Asian Pacific Journal of Cancer Prevention

Asian Pacific Journal of Cancer Prevention (아시아태평양암예방학회)
권호 표지
DOI QR코드

DOI QR Code

Identifying Differentially Expressed Genes and Small Molecule Drugs for Prostate Cancer by a Bioinformatics Strategy

  • Li, Jian (Department of Urology, the 452nd Hospital of PLA) ;
  • Xu, Ya-Hong (Department of Urology, the 452nd Hospital of PLA) ;
  • Lu, Yi (Department of Urology, the 452nd Hospital of PLA) ;
  • Ma, Xiao-Ping (Department of Urology, the 452nd Hospital of PLA) ;
  • Chen, Ping (Department of Urology, the 452nd Hospital of PLA) ;
  • Luo, Shun-Wen (Department of Urology, the 452nd Hospital of PLA) ;
  • Jia, Zhi-Gang (Department of Urology, the 452nd Hospital of PLA) ;
  • Liu, Yang (Department of Urology, the 452nd Hospital of PLA) ;
  • Guo, Yu (Department of Urology, the 452nd Hospital of PLA)
  • Published : 2013.09.30
Download PDF
⟨ Previous Next ⟩

Abstract

Purpose: Prostate cancer caused by the abnormal disorderly growth of prostatic acinar cells is the most prevalent cancer of men in western countries. We aimed to screen out differentially expressed genes (DEGs) and explore small molecule drugs for prostate cancer. Materials and Methods: The GSE3824 gene expression profile of prostate cancer was downloaded from Gene Expression Omnibus database which including 21 normal samples and 18 prostate cancer cells. The DEGs were identified by Limma package in R language and gene ontology and pathway enrichment analyses were performed. In addition, potential regulatory microRNAs and the target sites of the transcription factors were screened out based on the molecular signature database. In addition, the DEGs were mapped to the connectivity map database to identify potential small molecule drugs. Results: A total of 6,588 genes were filtered as DEGs between normal and prostate cancer samples. Examples such as ITGB6, ITGB3, ITGAV and ITGA2 may induce prostate cancer through actions on the focal adhesion pathway. Furthermore, the transcription factor, SP1, and its target genes ARHGAP26 and USF1 were identified. The most significant microRNA, MIR-506, was screened and found to regulate genes including ITGB1 and ITGB3. Additionally, small molecules MS-275, 8-azaguanine and pyrvinium were discovered to have the potential to repair the disordered metabolic pathways, abd furthermore to remedy prostate cancer. Conclusions: The results of our analysis bear on the mechanism of prostate cancer and allow screening for small molecular drugs for this cancer. The findings have the potential for future use in the clinic for treatment of prostate cancer.

