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Current Pharmaceutical Design

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ISSN (Print): 1381-6128
ISSN (Online): 1873-4286

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Research Article

A Hidden Human Proteome Signature Characterizes the Epithelial Mesenchymal Transition Program

Author(s): Daniele Vergara, Tiziano Verri, Marina Damato, ">Marco Trerotola, ">Pasquale Simeone, Julien Franck, Isabelle Fournier, Michel Salzet* and Michele Maffia*

Volume 26, Issue 3, 2020

Page: [372 - 375] Pages: 4

DOI: 10.2174/1381612826666200129091610

Price: $65

Abstract

Background: Molecular changes associated with the initiation of the epithelial to mesenchymal transition (EMT) program involve alterations of large proteome-based networks. The role of protein products mapping to non-coding genomic regions is still unexplored.

Objective: The goal of this study was the identification of an alternative protein signature in breast cancer cellular models with a distinct expression of EMT markers.

Methods: We profiled MCF-7 and MDA-MB-231 cells using liquid-chromatography mass/spectrometry (LCMS/ MS) and interrogated the OpenProt database to identify novel predicted isoforms and novel predicted proteins from alternative open reading frames (AltProts).

Results: Our analysis revealed an AltProt and isoform protein signature capable of classifying the two breast cancer cell lines. Among the most highly expressed alternative proteins, we observed proteins potentially associated with inflammation, metabolism and EMT.

Conclusion: Here, we present an AltProts signature associated with EMT. Further studies will be needed to define their role in cancer progression.

Keywords: Epithelial mesenchymal transition, proteome, alternative proteins, breast cancer, OpenProt database, predicted isoforms.

