Asian Pacific Journal of Cancer Prevention

Asian Pacific Journal of Cancer Prevention (아시아태평양암예방학회)
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MicroRNA Expression Profile Analysis Reveals Diagnostic Biomarker for Human Prostate Cancer

  • Liu, Dong-Fu (Department of Urology, Yantai Yuhuangding Hospital) ;
  • Wu, Ji-Tao (Department of Urology, Yantai Yuhuangding Hospital) ;
  • Wang, Jian-Ming (Department of Urology, Yantai Yuhuangding Hospital) ;
  • Liu, Qing-Zuo (Department of Urology, Yantai Yuhuangding Hospital) ;
  • Gao, Zhen-Li (Department of Urology, Yantai Yuhuangding Hospital) ;
  • Liu, Yun-Xiang (Department of Urology, Yantai Yuhuangding Hospital)
  • Published : 2012.07.31
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Abstract

Prostate cancer is a highly prevalent disease in older men of the western world. MicroRNAs (miRNAs) are small RNA molecules that regulate gene expression via posttranscriptional inhibition of protein synthesis. To identify the diagnostic potential of miRNAs in prostate cancer, we downloaded the miRNA expression profile of prostate cancer from the GEO database and analysed the differentially expressed miRNAs (DE-miRNAs) in prostate cancerous tissue compared to non-cancerous tissue. Then, the targets of these DE-miRNAs were extracted from the database and mapped to the STRING and KEGG databases for network construction and pathway enrichment analysis. We identified a total of 16 miRNAs that showed a significant differential expression in cancer samples. A total of 9 target genes corresponding to 3 DE-miRNAs were obtained. After network and pathway enrichment analysis, we finally demonstrated that miR-20 appears to play an important role in the regulation of prostate cancer onset. MiR-20 as single biomarker or in combination could be useful in the diagnosis of prostate cancer. We anticipate our study could provide the groundwork for further experiments.

