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Role of Estrogen Receptor Beta in Monitoring Hormone-Responsive Breast Cancer Cells by Weighing Gene Fusion Process

DOI: 10.4236/ajmb.2025.152014, PP. 185-211

Keywords: Hormone-Responsive MCF7 BC, Gene Fusion, Estradiol (E2), Estrogen Nuclear Receptor α and β (ERα and ERβ)

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Abstract:

One of the most common cancers is Breast Cancer (BC), representing a worldwide public health concern. We previously showed estrogen receptor β (ERβ) oncosuppressor activity as significantly affected estrogen-induced early transcription and mRNA splicing in hormone-responsive MCF-7 human BC cell models. Since gene fusion phenomena are recurrent in cancer cells, we performed a transcriptomic analysis (RNAseq) aiming to assess gene fusion events in MCF-7 human BC cell models that expressed estrogen nuclear receptors α/β under early estradiol (E2) stimulation. The genomic reads sequences were aligned on GRCh38 human genome by using RNA STAR, while the star-fusion was used to detect gene fusion events. Results showed a non-significant variability regarding gene fusion events happening between estradiol-stimulated MCF-7E expressing ERβ (ERβ+/ERα?) and non-stimulated MCF-7noE repressing ERβ expression (ERβ?/ERα+), MCF-7 human BC cell models (p > 0.05). Commonly detected gene fusion events between these two (2) BC cell models result in biomarkers of several cancers and as well BC, and are characterized by intra-chromosomal interactions. Findings revealed five (5) gene fusion events specific to MCF-7noE BC cell models in which ERβ gene expression is repressed (ERβ?/ERα+), and recognized as breast cancer biomarkers. Interestingly, results exhibited ERβ expression as inhibiting gene fusion phenomena specific to BC (BC biomarkers) in replying to estradiol (E2) stimulus in monitoring early hormone-responsive MCF-7 human BC cell models (ERβ+/ERα?; MCF-7E). Overall, even if early estrogen hormone stimulation by inducing nuclear ERβ has non-significant impact on gene fusion variability between MCF-7noE (ERβ?/ERα+) and MCF-7E (ERβ+/ERα?) BC cell line models by contrast to the alternative splicing event, our study highlighted onco-suppressor activity of ERβ in hormone-responsive BC cell line model by potentially silencing several gene fusion expression recognized as BC biomarkers. However, further investigation is necessary to comprehend how ERβ monitors gene fusion in BC.

References

[1]  Mitelman, F., Johansson, B. and Mertens, F. (2007) The Impact of Translocations and Gene Fusions on Cancer Causation. Nature Reviews Cancer, 7, 233-245.
https://doi.org/10.1038/nrc2091
[2]  Futreal, P.A., Coin, L., Marshall, M., Down, T., Hubbard, T., Wooster, R., et al. (2004) A Census of Human Cancer Genes. Nature Reviews Cancer, 4, 177-183.
https://doi.org/10.1038/nrc1299
[3]  Geyer, F.C., Marchiò, C. and Reis-Filho, J.S. (2009) The Role of Molecular Analysis in Breast Cancer. Pathology, 41, 77-88.
https://doi.org/10.1080/00313020802563536
[4]  Weigelt, B. and Reis-Filho, J.S. (2009) Histological and Molecular Types of Breast Cancer: Is There a Unifying Taxonomy? Nature Reviews Clinical Oncology, 6, 718-730.
https://doi.org/10.1038/nrclinonc.2009.166
[5]  Stingl, J. and Caldas, C. (2007) Molecular Heterogeneity of Breast Carcinomas and the Cancer Stem Cell Hypothesis. Nature Reviews Cancer, 7, 791-799.
https://doi.org/10.1038/nrc2212
[6]  Weigelt, B., Geyer, F.C. and Reis-Filho, J.S. (2010) Histological Types of Breast Cancer: How Special Are They? Molecular Oncology, 4, 192-208.
