Applied Biochemistry and Biotechnology

, Volume 173, Issue 7, pp 1618–1639 | Cite as

Gene Expression Analysis in MCF-7 Breast Cancer Cells Treated with Recombinant Bromelain

  • Nour Fouz
  • Azura AmidEmail author
  • Yumi Zuhanis Has-Yun Hashim


The contributing molecular pathways underlying the pathogenesis of breast cancer need to be better characterized. The principle of our study was to better understand the genetic mechanism of oncogenesis for human breast cancer and to discover new possible tumor markers for use in clinical practice. We used complimentary DNA (cDNA) microarrays to compare gene expression profiles of treated Michigan Cancer Foundation-7 (MCF-7) with recombinant bromelain and untreated MCF-7. SpringGene analysis was carried out of differential expression followed by Ingenuity Pathway Analysis (IPA), to understand the underlying consequence in developing disease and disorders. We identified 1,102 known genes differentially expressed to a significant degree (p < 0.001) changed between the treatment. Within this gene set, 20 genes were significantly changed between treated cells and the control cells with cutoff fold change of more than 1.5. These genes are RNA-binding motif, single-stranded interacting protein 1 (RBMS1), ribosomal protein L29 (RPL29), glutathione S-transferase mu 2 (GSTM2), C15orf32, Akt3, B cell translocation gene 1 (BTG1), C6orf62, C7orf60, kinesin-associated protein 3 (KIFAP3), FBXO11, AT-rich interactive domain 4A (ARID4A), COPS2, TBPL1|SLC2A12, TMEM59, SNORD46, glioma tumor suppressor candidate region gene 2 (GLTSCR2), and LRRFIP. Our observation on gene expression indicated that recombinant bromelain produces a unique signature affecting different pathways, specific for each congener. The microarray results give a molecular mechanistic insight and functional effects, following recombinant bromelain treatment. The extent of changes in genes is related to and involved significantly in gap junction signaling, amyloid processing, cell cycle regulation by BTG family proteins, and breast cancer regulation by stathmin1 that play major roles.


Cancer MCF-7 Microarray Ingenuity Pathway Analysis (IPA) Bromelain 



The authors gratefully acknowledge the support from the International Islamic University Malaysia for providing research grant number EDW B11-078-0556 and the Institute of Medical Research (IMR), Kuala Lumpur, for letting the researchers use the GeneSpring software in conducting this research.


