Asian Pacific Journal of Cancer Prevention

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

DOI QR Code

Evaluation of Combined Quantification of PCA3 and AMACR Gene Expression for Molecular Diagnosis of Prostate Cancer in Moroccan Patients by RT-qPCR

  • Maane, Imane Abdellaoui (MASCIR: Moroccan Foundation for Advanced Science, Innovation and Research) ;
  • El Hadi, Hicham (MASCIR: Moroccan Foundation for Advanced Science, Innovation and Research) ;
  • Qmichou, Zineb (MASCIR: Moroccan Foundation for Advanced Science, Innovation and Research) ;
  • Al Bouzidi, Abderrahmane (Military Hospital Mohammed V) ;
  • Bakri, Youssef (Biochemistry and Immunology Laboratory, Faculty of Science, Rabat University Mohamed V) ;
  • Sefrioui, Hassan (MASCIR: Moroccan Foundation for Advanced Science, Innovation and Research) ;
  • Dakka, Nadia (Biochemistry and Immunology Laboratory, Faculty of Science, Rabat University Mohamed V) ;
  • Moumen, Abdeladim (MASCIR: Moroccan Foundation for Advanced Science, Innovation and Research)
  • Published : 2016.12.01
⟨ Previous Next ⟩

Abstract

Prostate cancer (PCa) remains one of the most widespread and perplexing of all human malignancies. Assessment of gene expression is thought to have an important impact on cancer diagnosis, prognosis and therapeutic decisions. In this context, we explored combined expression of PCa related target genes AMACR and PCA3 in 126 formalin fixed paraffin embedded prostate tissues (FFPE) from Moroccan patients, using quantitative real time reverse transcription-PCR (RT-qPCR). This quantification required data normalization accomplished using stably expressed reference genes (RGs). A panel of twelve RG was assessed, data being analyzed using GenEx V6 based on geNorm, NormFinder and statistical methods. Accordingly, the hnRNP A1 gene was identified and selected as the most stably expressed RG for reliable and accurate gene expression quantification in prostate tissues. The ratios of both PCA3 and AMACR gene expression relative to that of the hnRNP A1 gene were calculated and the performance of each target gene for PCa diagnosis was evaluated using receiver-operating characteristics. PCA3 and AMACR mRNA quantification based on RT-qPCR may prove useful in PCa diagnosis. Of particular interesting, combining PCA3 and AMACR quantification improved PCa prediction by increasing sensitivity with retention of good specificity.