« Previous Next »
[1]
Giudetti AM, De Domenico S, Ragusa A, et al. A specific lipid metabolic profile is associated with the epithelial mesenchymal transition program. Biochim Biophys Acta Mol Cell Biol Lipids 2019; 1864(3): 344-57.
[http://dx.doi.org/10.1016/j.bbalip.2018.12.011] [PMID: 30578966]
[2]
Vergara D, Romano A, Stanca E, et al. Proteomic expression profile of injured rat peripheral nerves revealed biological networks and processes associated with nerve regeneration. J Cell Physiol 2018; 233(8): 6207-23.
[http://dx.doi.org/10.1002/jcp.26478] [PMID: 29327509]
[3]
Frese CK, Mikhaylova M, Stucchi R, et al. Quantitative map of proteome dynamics during neuronal differentiation. Cell Rep 2017; 18(6): 1527-42.
[http://dx.doi.org/10.1016/j.celrep.2017.01.025] [PMID: 28178528]
[4]
Stefania D, Vergara D. The Many-faced program of epithelial-mesenchymal transition: a system biology-based view. Front Oncol 2017; 7: 274.
[http://dx.doi.org/10.3389/fonc.2017.00274] [PMID: 29181337]
[5]
Bhowmik SK, Ramirez-Peña E, Arnold JM, et al. EMT-induced metabolite signature identifies poor clinical outcome. Oncotarget 2015; 6(40): 42651-60.
[http://dx.doi.org/10.18632/oncotarget.4765] [PMID: 26315396]
[6]
Simeone P, Trerotola M, Franck J, et al. The multiverse nature of epithelial to mesenchymal transition. Semin Cancer Biol 2018; pii: 58: 1-10.
[7]
Vergara D, Stanca E, Guerra F, et al. β-Catenin knockdown affects mitochondrial biogenesis and lipid metabolism in breast cancer cells. Front Physiol 2017; 8: 544.
[http://dx.doi.org/10.3389/fphys.2017.00544] [PMID: 28798698]
[8]
Vergara D, Simeone P, Latorre D, et al. Proteomics analysis of E-cadherin knockdown in epithelial breast cancer cells. J Biotechnol 2015; 202: 3-11.
[http://dx.doi.org/10.1016/j.jbiotec.2014.10.034] [PMID: 25449012]
[9]
Vaklavas C, Blume SW, Grizzle WE. Translational dysregulation in cancer: molecular insights and potential clinical applications in biomarker development. Front Oncol 2017; 7: 158.
[http://dx.doi.org/10.3389/fonc.2017.00158] [PMID: 28798901]
[10]
Couso JP, Patraquim P. Classification and function of small open reading frames. Nat Rev Mol Cell Biol 2017; 18(9): 575-89.
[http://dx.doi.org/10.1038/nrm.2017.58] [PMID: 28698598]
[11]
Brunet MA, Levesque SA, Hunting DJ, Cohen AA, Roucou X. Recognition of the polycistronic nature of human genes is critical to understanding the genotype-phenotype relationship. Genome Res 2018; 28(5): 609-24.
[http://dx.doi.org/10.1101/gr.230938.117] [PMID: 29626081]
[12]
Delcourt V, Franck J, Quanico J, et al. Spatially-resolved top-down proteomics bridged to MALDI MS imaging reveals the molecular physiome of brain regions. Mol Cell Proteomics 2018; 17(2): 357-72.
[http://dx.doi.org/10.1074/mcp.M116.065755] [PMID: 29122912]
[13]
Delcourt V, Franck J, Leblanc E, et al. Combined mass spectrometry imaging and top-down microproteomics reveals evidence of a hidden proteome in ovarian cancer. EBioMedicine 2017; 21: 55-64.
[http://dx.doi.org/10.1016/j.ebiom.2017.06.001] [PMID: 28629911]
[14]
Delcourt V, Brunelle M, Roy AV, et al. The protein coded by a short open reading frame, not by the annotated coding sequence, is the main gene product of the dual-coding gene MIEF1. Mol Cell Proteomics 2018; 17(12): 2402-11.
[http://dx.doi.org/10.1074/mcp.RA118.000593] [PMID: 30181344]
[15]
Brunet MA, Brunelle M, Lucier JF, et al. OpenProt: a more comprehensive guide to explore eukaryotic coding potential and proteomes. Nucleic Acids Res 2019; 47(D1): D403-10.
[PMID: 30299502]
[16]
Perron U, Provero P, Molineris I. In silico prediction of lncRNA function using tissue specific and evolutionary conserved expression. BMC Bioinformatics 2017; 18(Suppl. 5): 144.
[http://dx.doi.org/10.1186/s12859-017-1535-x] [PMID: 28361701]
[17]
Tang Z, Li C, Kang B, Gao G, Li C, Zhang Z. GEPIA: a web server for cancer and normal gene expression profiling and interactive analyses. Nucleic Acids Res 2017; 45(W1) W98-102
[http://dx.doi.org/10.1093/nar/gkx247] [PMID: 28407145]
[18]
Jézéquel P, Campone M, Gouraud W, et al. bc-GenExMiner: an easy-to-use online platform for gene prognostic analyses in breast cancer. Breast Cancer Res Treat 2012; 131(3): 765-75.
[http://dx.doi.org/10.1007/s10549-011-1457-7] [PMID: 21452023]
[19]
Fishilevich S, Nudel R, Rappaport N, et al. GeneHancer: genome-wide integration of enhancers and target genes in GeneCards. Database (Oxford) 2017; 2017 bax028
[http://dx.doi.org/10.1093/database/bax028] [PMID: 28605766]
[20]
Meyer-Schaller N, Cardner M, Diepenbruck M, et al. A hierarchical regulatory landscape during the multiple stages of EMT. Dev Cell 2019; 48(4): 539-553.e6.
[http://dx.doi.org/10.1016/j.devcel.2018.12.023] [PMID: 30713070]
[21]
De Craene B, Berx G. Regulatory networks defining EMT during cancer initiation and progression. Nat Rev Cancer 2013; 13(2): 97-110.
[http://dx.doi.org/10.1038/nrc3447] [PMID: 23344542]
[22]
Anastasiadou E, Jacob LS, Slack FJ. Non-coding RNA networks in cancer. Nat Rev Cancer 2018; 18(1): 5-18.
[http://dx.doi.org/10.1038/nrc.2017.99] [PMID: 29170536]
[23]
Johansson HJ, Socciarelli F, Vacanti NM, et al. Consortia Oslo Breast Cancer Research Consortium (OSBREAC). Breast cancer quantitative proteome and proteogenomic landscape. Nat Commun 2019; 10(1): 1600.
[http://dx.doi.org/10.1038/s41467-019-09018-y] [PMID: 30962452]

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