Molecular Biotechnology

, Volume 50, Issue 1, pp 49–56 | Cite as

Identification of Suitable Reference Genes for qRT-PCR Analysis of Circulating microRNAs in Hepatitis B Virus-Infected Patients

  • Hai-Tao Zhu
  • Qiong-Zhu Dong
  • Guan Wang
  • Hai-Jun Zhou
  • Ning Ren
  • Hu-Liang Jia
  • Qing-Hai Ye
  • Lun-Xiu Qin
Original Paper

Abstract

Circulating microRNAs (miRNAs) were found to exist in serum/plasma in a highly stable, cell-free form, and aberrantly expressed in many human diseases. Currently, the expression levels of circulating miRNAs are estimated by quantitative real-time polymerase chain reaction. However, no study has systematically evaluated reference genes for evaluating circulating microRNA expression. This study describes the identification and characterization of an appropriate reference gene for the normalization of circulating miRNA levels in hepatitis B virus (HBV)-infected patients and healthy people. Ten miRNAs that resemble the mean expression of the TaqMan low density array together with U6, RNU6B, and miR-16 were validated with two algorithms, geNorm, and NormFinder, after ensuring their equivalent expression between the two study groups. The combination of miR-26a, miR-221, and miR-22* is recommended as the most stable set of reference genes for circulating miRNA evaluation in HBV patients and healthy people.

Keywords

Hepatitis B virus Circulating microRNA Quantitative real-time PCR Reference genes Normalization 

Abbreviations

Ct

Cycle threshold

HBV

Hepatitis B virus

HCV

Hepatitis C virus

qRT-PCR

Quantitative reverse-transcription polymerase chain reaction

Notes

Acknowledgments

This article was supported by grants from the National Key Projects for Infectious Disease of China (2008ZX10002-021), the Program of Shanghai Chief Scientist (08XD1400800), the National Natural Science Foundation of China (30700991), and the Research Fund for New Teachers of Fudan University.

Supplementary material

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12033_2011_9414_MOESM7_ESM.tif (12 mb)
Fig. S1 a Real-time PCR amplification plots for the miR-22* assay using a dilution series of known input amounts of synthetic miR-22* (Invitrogen, China; from left to right: 1.0E + 12 to 1.0E + 4 copies). Each graph represents amplification [presented on the y-axis as Rn: the fluorescence emission intensity of the reporter dye (FAM) normalized to the passive reference dye (ROX)] plotted against cycle number (presented on the x-axis as the cycle at which fluorescence was detected above an automatically determined threshold) for miR-22*. The curves represent technical replicates (in triplicate or duplicate) of real-time PCR. b Standard curve for miR-22* TaqMan qRT-PCR assays. Standard curves were generated for the miR-22* assay by using a dilution series of known input amounts of synthetic miR-22* corresponding to the target of the assay (Table 1). The dilution series samples were run using common RT and PCR enzyme master mixes and on the same plate as experimental samples. The standard curve revealed that the Ct values between 14 and 37 were reliable, stable in replicate, and quantitative in this assay. (TIFF 12276 kb)