  1. 1.
    Chobotova, K., Vernallis, A. B., & Majid, F. A. A. (2010). Bromelain’s activity and potential as an anti-cancer agent: current evidence and perspectives. Cancer Letters, 290(2), 148–156.CrossRefGoogle Scholar
  2. 2.
    Berit, B., & Tysnes, H. R. M. (2001). Torsten Porwol, Beatrice Probst, Rolf Bjerkvig, Frank Hoover, Bromelain reversibly inhibits invasive properties of glioma cells. Neoplasia, 3(6), 469–479.CrossRefGoogle Scholar
  3. 3.
    Rajendra, P., Jain, S., Shraddha, A., Ajay, K. (2012). Properties and therapeutic application of bromelain: a review. Biotechnology Research International. 2012 (2012) (Article ID 976203): p. 6Google Scholar
  4. 4.
    Amid, A., et al. (2011). Expression, purification, and characterization of a recombinant stem bromelain from Ananas comosus. Process Biochemistry, 46(12), 2232–2239.CrossRefGoogle Scholar
  5. 5.
    Paul, C. (2004). Protein purification protocols. Totowa: Human press.Google Scholar
  6. 6.
    Lucia, F., & Tomas, G. V. (2010). Native and biotechnologically engineered plant proteases with industrial applications. Journal Food Bioprocess Technology, 4, 1066–1088.Google Scholar
  7. 7.
    Muntari, B., et al. (2011). Recovery of recombinant bromelain from Escherichia coli BL21-AI. African Journal of Biotechnology, 10(81), 18829–18832.Google Scholar
  8. 8.
    Dang, C., et al. (2003). Cytokinetics. In D. W. Kufe, R. E. Pollock, & R. R. Weichselbaum (Eds.), Holland-Frei cancer medicine (6th ed.). Hamilton: BC Decker.Google Scholar
  9. 9.
    Clarke, P. A., te Poele, R., & Workman, P. (2004). Gene expression microarray technologies in the development of new therapeutic agents. European Journal of Cancer, 40(17), 2560–2591.CrossRefGoogle Scholar
  10. 10.
    Bala, M., et al. (2011). Recovery of recombinant bromelain from Escherichia coli BL21-AI. African Journal of Biotechnology, 10(81), 18829–18832.CrossRefGoogle Scholar
  11. 11.
    Zar, J. H. (1999). Biostatistical analysis (4th ed.). New Jersey: Prentice Hall.Google Scholar
  12. 12.
    Ghosh, S., et al. (2011). Global gene expression and ingenuity biological functions analysis on PCBs 153 and 138 induced human PBMC in vitro reveals differential mode(s) of action in developing toxicities. Environment International, 37(5), 838–857.CrossRefGoogle Scholar
  13. 13.
    Dutta, S. K., et al. (2012). Differential gene expression and a functional analysis of PCB-exposed children: understanding disease and disorder development. Environment International, 40, 143–154.CrossRefGoogle Scholar
  14. 14.
    Erk, M. J., et al. (2005). Integrated assessment by multiple gene expression analysis of quercetin bioactivity on anticancer-related mechanisms in colon cancer cells in vitro. European Journal of Nutrition, 44(3), 143–156.CrossRefGoogle Scholar
  15. 15.
    Lin, Y.-H., et al. (2007). Multiple gene expression classifiers from different array platforms predict poor prognosis of colorectal cancer. Clinical Cancer Research, 13(2), 498–507.CrossRefGoogle Scholar
  16. 16.
    Nakamura, T., et al. (2004). Genome-wide cDNA microarray analysis of gene expression profiles in pancreatic cancers using populations of tumor cells and normal ductal epithelial cells selected for purity by laser microdissection. Oncogene, 23(13), 2385–2400.CrossRefGoogle Scholar
  17. 17.
    Hurst, D. R., et al. (2008). Alterations of BRMS1-ARID4A interaction modify gene expression but still suppress metastasis in human breast cancer cells. Journal of Biological Chemistry, 283(12), 7438–7444.CrossRefGoogle Scholar
  18. 18.
    Duisters, R. F., et al. (2009). miR-133 and miR-30 regulate connective tissue growth factor: implications for a role of MicroRNAs in myocardial matrix remodeling. Circulation Research, 104(2), 170–178.CrossRefGoogle Scholar
  19. 19.
    Yu, F., et al. (2010). Mir-30 reduction maintains self-renewal and inhibits apoptosis in breast tumor-initiating cells. Oncogene, 29(29), 4194–4204.CrossRefGoogle Scholar
  20. 20.
    Mao-De, L., & Jing, X. (2007). Ribosomal proteins and colorectal cancer. Current Genomics, 8(1), 43–49.CrossRefGoogle Scholar
  21. 21.
    Zhou, S.-G., et al. (2008). Reduced expression of GSTM2 and increased oxidative stress in spontaneously hypertensive rat. Molecular and Cellular Biochemistry, 309(1–2), 99–107.CrossRefGoogle Scholar
  22. 22.
    Kennedy, N. J., Cellurale, C., & Davis, R. J. (2007). A radical role for p38 MAPK in tumor initiation. Cancer Cell, 11(2), 101–103.CrossRefGoogle Scholar
  23. 23.