https://doi.org/10.1016/j.molonc.2010.04.004
[7]  Dago, D.N., Scafoglio, C., Rinaldi, A., Memoli, D., Giurato, G., Nassa, G., et al. (2015) Estrogen Receptor Beta Impacts Hormone-Induced Alternative mRNA Splicing in Breast Cancer Cells. BMC Genomics, 16, Article No. 367.
https://doi.org/10.1186/s12864-015-1541-1
[8]  Bauer, K.R., Brown, M., Cress, R.D., Parise, C.A. and Caggiano, V. (2007) Descriptive Analysis of Estrogen Receptor (ER)-Negative, Progesterone Receptor (PR)-Negative, and HER2-Negative Invasive Breast Cancer, the So-Called Triple-Negative Phenotype: A Population-Based Study from the California Cancer Registry. Cancer, 109, 1721-1728.
https://doi.org/10.1002/cncr.22618
[9]  Fimereli, D., Fumagalli, D., Brown, D., Gacquer, D., Rothé, F., Salgado, R., et al. (2018) Genomic Hotspots but Few Recurrent Fusion Genes in Breast Cancer. Genes, Chromosomes and Cancer, 57, 331-338.
https://doi.org/10.1002/gcc.22533
[10]  Noel, D.D., Nafan, D., Inza, J.F., Jean-Luc, A.M., Hermann-Desire, L., Didier, M.Y.S., et al. (2017) DEXseq and Cuffdiff Approaches Weighing Differential Spliced Genes Exons Modulation in Estrogen Receptor (Erβ) Breast Cancer Cells. African Journal of Biotechnology, 16, 1404-1427.
https://doi.org/10.5897/ajb2016.15860
[11]  Cotrim, C.Z., Fabris, V., Doria, M.L., Lindberg, K., Gustafsson, J., Amado, F., et al. (2012) Estrogen Receptor β Growth-Inhibitory Effects Are Repressed through Activation of MAPK and PI3K Signalling in Mammary Epithelial and Breast Cancer Cells. Oncogene, 32, 2390-2402.
https://doi.org/10.1038/onc.2012.261
[12]  Bado, I., Pham, E., Soibam, B., Nikolos, F., Gustafsson, J. and Thomas, C. (2018) Erβ Alters the Chemosensitivity of Luminal Breast Cancer Cells by Regulating P53 Function. Oncotarget, 9, 22509-22522.
https://doi.org/10.18632/oncotarget.25147
[13]  Bado, I., Nikolos, F., Rajapaksa, G., Wu, W., Castaneda, J., Krishnamurthy, S., et al. (2017) Somatic Loss of Estrogen Receptor β and p53 Synergize to Induce Breast Tumorigenesis. Breast Cancer Research, 19, Article No. 79.
https://doi.org/10.1186/s13058-017-0872-z
[14]  Hall, J.M., McDonnell, D.P. and Korach, K.S. (2002) Allosteric Regulation of Estrogen Receptor Structure, Function, and Coactivator Recruitment by Different Estrogen Response Elements. Molecular Endocrinology, 16, 469-486.
https://doi.org/10.1210/mend.16.3.0814
[15]  Hawse, J.R., Carter, J.M., Aspros, K.G.M., Bruinsma, E.S., Koepplin, J.W., Negron, V., et al. (2019) Optimized Immunohistochemical Detection of Estrogen Receptor Beta Using Two Validated Monoclonal Antibodies Confirms Its Expression in Normal and Malignant Breast Tissues. Breast Cancer Research and Treatment, 179, 241-249.
https://doi.org/10.1007/s10549-019-05441-3
[16]  Lattrich, C., Stegerer, A., Häring, J., Schüler, S., Ortmann, O. and Treeck, O. (2013) Estrogen Receptor β Agonists Affect Growth and Gene Expression of Human Breast Cancer Cell Lines. Steroids, 78, 195-202.
