Computational Method for Prediction of Targets for Breast Cancer Using siRNA Approach

  • Atul Tyagi
  • Mukti N. Mishra
  • Ashok SharmaEmail author
Part of the Methods in Pharmacology and Toxicology book series (MIPT)


The increasing incident of breast cancer, which is a leading cause of women’s death in both developed and developing countries, demands the development of novel and efficient therapies. One of the major challenges is to design drugs that can specifically target the genes or proteins responsible for breast cancer, as gene and chemotherapy both are suffering from the drug specificity issues. Several recent studies have highlighted the potential of RNA interference (RNAi)-mediated targeted silencing of breast oncogenes, which can be exploited to develop cancer cell-/target-specific therapeutic molecules. However, one of the bottlenecks of RNAi-based gene therapy is to identify the RNAi sequences for efficient and targeted suppression of oncogenes. In this chapter, we discuss the development and application of a web-based database, BOSS (, for selection of potential RNAi based on the sequences that have been used and validated for RNAi-mediated suppression of breast oncogenes. This database includes the latest information regarding used RNAi molecules that can be cost-effective and less time-consuming.


Breast cancer Gene silencing Mammary cancer Oncogene RNAi shRNAs siRNAs 



A.T. is thankful to ICMR, New Delhi, India, for ICMR-SRF fellowship.



