Geometric Mean of 5S rRNA and MiR-16 As A Suitable Normalizer In Esophageal Cancer

Journal of Cell and Molecular Research

مقاله 2، دوره 8، شماره 2 - شماره پیاپی 16، اسفند 2016، صفحه 52-57   

نوع مقاله: Research Articles

شناسه دیجیتال (DOI): 10.22067/jcmr.v8i2.56251

نویسندگان

Samaneh Khazaei^{* 1}؛ Sedigheh Gharbi²؛ Seyed Javad Mowla³

¹Isfahan University

²shahid Bahonar university

³Tarbiat modares university

چکیده

Esophageal squamous cell carcinoma (ESCC) is a deadly cancer with poor prognosis. In this regard, early diagnosis is of vital importance to cure the tumor in its early stages. Novel cancer diagnostic and therapeutic approaches have been recently introduced based on microRNAs (miRNAs). Also, accurate normalization using appropriate reference genes is a critical step in miRNA expression studies. In this study, we aimed to identify appropriate reference genes for miRNA quantification in serum samples of ESCC. In this case and control experimental study, two statistical algorithms including GeNorm and NormFinder were used to evaluate the suitability of miR-16 and 5S rRNA and their geometric mean as reference genes. Then, relative expression of miR-451 and miR-24 were evaluated while different normalizer including miR-16, 5S rRNA and their geometric mean were applied. Both GeNorm and NormFinder analyses showed that geometric mean of miR-16 and 5S rRNA is the most stable reference gene in these samples. Also, our data showed that choosing an inappropriate normalizer could change the relative expression of target genes of miR-451 and miR-24 in ESCC samples which emphasize on the importance of selecting a reliable internal control in expression analyses. We demonstrated that geometric mean of two reference genes could increase the reliability of normalizers and also by using geometric mean as reference gene, relative expression of different target is closer to reality.

کلیدواژه‌ها

Esophageal cancer؛ microRNA؛ qRT-PCR؛ Reference genes

مراجع

1. Chang K. H., Mestdagh P., Vandesompele J., Kerin M. J. and Miller N. (2010) MicroRNA expression profiling to identify and validate reference genes for relative quantification in colorectal cancer. BMC Cancer 10:173.

2. Chen C.-Z., Li L., Lodish H. F. and Bartel D. P. (2004) MicroRNAs modulate hematopoietic lineage differentiation. Science 303:83-86.

3. Croce C. M. and Calin G. A. (2005) miRNAs, cancer, and stem cell division. Cell 122:6-7.

4. Das M. K., Andreassen R., Haugen T. B. and Furu K. (2016) Identification of Endogenous Controls for Use in miRNA Quantification in Human Cancer Cell Lines. Cancer Genomics and Proteomics 13:63-68.

5. Engels B. M. and Hutvagner G. (2006) Principles and effects of microRNA-mediated post-transcriptional gene regulation. Oncogene 25:6163-6169.

6. Esquela-Kerscher A. and Slack F. J. (2006) Oncomirs—microRNAs with a role in cancer. Nature Reviews Cancer 6:259-269.

7. Ferdous J., Li Y., Reid N., Langridge P., Shi B. J. and Tricker P. J. (2015) Identification of reference genes for quantitative expression analysis of microRNAs and mRNAs in barley under various stress conditions. PloS One 10:e0118503.

8. Fitzmaurice C., Dicker D., Pain A., Hamavid H., Moradi-Lakeh M., MacIntyre M. F., Allen C., Hansen G., Woodbrook R. and Wolfe C. (2015) The global burden of cancer 2013. JAMA Oncology 1:505-527.

9. Gharbi S., Mirzadeh F., Khatrei S., Soroush M. R., Tavallaie M., Nourani M. R. and Mowla S. J. (2014) Optimizing microRNA quantification in serum samples. Journal of Cell and Molecular Research 6:52-56.