https://doi.org/10.1016/j.steroids.2012.10.014
[17]  Punzi, S., Meliksetian, M., Riva, L., Marocchi, F., Pruneri, G., Criscitiello, C., et al. (2019) Development of Personalized Therapeutic Strategies by Targeting Actionable Vulnerabilities in Metastatic and Chemotherapy-Resistant Breast Cancer PDXs. Cells, 8, Article 605.
https://doi.org/10.3390/cells8060605
[18]  Hsu, J. (1996) Multiple Comparisons: Theory and Methods. Chapman and Hall/CRC.
https://doi.org/10.1201/b15074
[19]  Stevens, J.P. (2013) Intermediate Statistics: A Modern Approach. Routledge.
[20]  Dago, N., Saraka, M., Diarrassouba, N., Mori, A., Lallié, H., N’Goran, E., et al. (2017) RNA-Seq Evaluating Several Custom Microarrays Background Correction and Gene Expression Data Normalization Systems. Biotechnology Journal International, 19, 1-14.
https://doi.org/10.9734/bji/2017/36345
[21]  Noel, D.D. (2021) Normality Assessment of Several Quantitative Data Transformation Procedures. Biostatistics and Biometrics Open Access Journal, 10, 53-67.
https://doi.org/10.19080/bboaj.2021.10.555786
[22]  Treeck, O., Lattrich, C., Springwald, A. and Ortmann, O. (2009) Estrogen Receptor Beta Exerts Growth-Inhibitory Effects on Human Mammary Epithelial Cells. Breast Cancer Research and Treatment, 120, 557-565.
https://doi.org/10.1007/s10549-009-0413-2
[23]  Ma, L., Liu, Y., Geng, C., Qi, X. and Jiang, J. (2013) Estrogen Receptor Β Inhibits Estradiol-Induced Proliferation and Migration of MCF-7 Cells through Regulation of Mitofusin 2. International Journal of Oncology, 42, 1993-2000.
https://doi.org/10.3892/ijo.2013.1903
[24]  Corchete, L.A., Rojas, E.A., Alonso-López, D., De Las Rivas, J., Gutiérrez, N.C. and Burguillo, F.J. (2020) Systematic Comparison and Assessment of RNA-Seq Procedures for Gene Expression Quantitative Analysis. Scientific Reports, 10, Article No. 19737.
https://doi.org/10.1038/s41598-020-76881-x
[25]  Noel, D.D., Marinella, P., Mauro, G., Tripodi, S.I., Pin, A., Serena, A., et al. (2021) Genetic Variants Assessing Crohn’s Disease Pattern in Pediatric Inflammatory Bowel Disease Patients by a Clinical Exome Survey. Bioinformatics and Biology Insights, 15.
https://doi.org/10.1177/11779322211055285
[26]  Edwards, P.A. and Howarth, K.D. (2012) Are Breast Cancers Driven by Fusion Genes? Breast Cancer Research, 14, Article No. 303.
https://doi.org/10.1186/bcr3122
[27]  Fimereli, D., Fumagalli, D., Brown, D., Gacquer, D., Rothé, F., Salgado, R., et al. (2018) Genomic Hotspots but Few Recurrent Fusion Genes in Breast Cancer. Genes, Chromosomes and Cancer, 57, 331-338.
https://doi.org/10.1002/gcc.22533
[28]  Veeraraghavan, J., Ma, J., Hu, Y. and Wang, X. (2016) Recurrent and Pathological Gene Fusions in Breast Cancer: Current Advances in Genomic Discovery and Clinical Implications. Breast Cancer Research and Treatment, 158, 219-232.
https://doi.org/10.1007/s10549-016-3876-y
[29]  Loman, N.J., Misra, R.V., Dallman, T.J., Constantinidou, C., Gharbia, S.E., Wain, J., et al. (2012) Performance Comparison of Benchtop High-Throughput Sequencing Platforms. Nature Biotechnology, 30, 434-439.