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References

  1. Ambs S, Prueitt RL, Yi M, et al (2008). Genomic profiling of microRNA and messenger RNA reveals deregulated microRNA expression in prostate cancer. Cancer Res, 68, 6162-70. https://doi.org/10.1158/0008-5472.CAN-08-0144
  2. Bartel DP (2004). MicroRNAs: genomics, biogenesis, mechanism, and function. Cell, 116, 281-97. https://doi.org/10.1016/S0092-8674(04)00045-5
  3. Benjamini YH, Y (1995). Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. Journal of the royal statistical society Series B (Methodological), 57, 289-300.
  4. Berezikov E, Guryev V, van de Belt J, et al (2005). Phylogenetic shadowing and computational identification of human microRNA genes. Cell, 120, 21-4. https://doi.org/10.1016/j.cell.2004.12.031
  5. Bubendorf L, Schopfer A, Wagner U, et al (2000). Metastatic patterns of prostate cancer: an autopsy study of 1,589 patients. Hum Pathol, 31, 578-83. https://doi.org/10.1053/hp.2000.6698
  6. Chan JM, Jou RM, Carroll PR (2004). The relative impact and future burden of prostate cancer in the United States. J Urol, 172, S13-6; discussion S7. https://doi.org/10.1097/01.ju.0000142068.66876.53
  7. Cummins JM, He Y, Leary RJ, et al (2006). The colorectal microRNAome. Proc Natl Acad Sci U S A, 103, 3687-92. https://doi.org/10.1073/pnas.0511155103
  8. Ferrara N, Gerber HP, LeCouter J (2003). The biology of VEGF and its receptors. Nat Med, 9, 669-76. https://doi.org/10.1038/nm0603-669
  9. Garcia DM, Baek D, Shin C, et al (2011). Weak seed-pairing stability and high target-site abundance decrease the proficiency of lsy-6 and other microRNAs. Nat Struct Mol Biol, 18, 1139-46. https://doi.org/10.1038/nsmb.2115
  10. Gautier L, Cope L, Bolstad BM, Irizarry RA (2004). affy--analysis of Affymetrix GeneChip data at the probe level. Bioinformatics, 20, 307-15. https://doi.org/10.1093/bioinformatics/btg405
  11. Hao P, Chen X, Geng H, et al (2004). Expression and implication of hypoxia inducible factor-1alpha in prostate neoplasm. J Huazhong Univ Sci Technolog Med Sci, 24, 593-5. https://doi.org/10.1007/BF02911365
  12. Horvath S, Zhang B, Carlson M, et al (2006). Analysis of oncogenic signaling networks in glioblastoma identifies ASPM as a molecular target. Proc Natl Acad Sci USA, 103, 17402-7. https://doi.org/10.1073/pnas.0608396103
  13. Iorio MV, Ferracin M, Liu CG, et al (2005). MicroRNA gene expression deregulation in human breast cancer. Cancer Res, 65, 7065-70. https://doi.org/10.1158/0008-5472.CAN-05-1783
  14. Irizarry RA, Hobbs B, Collin F, et al (2003). Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics, 4, 249-64. https://doi.org/10.1093/biostatistics/4.2.249
  15. John B, Enright AJ, Aravin A, et al (2004). Human MicroRNA targets. PLoS Biol, 2, e363. https://doi.org/10.1371/journal.pbio.0020363
  16. Kanehisa M (2002). The KEGG database. Novartis Found Symp, 247, 91-101; discussion -3, 19-28, 244-52. https://doi.org/10.1002/0470857897.ch8
  17. Kimbro KS, Simons JW (2006). Hypoxia-inducible factor-1 in human breast and prostate cancer. Endocr Relat Cancer, 13, 739-49. https://doi.org/10.1677/erc.1.00728
  18. Kozomara A, Griffiths-Jones S (2011). miRBase: integrating microRNA annotation and deep-sequencing data. Nucleic Acids Res, 39, D152-7. https://doi.org/10.1093/nar/gkq1027
  19. Krek A, Grun D, Poy MN, et al (2005). Combinatorial microRNA target predictions. Nat Genet, 37, 495-500. https://doi.org/10.1038/ng1536
  20. Lee EJ, Gusev Y, Jiang J, et al (2007). Expression profiling identifies microRNA signature in pancreatic cancer. Int J Cancer, 120, 1046-54.
  21. Lee RC, Feinbaum RL, Ambros V (1993). The C. elegans heterochronic gene lin-4 encodes small RNAs with antisense complementarity to lin-14. Cell, 75, 843-54. https://doi.org/10.1016/0092-8674(93)90529-Y
  22. Linton DK, Hamdy FC (2004). Early diagnosis and surgical management of prostate cancer. Ann Urol (Paris), 38, 137-47. https://doi.org/10.1016/j.anuro.2004.06.001
  23. Logothetis CJ, Lin SH (2005). Osteoblasts in prostate cancer metastasis to bone. Nat Rev Cancer, 5, 21-8. https://doi.org/10.1038/nrc1528
  24. Lu J, Getz G, Miska EA, et al (2005). MicroRNA expression profiles classify human cancers. Nature, 435, 834-8. https://doi.org/10.1038/nature03702