10. Gharbi S., Shamsara M., Khateri S., Soroush M. R., Ghorbanmehr N., Tavallaei M., Nourani M. R. and Mowla S. J. (2015) Identification of reliable reference genes for quantification of microRNAs in serum samples of sulfur mustard-exposed veterans. Cell Journal (Yakhteh) 17:494.

11. Gu Y.-Q., Gong G., Xu Z.-L., Wang L.-Y., Fang M.-L., Zhou H., Xing H., Wang K.-R. and Sun L. (2014) miRNA profiling reveals a potential role of milk stasis in breast carcinogenesis. International Journal of Molecular Medicine 33:1243-1249.

12. Islami F., Kamangar F., Nasrollahzadeh D., Møller H., Boffetta P. and Malekzadeh R. (2009) Oesophageal cancer in Golestan Province, a high-incidence area in northern Iran–A review. European Journal of Cancer 45:3156-3165.

13. Lai E. C. (2002) Micro RNAs are complementary to 3 [variant prime] UTR sequence motifs that mediate negative post-transcriptional regulation. Nature Genetics 30:363.

14. Lim Q., Zhou L., Ho Y., Wan G. and Too H. (2011) snoU6 and 5S RNAs are not reliable miRNA reference genes in neuronal differentiation. Neuroscience 199:32-43.

15. Liu X., Zhang L., Cheng K., Wang X., Ren G. and Xie P. (2014) Identification of suitable plasma-based reference genes for miRNAome analysis of major depressive disorder. Journal of Affective Disorders 163:133-139.

16. Lu J., Getz G., Miska E. A., Alvarez-Saavedra E., Lamb J., Peck D., Sweet-Cordero A., Ebert B. L., Mak R. H. and Ferrando A. A. (2005) MicroRNA expression profiles classify human cancers. Nature 435:834-838.

17. Monroig P. d. C. and Calin G. A. (2013) MicroRNA and epigenetics: diagnostic and therapeutic opportunities. Current Pathobiology Reports 1:43-52.

18. Murata K., Furu M., Yoshitomi H., Ishikawa M., Shibuya H., Hashimoto M., Imura Y., Fujii T., Ito H. and Mimori T. (2013) Comprehensive microRNA analysis identifies miR-24 and miR-125a-5p as plasma biomarkers for rheumatoid arthritis. PloS One 8:e69118.

19. Peltier H. J. and Latham G. J. (2008) Normalization of microRNA expression levels in quantitative RT-PCR assays: identification of suitable reference RNA targets in normal and cancerous human solid tissues. RNA 14:844-852.

20. Pritchard C. C., Cheng H. H. and Tewari M. (2012) MicroRNA profiling: approaches and considerations. Nature Reviews Genetics 13:358-369.

21. Schaefer A., Jung M., Miller K., Lein M., Kristiansen G., Erbersdobler A. and Jung K. (2010) Suitable reference genes for relative quantification of miRNA expression in prostate cancer. Experimental and Molecular Medicine 42:749-758.

22. Shen Y., Li Y., Ye F., Wang F., Wan X., Lu W. and Xie X. (2011) Identification of miR-23a as a novel microRNA normalizer for relative quantification in human uterine cervical tissues. Experimental and Molecular Medicine 43:358-366.

23. Vandesompele J., De Preter K., Pattyn F., Poppe B., Van Roy N., De Paepe A. and Speleman F. (2002) Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biology 3: research0034. 0031.

24. Xu Q., Ma P., Hu C., Chen L., Xue L., Wang Z., Liu M., Zhu H., Xu N. and Lu N. (2012) Overexpression of the DEC1 protein induces senescence in vitro and is related to better survival in esophageal squamous cell carcinoma. PloS One 7:e41862.

25. Zhang Y. (2013) Epidemiology of esophageal cancer. World Journal of Gastroenterology 19.

26. Zhu C., Ren C., Han J., Ding Y., Du J., Dai N., Dai J., Ma H., Hu Z. and Shen H. (2014) A five-microRNA panel in plasma was identified as potential biomarker for early detection of gastric cancer. British Journal of Cancer 110:2291-2299.