https://doi.org/10.1038/nbt.2198
[30]  Dobin, A. and Gingeras, T.R. (2015) Mapping RNA-Seq Reads with Star. Current Protocols in Bioinformatics, 51, 11.14.1-11.14.19.
https://doi.org/10.1002/0471250953.bi1114s51
[31]  Haas, B. J., Dobin, A., Stransky, N., Li, B., Yang, X., Tickle, T., et al. (2017). STAR-Fusion: Fast and Accurate Fusion Transcript Detection from RNA-Seq. BioRxiv.
https://doi.org/10.1101/120295
[32]  Eswaran, J., Cyanam, D., Mudvari, P., Reddy, S.D.N., Pakala, S.B., Nair, S.S., et al. (2012) Transcriptomic Landscape of Breast Cancers through mRNA Sequencing. Scientific Reports, 2, Article No. 264.
https://doi.org/10.1038/srep00264
[33]  Noel, D.D., Alberto, F., Luciano, X., Antonio, M., Massimo, D. and Giovanni, M. (2016) Heterogeneity of Global Gene Expression Microarray Designs in Detecting Differentially Expressed Genes. International Journal of Bioinformatics Research, 7, 349-357.
[34]  Remoué, A., Conan-Charlet, V., Le Flahec, G., Lambros, L., Marcorelles, P. and Uguen, A. (2020) Un cancer mammaire présentant un réarrangement de NTRK1. Annales de Pathologie, 40, 42-45.
https://doi.org/10.1016/j.annpat.2019.05.009
[35]  Rouault, A. (2013) Etude génomique des cancers du sein familiaux liés à une mutation constitutionnelle du gène BRCA2. Doctorat Thesis, Bordeaux 2 University.
[36]  Saha Roy, S. and Vadlamudi, R.K. (2012) Role of Estrogen Receptor Signaling in Breast Cancer Metastasis. International Journal of Breast Cancer, 2012, Article ID: 654698.
https://doi.org/10.1155/2012/654698
[37]  Yue, W., Wang, J., Li, Y., Fan, P., Liu, G., Zhang, N., et al. (2010) Effects of Estrogen on Breast Cancer Development: Role of Estrogen Receptor Independent Mechanisms. International Journal of Cancer, 127, 1748-1757.
https://doi.org/10.1002/ijc.25207
[38]  McDonnell, D.P. and Norris, J.D. (2002) Connections and Regulation of the Human Estrogen Receptor. Science, 296, 1642-1644.
https://doi.org/10.1126/science.1071884
[39]  Anda-Jáuregui, G.D., Fresno, C., García-Cortés, D., Enríquez, J.E. and Hernández-Lemus, E. (2019) Intrachromosomal Regulation Decay in Breast Cancer. Applied Mathematics and Nonlinear Sciences, 4, 223-230.
https://doi.org/10.2478/amns.2019.1.00020
[40]  Espinal-Enríquez, J., Fresno, C., Anda-Jáuregui, G. and Hernández-Lemus, E. (2017) RNA-Seq Based Genome-Wide Analysis Reveals Loss of Inter-Chromosomal Regulation in Breast Cancer. Scientific Reports, 7, Article No. 1760.
https://doi.org/10.1038/s41598-017-01314-1
[41]  Gunn, S., Yeh, I., Lytvak, I., Tirtorahardjo, B., Dzidic, N., Zadeh, S., et al. (2010) Clinical Array-Based Karyotyping of Breast Cancer with Equivocal HER2 Status Resolves Gene Copy Number and Reveals Chromosome 17 Complexity. BMC Cancer, 10, Article No. 396.
https://doi.org/10.1186/1471-2407-10-396
[42]  Kytölä, S., Rummukainen, J., Nordgren, A., Karhu, R., Farnebo, F., Isola, J., et al. (2000) Chromosomal Alterations in 15 Breast Cancer Cell Lines by Comparative Genomic Hybridization and Spectral Karyotyping. Genes, Chromosomes and Cancer, 28, 308-317.