Keywords

References

  1. Altmann A, Eisenhut M, Bauder-Wust U, et al (2010). Therapy of thyroid carcinoma with the histone deacetylase inhibitor MS-275. Eur J Nucl Med Mol Imaging, 37, 2286-97. https://doi.org/10.1007/s00259-010-1573-3
  2. Andriole GL, Bostwick DG, Brawley OW, et al (2010). Effect of dutasteride on the risk of prostate cancer. N Engl J Med, 362, 1192-202. https://doi.org/10.1056/NEJMoa0908127
  3. Aryee MJ, Liu W, Engelmann JC, et al (2013). DNA methylation alterations exhibit intraindividual stability and interindividual heterogeneity in prostate cancer metastases. Sci Transl Med, 5, 169ra10.
  4. Boorjian SA, Tindall DJ (2013). Molecular Progression of Prostate Cancer: Androgens and Estrogens. Management of Prostate Cancer. Springer.
  5. Braconi C, Swenson E, Kogure T, et al (2010). Targeting the IL-6 dependent phenotype can identify novel therapies for cholangiocarcinoma. PloS One, 5, e15195. https://doi.org/10.1371/journal.pone.0015195
  6. Chang JTC, Yang HT, Wang TCV, Cheng AJ (2005). Upstream stimulatory factor (USF) as a transcriptional suppressor of human telomerase reverse transcriptase (hTERT) in oral cancer cells. Mol Carcinog, 44, 183-92. https://doi.org/10.1002/mc.20129
  7. Daemen A, Gevaert O, De Bie T, et al (2008). Integrating microarray and proteomics data to predict the response on cetuximab in patients with rectal cancer. Pac Symp Biocomput, 2008, 166-77.
  8. de Bono JS, Logothetis CJ, Molina A, et al (2011). Abiraterone and increased survival in metastatic prostate cancer. N Engl J Med, 364, 1995-2005. https://doi.org/10.1056/NEJMoa1014618
  9. DeMarzo AM, Nelson WG, Isaacs WB , Epstein JI (2003). Pathological and molecular aspects of prostate cancer. Lancet, 361, 955-64. https://doi.org/10.1016/S0140-6736(03)12779-1
  10. Diboun I, Wernisch L, Orengo CA, Koltzenburg M (2006). Microarray analysis after RNA amplification can detect pronounced differences in gene expression using limma. BMC Genomics, 7, 252. https://doi.org/10.1186/1471-2164-7-252
  11. Duncan D, Prodduturi N, Zhang B (2010). WebGestalt2: an updated and expanded version of the Web-based Gene Set Analysis Toolkit. BMC Bioinformatics, 11, 1-. https://doi.org/10.1186/1471-2105-11-1
  12. Fabbri M, Ivan M, Cimmino A, et al (2007). Regulatory mechanisms of microRNAs involvement in cancer: the strange case of Dr Jekyll and Mr Hyde.
  13. Gogia S, Puranik M (2013). Solution structures of purine base analogues 6-chloroguanine, 8-azaguanine and allopurinol. J Biomol Struct Dyn, 1-9.
  14. Harada Y, Ishii I, Hatake K, Kasahara T (2012). Pyrvinium pamoate inhibits proliferation of myeloma/erythroleukemia cells by suppressing mitochondrial respiratory complex I and STAT3. Cancer Lett, 319, 83-8. https://doi.org/10.1016/j.canlet.2011.12.034
  15. Hulsegge I, Kommadath A , Smits MA (2009). Globaltest and GOEAST: two different approaches for Gene Ontology analysis. BMC Proc, 3, S10.
  16. Jarius S, Martinez-Garcia P, Hernandez AL, et al (2013). Two new cases of anti-Ca (anti-ARHGAP26/GRAF) autoantibodyassociated cerebellar ataxia. J Neuroinflammation, 10, 1-6. https://doi.org/10.1186/1742-2094-10-1
  17. Kelder T, van Iersel MP, Hanspers K, et al (2012). WikiPathways: building research communities on biological pathways. Nucleic Acids Res, 40, D1301-7. https://doi.org/10.1093/nar/gkr1074
  18. Lamb J, Crawford ED, Peck D, et al (2006). The Connectivity Map: using gene-expression signatures to connect small molecules, genes, and disease. Science, 313, 1929. https://doi.org/10.1126/science.1132939
  19. Langsenlehner U, Renner W, Yazdani-Biuki B, et al (2006). Integrin alpha-2 and beta-3 gene polymorphisms and breast cancer risk. Breast Cancer Res Treat, 97, 67-72. https://doi.org/10.1007/s10549-005-9089-4
  20. Liu C, Kelnar K, Liu B, et al (2011). The microRNA miR-34a inhibits prostate cancer stem cells and metastasis by directly repressing CD44. Nat Med, 17, 211-5. https://doi.org/10.1038/nm.2284
  21. Luo M , Guan JL (2010). Focal adhesion kinase: a prominent determinant in breast cancer initiation, progression and metastasis. Cancer Lett, 289, 127-39. https://doi.org/10.1016/j.canlet.2009.07.005
  22. Pico AR, Kelder T, Van Iersel MP, et al (2008). WikiPathways: pathway editing for the people. PLoS Biol, 6, e184. https://doi.org/10.1371/journal.pbio.0060184
  23. Pontes-Junior J, Reis ST, Dall'Oglio M, et al (2009). Evaluation of the expression of integrins and cell adhesion molecules through tissue microarray in lymph node metastases of prostate cancer. J Carcinog, 8, 3. https://doi.org/10.4103/1477-3163.48453
  24. Porkka KP, Visakorpi T (2004). Molecular mechanisms of prostate cancer. Eur Urol, 45, 683-91. https://doi.org/10.1016/j.eururo.2004.01.012
  25. Shapiro DJ, Katzenellenbogen B, Hergenrother PJ, Bolton EC (2012). Small molecule inhibitors of steroid receptors for breast and prostate cancer. J Biol Chem, 286, 4043-8.
  26. Sherman BT, Lempicki RA (2009). Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res, 37, 1-13. https://doi.org/10.1093/nar/gkn923
  27. Singh PK, Doig CL, Dhiman VK, et al (2012). Epigenetic distortion to VDR transcriptional regulation in prostate cancer cells. J Steroid Biochem Mol Biol, 136, 258-63.
  28. Smyth GK (2004). Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol, 3, 3.
  29. Subramanian A, Tamayo P, Mootha VK, et al (2005). Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A, 102, 15545-50. https://doi.org/10.1073/pnas.0506580102
  30. Sugahara KN, Teesalu T, Karmali PP, et al (2010). Coadministration of a tumor-penetrating peptide enhances the efficacy of cancer drugs. Science, 328, 1031-5. https://doi.org/10.1126/science.1183057
  31. Taylor BS, Schultz N, Hieronymus H, et al (2010). Integrative genomic profiling of human prostate cancer. Cancer Cell, 18, 11-22. https://doi.org/10.1016/j.ccr.2010.05.026
  32. Team RC (2011). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2008. ISBN 3-900051-07-0. Available at: http:// www. R-project.org.
  33. Zhang B, Kirov S, Snoddy J (2005). WebGestalt: an integrated system for exploring gene sets in various biological contexts. Nucleic Acids Res, 33, W741-8. https://doi.org/10.1093/nar/gki475
  34. Zhang L, Yang BX, Zhang HT, et al (2011). Prostate cancer: an emerging threat to the health of aging men in Asia. Asian J Androl, 13, 574-8. https://doi.org/10.1038/aja.2010.126
  35. Zheng Q , Wang XJ (2008). GOEAST: a web-based software toolkit for Gene Ontology enrichment analysis. Nucleic Acids Res, 36, W358-63. https://doi.org/10.1093/nar/gkn276

Cited by

  1. RETRACTED ARTICLE: ChIP-seq analysis of androgen receptor in LNCaP cell line vol.41, pp.9, 2014, https://doi.org/10.1007/s11033-014-3511-0
  2. Cellular prostatic acid phosphatase (cPAcP) serves as a useful biomarker of histone deacetylase (HDAC) inhibitors in prostate cancer cell growth suppression vol.5, pp.1, 2015, https://doi.org/10.1186/s13578-015-0033-y
  3. Common Dysregulation of Ribosomal Genes Present in Infants with Acute Respiratory Infection of Respiratory Syncytial Virus, Rhinovirus, and Influenza A vol.28, pp.1, 2015, https://doi.org/10.1089/ped.2014.0400