Breast oncogenic specific siRNAs database


Small interfering RNAs


Short hairpin RNAs


RNA interference


  1. 1.
    Sledge GW, Miller KD (2003) Exploiting the hallmarks of cancer: the future conquest of breast cancer. Eur J Cancer 39:1668–1675CrossRefGoogle Scholar
  2. 2.
    Croce CM (2008) Oncogenes and cancer. N Engl J Med 358:502–511CrossRefGoogle Scholar
  3. 3.
    Osborne C, Wilson P, Tripathy D (2004) Oncogenes and tumor suppressor genes in breast cancer: potential diagnostic and therapeutic applications. Oncologist 9:361–377CrossRefGoogle Scholar
  4. 4.
    Timmons L, Fire A (1998) Specific interference by ingested dsRNA. Nature 395:854–854CrossRefGoogle Scholar
  5. 5.
    Zheng Y, Liu Y, Jin H et al (2013) Scavenger receptor B1 is a potential biomarker of human nasopharyngeal carcinoma and its growth is inhibited by HDL-mimetic nanoparticles. Theranostics 3:477–486CrossRefGoogle Scholar
  6. 6.
    Mishra MN, Mishra MN, Vangara KK et al (2014) Transcriptional targeting of human liver carboxylesterase (hCE1m6) and simultaneous expression of anti-BCRP shRNA enhances sensitivity of breast cancer cells to CPT-11. Anticancer Res 34:6345–6351PubMedGoogle Scholar
  7. 7.
    Dash R, Moharana SS, Reddy AS et al (2006) DSTHO: database of siRNAs targeted at human oncogenes: a statistical analysis. Int J Biol Macromol 38:65–69CrossRefGoogle Scholar
  8. 8.
    Tyagi A, Ahmed F, Thakur N et al (2011) HIVsirDB: a database of HIV inhibiting siRNAs. PLoS One 6:e25917CrossRefGoogle Scholar
  9. 9.
    Ren Y, Gong W, Zhou H et al (2009) siRecords: a database of mammalian RNAi experiments and efficacies. Nucleic Acids Res 37:D146–D149CrossRefGoogle Scholar
  10. 10.
    Tyagi A, Semwal M, Sharma A (2017) A database of breast oncogenic specific siRNAs. Sci Rep 7:8706CrossRefGoogle Scholar
  11. 11.
    Jemal A, Bray F, Center MM et al (2011) Global cancer statistics. CA Cancer J Clin 61:69–90CrossRefGoogle Scholar
  12. 12.
    Elbashir SM, Harborth J, Lendeckel W et al (2001) Duplexes of 21-nucleotide RNAs mediate RNA interference in cultured mammalian cells. Nature 411:494–498CrossRefGoogle Scholar
  13. 13.
    Mazur S, Csucs G, Kozak K (2012) RNAiAtlas: a database for RNAi (siRNA) libraries and their specificity. Database (Oxford) 2012:bas027CrossRefGoogle Scholar
  14. 14.
    Zhang C, Li G, Zhu S et al (2014) tasiRNAdb: a database of ta-siRNA regulatory pathways. Bioinformatics 30:1045–1046CrossRefGoogle Scholar
  15. 15.
    Saunders RE, Instrell R, Rispoli R et al (2013) HTS-DB: an online resource to publish and query data from functional genomics high-throughput siRNA screening projects. Database (Oxford) 2013:bat072CrossRefGoogle Scholar
  16. 16.
    sIR: siRNA information Resource, a web-based tool for siRNA sequence design and analysis and an open access siRNA database. BMC Bioinformatics, Full Text,
  17. 17.
    Truss M, Swat M, Kielbasa SM et al (2005) HuSiDa—the human siRNA database: an open-access database for published functional siRNA sequences and technical details of efficient transfer into recipient cells. Nucleic Acids Res 33:D108–D111CrossRefGoogle Scholar
  18. 18.
    Thakur N, Qureshi A, Kumar M (2012) VIRsiRNAdb: a curated database of experimentally validated viral siRNA/shRNA. Nucleic Acids Res 40:D230–D236CrossRefGoogle Scholar
  19. 19.
    Dar SA, Thakur A, Qureshi A et al (2016) siRNAmod: a database of experimentally validated chemically modified siRNAs. Sci Rep 6:20031CrossRefGoogle Scholar
  20. 20.
    Liang Y, Gao H, Lin S-Y et al (2010) siRNA-based targeting of cyclin E overexpression inhibits breast cancer cell growth and suppresses tumor development in breast cancer mouse model. PLoS One 5:e12860CrossRefGoogle Scholar
  21. 21.
    Garrido P, Osorio FG, Morán J et al (2015) Loss of GLUT4 induces metabolic reprogramming and impairs viability of breast cancer cells. J Cell Physiol 230:191–198CrossRefGoogle Scholar
  22. 22.
    Qin B, Cheng K (2010) Silencing of the IKKε gene by siRNA inhibits invasiveness and growth of breast cancer cells. Breast Cancer Res BCR 12:R74CrossRefGoogle Scholar
  23. 23.
    Xu D, Kang H, Fisher M et al (2004) Strategies for inhibition of MDR1 gene expression. Mol Pharmacol 66:268–275CrossRefGoogle Scholar
  24. 24.
    Luo X-G, Zou J-N, Wang S-Z et al (2010) Novobiocin decreases SMYD3 expression and inhibits the migration of MDA-MB-231 human breast cancer cells. IUBMB Life 62:194–199CrossRefGoogle Scholar
  25. 25.
    Shaker H, Harrison H, Clarke R et al (2017) Tissue factor promotes breast cancer stem cell activity in vitro. Oncotarget 8:25915–25927CrossRefGoogle Scholar
  26. 26.
    Wu J, Richer J, Horwitz KB et al (2004) Progestin-dependent induction of vascular endothelial growth factor in human breast cancer cells: preferential regulation by progesterone receptor B. Cancer Res 64:2238–2244CrossRefGoogle Scholar
  27. 27.
    Liang B, Wang X-J, Shen P-H et al (2013) Synuclein-γ suppression mediated by RNA interference inhibits the clonogenicity and invasiveness of MCF-7 cells. Oncol Lett 5:1347–1352CrossRefGoogle Scholar
  28. 28.
    Han G, Fan B, Zhang Y et al (2008) Positive regulation of migration and invasion by vasodilator-stimulated phosphoprotein via Rac1 pathway in human breast cancer cells. Oncol Rep 20:929–939PubMedGoogle Scholar
  29. 29.
    Ji X, Lu H, Zhou Q et al (2014) LARP7 suppresses P-TEFb activity to inhibit breast cancer progression and metastasis. Elife 3:e02907CrossRefGoogle Scholar
  30. 30.
    Jang J-Y, Choi Y, Jeon Y-K et al (2008) Suppression of adenine nucleotide translocase-2 by vector-based siRNA in human breast cancer cells induces apoptosis and inhibits tumor growth in vitro and in vivo. Breast Cancer Res BCR 10:R11CrossRefGoogle Scholar
  31. 31.
    Aletaha M, Mansoori B, Mohammadi A et al (2017) Therapeutic effects of bach1 siRNA on human breast adenocarcinoma cell line. Biomed Pharmacother 88:34–42CrossRefGoogle Scholar
  32. 32.
    Sun L, Cai L, Yu Y et al (2007) Knockdown of S-phase kinase-associated protein-2 expression in MCF-7 inhibits cell growth and enhances the cytotoxic effects of epirubicin. Acta Biochim Biophys Sin 39:999–1007CrossRefGoogle Scholar
  33. 33.
    Salceda S, Tang T, Kmet M et al (2005) The immunomodulatory protein B7-H4 is overexpressed in breast and ovarian cancers and promotes epithelial cell transformation. Exp Cell Res 306:128–141CrossRefGoogle Scholar
  34. 34.
    Toy EP, Lamb T, Azodi M et al (2011) Inhibition of the c-fms proto-oncogene autocrine loop and tumor phenotype in glucocorticoid stimulated human breast carcinoma cells. Breast Cancer Res Treat 129:411–419CrossRefGoogle Scholar
  35. 35.
    Li Z, Meng Q, Pan A et al (2017) MicroRNA-455-3p promotes invasion and migration in triple negative breast cancer by targeting tumor suppressor EI24. Oncotarget 8:19455–19466PubMedGoogle Scholar
  36. 36.
    US 7615627 B2—Rna interference mediated inhibition of aurorakinase B and its combinations as anticancer therapy. The Lens,
  37. 37.
    Soni A, Akcakanat A, Singh G et al (2008) eIF4E knockdown decreases breast cancer cell growth without activating Akt signaling. Mol Cancer Ther 7:1782–1788CrossRefGoogle Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Biotechnology DivisionCSIR-Central Institute of Medicinal and Aromatic PlantsLucknowIndia

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