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  • Wang J Zhao P Hoi

    2019-10-29

    [17] Wang J, Zhao P, Hoi SC, Jin R. Online feature selection and its applications. IEEE Trans Knowl Data Eng 2014;26(3):698–710. [18] Bolón-Canedo V, Sánchez-Marono N, Alonso-Betanzos A, Benítez JM, Herrera F. A review of microarray datasets and applied feature selection methods. Inf Sci 2014;282:111–35. [19] Hira ZM, Gillies DF. A review of feature selection and feature extraction methods applied on microarray data. Advances in bioinformatics, 2015;2015:198363. 13 pages [1-13]. [20] Uysal AK, Gunal S. A novel probabilistic feature selection method for text classifi-cation. Knowl Based Syst 2012;36:226–35. [21] Zhu X, Wu X. Class noise vs. attribute noise: a quantitative study. Artif Intell Rev 2004;22(3):177–210.
    T. Bikku and R. Paturi
    [26] Nalisnick E, Mitra B, Craswell N, Caruana R. Improving document ranking with dual word embeddings April Proceedings of the 25th international conference companion on world wide webInternational World Wide Web Conferences Steering Committee; 2016. p. 83–4.
    [27] Bikku T, Nandam SR, Akepogu AR. A contemporary feature selection and classifi-cation framework for imbalanced biomedical datasets. Egyptian Informatics Journal 2018 Nov 1;19(3):191–8.
    Contents lists available at ScienceDirect
    Journal of Steroid Biochemistry and Molecular Biology
    journal homepage: www.elsevier.com/locate/jsbmb
    A novel SRC-2-dependent regulation of epithelial-mesenchymal transition in T breast cancer LY3009120
    Olivera Bozickovica,b,d,1, Linn Skartveitb,1, Agnete S.T. Engelsenc, Thomas Hellandb, Kristin Jonsdottirf, Marianne Hauglid Flågengb, Ingvild S. Fenneb, Emiel Jansseng, James B. Lorensc, Lise Bjørkhaugb,d,e, Jørn V. Sagena,b,d, Gunnar Mellgrena,b,d,
    a Department of Clinical Science, University of Bergen, N-5021 Bergen, Norway
    b Hormone Laboratory, Haukeland University Hospital, N-5021 Bergen, Norway
    c Centre for Cancer Biomarkers (CCBIO), Department of Biomedicine, University of Bergen, N-5009 Bergen, Norway
    d KG Jebsen Center for Diabetes Research, Department of Clinical Science, University of Bergen, N-5021 Bergen, Norway r> e Department of Biomedical Laboratory Sciences, Western Norway University of Applied Sciences, N-5020 Bergen, Norway
    f Department of Pathology, Stavanger University Hospital, N-4068 Stavanger, Norway
    g Department of Mathematics and Natural Sciences, University of Stavanger, N-4036 Stavanger, Norway
    Keywords:
    Coactivator
    Breast cancer
    EMT
    Lyn 
    Steroid receptor coactivator 2 (SRC-2) is a nuclear receptor coactivator, important for the regulation of estrogen receptor alpha (ERα)-mediated transcriptional activity in breast cancer cells. However, the transcriptional role of SRC-2 in breast cancer is still ambiguous. Here we aimed to unravel a more precise transcriptional role of SRC-2 and uncover unique target genes in MCF-7 breast cancer cells, as opposed to the known oncogene SRC-3. Gene expression analyses of cells depleted of either SRC-2 or SRC-3 showed that they transcriptionally regulate mostly separate gene sets. However, individual unique gene sets were implicated in some of the same major gene ontology biological processes, such as cellular structure and development. This finding was supported by three-dimensional cell cultures, demonstrating that depletion of SRC-2 and SRC-3 changed the morphology of the cells into epithelial-like hollow acinar structures, indicating that both SRC proteins are involved in maintaining the hybrid E/M phenotype. In clinical ER-positive, HER2-negative breast cancer samples the expression of SRC-2 was negatively correlated with the expression of MCF-7-related luminal, cell cycle and cellular morphogenesis genes. Finally, elucidating SRC-2 unique transcriptional effects, we identified Lyn kinase (an EMT biomarker) to be upregulated exclusively after SRC-2 depletion. In conclusion, we LY3009120 show that both SRC-2 and SRC-3 are es-sential for the EMT in breast cancer cells, controlling different transcriptional niches.