https://doi.org/10.1002/1098-2264(200007)28:3<308::aid-gcc9>3.0.co;2-b
[43]  Marchiò, C., Lambros, M.B., Gugliotta, P., Di Cantogno, L.V., Botta, C., Pasini, B., et al. (2009) Does Chromosome 17 Centromere Copy Number Predict Polysomy in Breast Cancer? A Fluorescence in Situ Hybridization and Microarray-Based CGH Analysis. The Journal of Pathology, 219, 16-24.
https://doi.org/10.1002/path.2574
[44]  Kim, P., Jang, Y.E. and Lee, S. (2019) FusionScan: Accurate Prediction of Fusion Genes from RNA-Seq Data. Genomics & Informatics, 17, e26.
https://doi.org/10.5808/gi.2019.17.3.e26
[45]  Fotakis, G., Rieder, D., Haider, M., Trajanoski, Z. and Finotello, F. (2019) NeoFuse: Predicting Fusion Neoantigens from RNA Sequencing Data. Bioinformatics, 36, 2260-2261.
https://doi.org/10.1093/bioinformatics/btz879
[46]  Zhu, C., He, L., Zhou, X., Nie, X. and Gu, Y. (2015) Sulfatase 2 Promotes Breast Cancer Progression through Regulating Some Tumor-Related Factors. Oncology Reports, 35, 1318-1328.
https://doi.org/10.3892/or.2015.4525
[47]  Chen, Z., Huang, J., Feng, Y., Li, Z. and Jiang, Y. (2021) Profiling of Specific Long Non-Coding RNA Signatures Identifies ST8SIA6‐AS1 as a Novel Target for Breast Cancer. The Journal of Gene Medicine, 23, e3286.
https://doi.org/10.1002/jgm.3286
[48]  Milne, R.L., Burwinkel, B., Michailidou, K., Arias-Perez, J.I., Zamora, M.P., Menéndez-Rodríguez, P., et al. (2014) Common Non-Synonymous SNPs Associated with Breast Cancer Susceptibility: Findings from the Breast Cancer Association Consortium. Human Molecular Genetics, 23, 6096-6111.
[49]  Zhou, Z., Qiu, R., Liu, W., Yang, T., Li, G., Huang, W., et al. (2021) BCAS3 Exhibits Oncogenic Properties by Promoting CRL4A-Mediated Ubiquitination of P53 in Breast Cancer. Cell Proliferation, 54, e13088.
https://doi.org/10.1111/cpr.13088
[50]  Bieging-Rolett, K.T., Kaiser, A.M., Morgens, D.W., Boutelle, A.M., Seoane, J.A., Van Nostrand, E.L., et al. (2020) Zmat3 Is a Key Splicing Regulator in the p53 Tumor Suppression Program. Molecular Cell, 80, 452-469.e9.
https://doi.org/10.1016/j.molcel.2020.10.022
[51]  Asmann, Y.W., Hossain, A., Necela, B.M., Middha, S., Kalari, K.R., Sun, Z., et al. (2011) A Novel Bioinformatics Pipeline for Identification and Characterization of Fusion Transcripts in Breast Cancer and Normal Cell Lines. Nucleic Acids Research, 39, e100-e100.
https://doi.org/10.1093/nar/gkr362
[52]  Dupont, V. (2011) Signalisation par les récepteurs des œstrogènes: Mécanismes de reconnaissance de l’ADN et nouvelles approches pharmacologiques d’inhibition. Master’s Thesis, Université de Montréal.
[53]  Shi, X., Singh, S., Lin, E. and Li, H. (2021) Chimeric RNAs in Cancer. Advances in Clinical Chemistry, 100, 1-35.
https://doi.org/10.1016/bs.acc.2020.04.001

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