    Denti, S., et al. (2006). The COP9 signalosome regulates Skp2 levels and proliferation of human cells. Journal of Biological Chemistry, 281(43), 32188–32196.CrossRefGoogle Scholar
  24. 24.
    Kalt, I., et al. (2010). GLTSCR2/PICT-1, a putative tumor suppressor gene product, induces the nucleolar targeting of the Kaposi’s sarcoma-associated herpesvirus KS-Bcl-2 protein. Journal of Virology, 84(6), 2935–2945.CrossRefGoogle Scholar
  25. 25.
    Wu, M.-Y., Eldin, K. W., & Beaudet, A. L. (2008). Identification of chromatin remodeling genes Arid4a and Arid4b as Leukemia suppressor genes. Journal of the National Cancer Institute, 100(17), 1247–1259.CrossRefGoogle Scholar
  26. 26.
    Hirokawa, N., & Noda, Y. (2008). Intracellular transport and kinesin superfamily proteins, KIFs: structure, function, and dynamics. Physiological Reviews, 88(3), 1089–1118.CrossRefGoogle Scholar
  27. 27.
    Daire, V., & Poüs, C. (2011). Kinesins and protein kinases: key players in the regulation of microtubule dynamics and organization. Archives of Biochemistry and Biophysics, 510(2), 83–92.CrossRefGoogle Scholar
  28. 28.
    Bruno, L., Salierno, M., Wetzler, D. E., Despósito, M. A., & Levi, V. (2011). Mechanical properties of organelles driven by microtubule-dependent molecular motors in living cells. PLoS One, 6(4), e18332.CrossRefGoogle Scholar
  29. 29.
    Haraguchi, K., et al. (2006). Role of the kinesin-2 family protein, KIF3, during mitosis. Journal of Biological Chemistry, 281(7), 4094–4099.CrossRefGoogle Scholar
  30. 30.
    Park, J. J., Cawley, N. X., & Loh, Y. P. (2008). Carboxypeptidase E cytoplasmic tail-driven vesicle transport is key for activity-dependent secretion of peptide hormones. Molecular Endocrinology, 22(4), 989–1005.CrossRefGoogle Scholar
  31. 31.
    Deepthi, T., Jagadeesha, M., Harsh, P., Arivusudar, M., Manoj Kumar, K., Ramachandra, Y. L., et al. (2012). overexpression of kinesin associated protein 3 (KIFAP3) in breast cancer. Journal Proteomics Bioinformatics, 5, 122–126.Google Scholar
  32. 32.
    Blomme, E. A. G., Yang, Y., & Waring, J. F. (2009). Use of toxicogenomics to understand mechanisms of drug-induced hepatotoxicity during drug discovery and development. Toxicology Letters, 186(1), 22–31.CrossRefGoogle Scholar
  33. 33.
    Saverio, B., et al. (2000). Tumor progression is accompanied by significant changes in the levels of expression of polyamine metabolism regulatory genes and clusterin (sulfated glycoprotein 2) in human prostate cancer specimens. Cancer Research, 60(1), 28–34.Google Scholar
  34. 34.
    Frasor, J., et al. (2003). Profiling of estrogen up- and down-regulated gene expression in human breast cancer cells: insights into gene networks and pathways underlying estrogenic control of proliferation and cell phenotype. Endocrinology, 144(10), 4562–4574.CrossRefGoogle Scholar
  35. 35.
    Rouault, J., et al. (1996). Identification of BTG2, an antiproliferative p53-dependent component of the DNA damage cellular response pathway. Nature Genetics, 14, 482–486.CrossRefGoogle Scholar
  36. 36.
    Cho, I. J., et al. (2005). Repression by oxidative stress of iNOS and cytokine gene induction in macrophages results from AP-1 and NF-κB inhibition mediated by B cell translocation gene-1 activation. Free Radical Biology and Medicine, 39(11), 1523–1536.CrossRefGoogle Scholar
  37. 37.
    Hamatani, T., et al. (2004). Global gene expression analysis identifies molecular pathways distinguishing blastocyst dormancy and activation. Proceedings of the National Academy of Sciences of the United States of America, 101(28), 10326–10331.Google Scholar
  38. 38.
    Dean, J. L., et al. (2010). Therapeutic CDK4/6 inhibition in breast cancer: key mechanisms of response and failure. Oncogene, 29(28), 4018–4032.CrossRefGoogle Scholar
  39. 39.
    Nevins, J. R. (2001). The Rb/E2F pathway and cancer. Human Molecular Genetics, 10(7), 699–703.CrossRefGoogle Scholar
  40. 40.
    Whitfield, M. L., et al. (2002). Identification of genes periodically expressed in the human cell cycle and their expression in tumors. Molecular Biology of the Cell, 13(6), 1977–2000.CrossRefGoogle Scholar
  41. 41.
    Malumbres, M., & Barbacid, M. (2009). Cell cycle, CDKs and cancer: a changing paradigm. Nature Reviews Cancer, 9, 153–166.CrossRefGoogle Scholar
  42. 42.
    Kelsell, D. P., Dunlop, J., & Hodgins, M. B. (2001). Human diseases: clues to cracking the connexin code? Trends in Cell Biology, 11(1), 2–6.CrossRefGoogle Scholar
  43. 43.