  25. Mattie MD, Benz CC, Bowers J, et al (2006). Optimized high-throughput microRNA expression profiling provides novel biomarker assessment of clinical prostate and breast cancer biopsies. Mol Cancer, 5, 24. https://doi.org/10.1186/1476-4598-5-24
  26. McCarron SL, Edwards S, Evans PR, et al (2002). Influence of cytokine gene polymorphisms on the development of prostate cancer. Cancer Res, 62, 3369-72.
  27. Min H, Yoon S (2010). Got target? Computational methods for microRNA target prediction and their extension. Exp Mol Med, 42, 233-44. https://doi.org/10.3858/emm.2010.42.4.032
  28. Murakami Y, Yasuda T, Saigo K, et al (2006). Comprehensive analysis of microRNA expression patterns in hepatocellular carcinoma and non-tumorous tissues. Oncogene, 25, 2537-45. https://doi.org/10.1038/sj.onc.1209283
  29. O'Donnell KA, Wentzel EA, Zeller KI, et al (2005). c-Myc-regulated microRNAs modulate E2F1 expression. Nature, 435, 839-43. https://doi.org/10.1038/nature03677
  30. Ozen M, Creighton CJ, Ozdemir M, Ittmann M (2008). Widespread deregulation of microRNA expression in human prostate cancer. Oncogene, 27, 1788-93. https://doi.org/10.1038/sj.onc.1210809
  31. Porkka KP, Pfeiffer MJ, Waltering KK, et al (2007). MicroRNA expression profiling in prostate cancer. Cancer Res, 67, 6130-5. https://doi.org/10.1158/0008-5472.CAN-07-0533
  32. Schaefer A, Jung M, Miller K, et al (2010). Suitable reference genes for relative quantification of miRNA expression in prostate cancer. Exp Mol Med, 42, 749-58. https://doi.org/10.3858/emm.2010.42.11.076
  33. Schaefer A, Jung M, Mollenkopf HJ, et al (2010). Diagnostic and prognostic implications of microRNA profiling in prostate carcinoma. Int J Cancer, 126, 1166-76.
  34. Semenza GL (2003). Targeting HIF-1 for cancer therapy. Nat Rev Cancer, 3, 721-32. https://doi.org/10.1038/nrc1187
  35. Sempere LF, Christensen M, Silahtaroglu A, et al (2007). Altered MicroRNA expression confined to specific epithelial cell subpopulations in breast cancer. Cancer Res, 67, 11612-20. https://doi.org/10.1158/0008-5472.CAN-07-5019
  36. Sfar S, Hassen E, Saad H, et al (2006). Association of VEGF genetic polymorphisms with prostate carcinoma risk and clinical outcome. Cytokine, 35, 21-8. https://doi.org/10.1016/j.cyto.2006.07.003
  37. Shen MM, Abate-Shen C (2010). Molecular genetics of prostate cancer: new prospects for old challenges. Genes Dev, 24, 1967-2000. https://doi.org/10.1101/gad.1965810
  38. Siegel R, Naishadham D, Jemal A (2012). Cancer statistics, 2012. CA Cancer J Clin, 62, 10-29. https://doi.org/10.3322/caac.20138
  39. Szklarczyk D, Franceschini A, Kuhn M, et al (2011). The STRING database in 2011: functional interaction networks of proteins, globally integrated and scored. Nucleic Acids Res, 39, D561-8. https://doi.org/10.1093/nar/gkq973
  40. Team RDC (2011). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing.
  41. Tong AW, Fulgham P, Jay C, et al (2009). MicroRNA profile analysis of human prostate cancers. Cancer Gene Ther, 16, 206-16.
  42. van der Laan MJ, Dudoit S, Pollard KS (2004). Augmentation procedures for control of the generalized family-wise error rate and tail probabilities for the proportion of false positives. Stat Appl Genet Mol Biol, 3, Article15.
  43. Volinia S, Calin GA, Liu CG, et al (2006). A microRNA expression signature of human solid tumors defines cancer gene targets. Proc Natl Acad Sci USA, 103, 2257-61. https://doi.org/10.1073/pnas.0510565103
  44. Wach S, Nolte E, Szczyrba J, et al (2012). MicroRNA profiles of prostate carcinoma detected by multiplatform microRNA screening. Int J Cancer, 130, 611-21. https://doi.org/10.1002/ijc.26064
  45. Wang Y, Shao C, Shi CH, et al (2005). Change of the cell cycle after flutamide treatment in prostate cancer cells and its molecular mechanism. Asian J Androl, 7, 375-80. https://doi.org/10.1111/j.1745-7262.2005.00031.x
  46. Wightman B, Ha I, Ruvkun G (1993). Posttranscriptional regulation of the heterochronic gene lin-14 by lin-4 mediates temporal pattern formation in C. elegans. Cell, 75, 855-62. https://doi.org/10.1016/0092-8674(93)90530-4
  47. Zhang JF, Fu WM, He ML, et al (2011). MiRNA-20a promotes osteogenic differentiation of human mesenchymal stem cells by co-regulating BMP signaling. RNA Biol, 8.
  48. Zhong H, Agani F, Baccala AA, et al (1998). Increased expression of hypoxia inducible factor-1alpha in rat and human prostate cancer. Cancer Res, 58, 5280-4.

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