsala University, ( https://ror.org/048a87296) Uppsala, Sweden
                [2 ]GRID grid.8993.b, ISNI 0000 0004 1936 9457, National Bioinformatics Infrastructure Sweden, Science for Life Laboratory, , Uppsala University, ; Uppsala, Sweden
                [3 ]Department of Medical Biochemistry and Microbiology, Uppsala University, ( https://ror.org/048a87296) Uppsala, Sweden
                [4 ]Department of Immunology, Genetics and Pathology, Rudbeck Laboratory, Uppsala University, ( https://ror.org/048a87296) 751 85 Uppsala, Sweden
                Article
                863
                10.1007/s13402-023-00863-0
                10899388
                37606819
                1170e4e9-8e89-4f01-b734-ca851ac217f1
                © The Author(s) 2023

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 7 August 2023
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100010810, Ögonfonden;
                Funded by: FundRef http://dx.doi.org/10.13039/501100006313, Barncancerfonden;
                Award ID: PR2019-0068
                Award ID: PR2019-0068
                Award ID: PR2019-0068
                Award ID: PR2019-0068
                Award ID: PR2019-0068
                Funded by: ARMEC Lindebergs foundation
                Award ID: 2023-3
                Award ID: 2023-3
                Award ID: 2023-3
                Award ID: 2023-3
                Funded by: FundRef http://dx.doi.org/10.13039/501100003792, Hjärnfonden;
                Award ID: FO2020-0291
                Award ID: FO2020-0291
                Award ID: FO2020-0291
                Award ID: FO2020-0291
                Funded by: FundRef http://dx.doi.org/10.13039/100010817, Stiftelsen Kronprinsessan Margaretas Arbetsnämnd för Synskadade;
                Award ID: 2022-029, 2021-040
                Award ID: 2022-029, 2021-040
                Award ID: 2022-029, 2021-040
                Award ID: 2022-029, 2021-040
                Funded by: Uppsala University
                Categories
                Research
                Custom metadata
                © Springer Nature Switzerland AG 2024

                Oncology & Radiotherapy
                animal model,chicken,e2f,intraocular cancer,mycn,rb1 proficient,retinoblastoma
                Oncology & Radiotherapy
                animal model, chicken, e2f, intraocular cancer, mycn, rb1 proficient, retinoblastoma

                Comments

                Comment on this article