    Hofer, A., & Dermietzel, R. (1998). Visualization and functional blocking of gap junction hemichannels (connexons) with antibodies against external loop domains in astrocytes. Glia, 24(1), 141–154.CrossRefGoogle Scholar
  44. 44.
    Brachmann, S. M., et al. (2009). Specific apoptosis induction by the dual PI3K/mTor inhibitor NVP-BEZ235 in HER2 amplified and PIK3CA mutant breast cancer cells. Proceedings of the National Academy of Sciences, 106(52), 22299–22304.CrossRefGoogle Scholar
  45. 45.
    Kamenetz, F., et al. (2003). APP processing and synaptic function. Neuron, 37(6), 925–937.CrossRefGoogle Scholar
  46. 46.
    Hansel, D. E., et al. (2003). Increased expression and processing of the Alzheimer amyloid precursor protein in pancreatic cancer may influence cellular proliferation. Cancer Research, 63(21), 7032–7037.Google Scholar
  47. 47.
    Kayed, R., et al. (2003). Common structure of soluble amyloid oligomers implies common mechanism of pathogenesis. Science, 300(5618), 486–489.CrossRefGoogle Scholar
  48. 48.
    Wang, K. K. W., & Po-Wai, Y. (1994). Calpain inhibition: an overview of its therapeutic potential. Trends in Pharmacological Sciences, 15(11), 412–419.CrossRefGoogle Scholar
  49. 49.
    Song, G., Ouyang, G., & Bao, S. (2005). The activation of Akt/PKB signaling pathway and cell survival. Journal of Cellular and Molecular Medicine, 9(1), 59–71.CrossRefGoogle Scholar
  50. 50.
    DeFeo-Jones, D., et al. (2005). Tumor cell sensitization to apoptotic stimuli by selective inhibition of specific Akt/PKB family members. Molecular Cancer Therapeutics, 4(2), 271–279.Google Scholar
  51. 51.
    Vivanco, I., & Sawyers, C. L. (2002). The phosphatidylinositol 3-kinase-AKT pathway in human cancer. Nature Reviews Cancer, 2, 489–501.CrossRefGoogle Scholar
  52. 52.
    Rana, S., et al. (2008). Stathmin 1: a novel therapeutic target for anticancer activity. Expert Review of Anticancer Therapy, 8(9), 1461–1470.CrossRefGoogle Scholar
  53. 53.
    Karst, A. M., et al. (2011). Stathmin 1, a marker of PI3K pathway activation and regulator of microtubule dynamics, is expressed in early pelvic serous carcinomas. Gynecologic Oncology, 123(1), 5–12.CrossRefGoogle Scholar
  54. 54.
    Dejda, A., et al. (2010). Involvement of stathmin 1 in the neurotrophic effects of PACAP in PC12 cells. Journal of Neurochemistry, 114(5), 1498–1510.Google Scholar
  55. 55.
    Raftopoulou, M., & Hall, A. (2004). Cell migration: Rho GTPases lead the way. Developmental Biology, 265(1), 23–32.CrossRefGoogle Scholar
  56. 56.
    Barillé-Nion, S., et al. (2012). Regulation of cancer cell survival by BCL2 family members upon prolonged mitotic arrest: opportunities for anticancer therapy. Anticancer Research, 32(10), 4225–4233.Google Scholar
  57. 57.
    Brichese, L., Cazettes, G., & Valette, A. (2004). JNK is associated with Bcl-2 and PP1 in mitochondria: paclitaxel induces its activation and its association with the phosphorylated form of Bcl-2. Cell Cycle, 3(10), 1312–1319.CrossRefGoogle Scholar
  58. 58.
    Koo, C.-Y., Muir, K. W., & Lam, E. W. F. (2012). FOXM1: from cancer initiation to progression and treatment. Biochimica et Biophysica Acta (BBA)-Gene Regulatory Mechanisms, 1819(1), 28–37.CrossRefGoogle Scholar
  59. 59.
    Kovács, K. J. (1998). Invited review c-Fos as a transcription factor: a stressful (re)view from a functional map. Neurochemistry International, 33(4), 287–297.CrossRefGoogle Scholar
  60. 60.
    Karadedou, C. T. (2006). Regulation of the FOXM1 transcription factor by the estrogen receptor α at the protein level, in breast cancer. Hippokratia, 10(3), 128–132.Google Scholar
  61. 61.
    Ingman, W. V., & Robertson, S. A. (2008). Mammary gland development in transforming growth factor beta1 null mutant mice: systemic and epithelial effects. Biology of Reproduction, 79(4), 711–717.CrossRefGoogle Scholar
  62. 62.
    Cox, D., Penney, K., Guo, Q., Hankinson, S. E., & Hunter, D. J. (2007). TGFB1 and TGFBR1 polymorphisms and breast cancer risk in the Nurses' Health Study. BMC Cancer, 7, 175.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Nour Fouz
    • 1
  • Azura Amid
    • 1
    • 2
    Email author
  • Yumi Zuhanis Has-Yun Hashim
    • 2
  1. 1.Bioprocess and Molecular Engineering Research Unit (BPMERU), Department of Biotechnology Engineering, Faculty of EngineeringInternational Islamic University MalaysiaKuala LumpurMalaysia
  2. 2.International Institute for Halal Research and TrainingInternational Islamic University MalaysiaKuala LumpurMalaysia

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