Keywords

References

  1. Auprich M, Bjartell A, Chun FKH, et al (2011). Contemporary role of prostate cancer Antigen 3 in the management of prostate cancer. Eur Urol, 60, 1045-54. https://doi.org/10.1016/j.eururo.2011.08.003
  2. Bussemakers MJ, van Bokhoven A, Verhaegh GW, et al (1999). DD3: A new prostate-specific gene, highly overexpressed in prostate cancer. Cancer Res, 59, 5975-9.
  3. Bustin SA, Nolan T (2004). Pitfalls of quantitative real-time reverse-transcription polymerase chain reaction. J Biomol Tech, 15, 155.
  4. de Kok JB, Verhaegh GW, Roelofs RW, et al (2002). DD3PCA3, a very sensitive and specific marker to detect prostate tumors. Cancer Res, 62, 2695-8.
  5. Dijkstra S, Mulders P, Schalken J (2014). Clinical use of novel urine and blood based prostate cancer biomarkers: a review. Clin Biochem, 47, 889-96. https://doi.org/10.1016/j.clinbiochem.2013.10.023
  6. Groskopf J, Aubin SM, Deras IL, et al (2006). APTIMA PCA3 molecular urine test: development of a method to aid in the diagnosis of prostate cancer. Clin Chem, 52, 1089-95. https://doi.org/10.1373/clinchem.2005.063289
  7. Hanley JA, McNeil BJ (1982). The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology, 143, 29-36. https://doi.org/10.1148/radiology.143.1.7063747
  8. Herawi M, Epstein JI (2007). Immunohistochemical antibody cocktail staining (p63/HMWCK/AMACR) of ductal adenocarcinoma and Gleason pattern 4 cribriform and noncribriform acinar adenocarcinomas of the prostate. Am J Surg Pathol, 31, 889-94. https://doi.org/10.1097/01.pas.0000213447.16526.7f
  9. Hessels D, Schalken JA (2009). The use of PCA3 in the diagnosis of prostate cancer. Nat Rev Urol, 6, 255-61. https://doi.org/10.1038/nrurol.2009.40
  10. Huggett J, Dheda K, Bustin S, et al (2005). Real-time RT-PCR normalisation; strategies and considerations. Genes Immun, 6, 279-84. https://doi.org/10.1038/sj.gene.6364190
  11. Jiang Z, Woda BA, Rock KL, et al (2001). P504S: a new molecular marker for the detection of prostate carcinoma. Am J Surg Pathol, 25, 1397-404. https://doi.org/10.1097/00000478-200111000-00007
  12. Kheirelseid EA, Chang KH, Newell J, et al (2010). Identification of endogenous control genes for normalisation of real-time quantitative PCR data in colorectal cancer. BMC Mol Biol, 11, 12. https://doi.org/10.1186/1471-2199-11-12
  13. Kilpelainen T, Tammela T, Maattanen L, et al (2010). False-positive screening results in the Finnish prostate cancer screening trial. Br J Cancer, 102, 469-74. https://doi.org/10.1038/sj.bjc.6605512
  14. Kusuda Y, Miyake H, Kurahashi T, et al (2013). Assessment of optimal target genes for detecting micrometastases in pelvic lymph nodes in patients with prostate cancer undergoing radical prostatectomy by real-time reverse transcriptasepolymerase chain reaction. Urologic Oncology: Seminars and Original Investigations, 2013. Elsevier, 615-21.
  15. Lie YS, Petropoulos CJ (1998). Advances in quantitative PCR technology: 5' nuclease assays. Curr Opin Biotechnol, 9, 43-8. https://doi.org/10.1016/S0958-1669(98)80082-7
  16. Magi-Galluzzi C, Luo J, Isaacs WB, et al (2003). $\alpha$-Methylacyl- CoA racemase: a variably sensitive immunohistochemical marker for the diagnosis of small prostate cancer foci on needle biopsy. Am J Surg Pathol, 27, 1128-33. https://doi.org/10.1097/00000478-200308000-00010
  17. Mane VP, Heuer MA, Hillyer P, et al (2008). Systematic method for determining an ideal housekeeping gene for real-time PCR analysis. J Biomol Tech, 19, 342.
  18. Mearini E, Antognelli C, Del Buono C, et al (2009). The combination of urine DD3PCA3 mRNA and PSA mRNA as molecular markers of prostate cancer. Biomarkers, 14, 235-43. https://doi.org/10.1080/13547500902807306
  19. Molinie V, Fromont G, Sibony M, et al (2004). Diagnostic utility of a p63/$\alpha$-methyl-CoA-racemase (p504s) cocktail in atypical foci in the prostate. Mod Pathol, 17, 1180-90. https://doi.org/10.1038/modpathol.3800197
  20. Mubiru JN, Shen-Ong GL, Valente AJ, et al (2004). Alternative spliced variants of the alpha-methylacyl-CoA racemase gene and their expression in prostate cancer. Gene, 327, 89-98. https://doi.org/10.1016/j.gene.2003.11.009
  21. Mubiru JN, Valente AJ, Troyer DA (2005). A Variant of the alpha-methyl-acyl-CoA racemase gene created by a deletion in exon 5 and its expression in prostate cancer. Prostate, 65, 117-23. https://doi.org/10.1002/pros.20277
  22. Nadiminty N, Tummala R, Liu C, et al (2015). NF-${\kappa}B2$/p52: c-Myc: HnRNPA1 pathway regulates expression of androgen receptor splice variants and enzalutamide sensitivity in prostate cancer. Mol Cancer Biol, 14, 1884-95.
  23. Ohl F, Jung M, Radonic A, et al (2006). Identification and validation of suitable endogenous reference genes for gene expression studies of human bladder cancer. J Urol, 175, 1915-20. https://doi.org/10.1016/S0022-5347(05)00919-5
  24. Ouyang B, Bracken B, Burke B, et al (2009). A duplex quantitative polymerase chain reaction assay based on quantification of alpha-methylacyl-CoA racemase transcripts and prostate cancer antigen 3 in urine sediments improved diagnostic accuracy for prostate cancer. J Urol, 181, 2508-13. https://doi.org/10.1016/j.juro.2009.01.110
  25. Perkins NJ, Schisterman EF (2006). The inconsistency of "optimal" cutpoints obtained using two criteria based on the receiver operating characteristic curve. Am J Epidemiol, 163, 670-5. https://doi.org/10.1093/aje/kwj063
  26. Ploussard G, Haese A, Van Poppel H, et al (2010). The prostate cancer gene 3 (PCA3) urine test in men with previous negative biopsies: does free-to-total prostate-specific antigen ratio influence the performance of the PCA3 score in predicting positive biopsies?. BJU Int, 106, 1143-7. https://doi.org/10.1111/j.1464-410X.2010.09286.x
  27. Radonic A, Thulke S, Mackay IM, et al (2004). Guideline to reference gene selection for quantitative real-time PCR. Biochem Biophys Res Commun, 313, 856-62. https://doi.org/10.1016/j.bbrc.2003.11.177
  28. Rittenhouse H, Blase A, Shamel B, et al (2013). The long and winding road to FDA approval of a novel prostate cancer test: our story. Clin Chem, 59, 32-4. https://doi.org/10.1373/clinchem.2012.198739
  29. Rubin MA, Zhou M, Dhanasekaran SM, et al (2002). $\alpha$-Methylacyl coenzyme A racemase as a tissue biomarker for prostate cancer. JAMA, 287, 1662-70. https://doi.org/10.1001/jama.287.13.1662
  30. Smith DS, Catalona WJ, Herschman JD (1996). Longitudinal screening for prostate cancer with prostate-specific antigen. JAMA, 276, 1309-15. https://doi.org/10.1001/jama.1996.03540160031029
  31. Span PN, Manders P, Heuvel JJ, et al (2002). Expression of the transcription factor Ets-1 is an independent prognostic marker for relapse-free survival in breast cancer. Oncogene, 21, 8506-9. https://doi.org/10.1038/sj.onc.1206040
  32. Vandesompele J, De Preter K, Pattyn F, et al (2002). Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biol, 3, 34.
  33. Zheng SL, Chang Bl, Faith DA, et al (2002). Sequence variants of $\alpha$-methylacyl-CoA racemase are associated with prostate cancer risk. Cancer Res, 62, 6485-8.