References

  1. 1.
    Esquela-Kerscher, A., & Slack, F. J. (2006). Oncomirs: MicroRNAs with a role in cancer. Nature Reviews Cancer, 6, 259–269.CrossRefGoogle Scholar
  2. 2.
    Zhang, S., Chen, L., Jung, E. J., & Calin, G. A. (2010). Targeting MicroRNAs with small molecules: from dream to reality. Clinical Pharmacology and Therapeutics, 87, 754–758.CrossRefGoogle Scholar
  3. 3.
    Miller, B. H., & Wahlestedt, C. (2010). MicroRNA dysregulation in psychiatric disease. Brain Research, 1338, 78–88.CrossRefGoogle Scholar
  4. 4.
    Zhang, Y., Jia, Y., Zheng, R., Guo, Y., Wang, Y., Guo, H., et al. (2010). Plasma microRNA-122 as a biomarker for viral-, alcohol-, and chemical-related hepatic diseases. Clinical Chemistry, 56, 1830–1838.CrossRefGoogle Scholar
  5. 5.
    Heneghan, H. M., Miller, N., Lowery, A. J., Sweeney, K. J., Newell, J., & Kerin, M. J. (2010). Circulating microRNAs as novel minimally invasive biomarkers for breast cancer. Ann Surg, 251(3), 499–505.CrossRefGoogle Scholar
  6. 6.
    Huang, Z., Huang, D., Ni, S., Peng, Z., Sheng, W., & Du, X. (2009). Plasma microRNAs are promising novel biomarkers for early detection of colorectal cancer. International Journal of Cancer, 127, 118–126.CrossRefGoogle Scholar
  7. 7.
    Tijsen, A. J., Creemers, E. E., Moerland, P. D., de Windt, L. J., van der Wal, A. C., Kok, W. E., et al. (2010). MiR423–5p as a circulating biomarker for heart failure. Circulation Research, 106, 1035–1039.CrossRefGoogle Scholar
  8. 8.
    Wang, G. K., Zhu, J. Q., Zhang, J. T., Li, Q., Li, Y., He, J., et al. (2010). Circulating microRNA: a novel potential biomarker for early diagnosis of acute myocardial infarction in humans. European Heart Journal, 31, 659–666.CrossRefGoogle Scholar
  9. 9.
    Wang, K., Zhang, S., Marzolf, B., Troisch, P., Brightman, A., Hu, Z., et al. (2009). Circulating microRNAs, potential biomarkers for drug-induced liver injury. Proc Natl Acad Sci USA, 106, 4402–4407.CrossRefGoogle Scholar
  10. 10.
    Kosaka, N., Iguchi, H., & Ochiya, T. (2010). Circulating microRNA in body fluid: a new potential biomarker for cancer diagnosis and prognosis. Cancer Sci, 101, 2087–2092.CrossRefGoogle Scholar
  11. 11.
    Ho, A. S., Huang, X., Cao, H., Christman-Skieller, C., Bennewith, K., Le, Q. T., et al. (2010). Circulating miR-210 as a novel hypoxia marker in pancreatic cancer. Translational Oncology, 3, 109–113.Google Scholar
  12. 12.
    Tsujiura, M., Ichikawa, D., Komatsu, S., Shiozaki, A., Takeshita, H., Kosuga, T., et al. (2010). Circulating microRNAs in plasma of patients with gastric cancers. British Journal of Cancer, 102, 1174–1179.CrossRefGoogle Scholar
  13. 13.
    Cheng, Y., Tan, N., Yang, J., Liu, X., Cao, X., He, P., et al. (2010). A translational study of circulating cell-free microRNA-1 in acute myocardial infarction. Clinical Science (London), 119, 87–95.CrossRefGoogle Scholar
  14. 14.
    Heneghan, H. M., Miller, N., Lowery, A. J., Sweeney, K. J., Newell, J., & Kerin, M. J. (2010). Circulating microRNAs as novel minimally invasive biomarkers for breast cancer. Annals of Surgery, 251, 499–505.CrossRefGoogle Scholar
  15. 15.
    Ai, J., Zhang, R., Li, Y., Pu, J., Lu, Y., Jiao, J., et al. (2010). Circulating microRNA-1 as a potential novel biomarker for acute myocardial infarction. Biochemical and Biophysical Research Communications, 391, 73–77.CrossRefGoogle Scholar
  16. 16.
    Huang, Z., Huang, D., Ni, S., Peng, Z., Sheng, W., & Du, X. (2010). Plasma microRNAs are promising novel biomarkers for early detection of colorectal cancer. International Journal of Cancer, 127, 118–126.CrossRefGoogle Scholar
  17. 17.
    Zhu, W., Qin, W., Atasoy, U., & Sauter, E. R. (2009). Circulating microRNAs in breast cancer and healthy subjects. BMC Res Notes, 2, 89.CrossRefGoogle Scholar
  18. 18.
    Ng, E. K., Chong, W. W., Jin, H., Lam, E. K., Shin, V. Y., Yu, J., et al. (2009). Differential expression of microRNAs in plasma of patients with colorectal cancer: a potential marker for colorectal cancer screening. Gut, 58, 1375–1381.CrossRefGoogle Scholar
  19. 19.
    Mitchell, P. S., Parkin, R. K., Kroh, E. M., Fritz, B. R., Wyman, S. K., Pogosova-Agadjanyan, E. L., et al. (2008). Circulating microRNAs as stable blood-based markers for cancer detection. Proc Natl Acad Sci USA, 105, 10513–10518.CrossRefGoogle Scholar
  20. 20.
    Lawrie, C. H., Gal, S., Dunlop, H. M., Pushkaran, B., Liggins, A. P., Pulford, K., et al. (2008). Detection of elevated levels of tumour-associated microRNAs in serum of patients with diffuse large B-cell lymphoma. British Journal Haematology, 141, 672–675.CrossRefGoogle Scholar
  21. 21.
    Mestdagh, P., Van Vlierberghe, P., De Weer, A., Muth, D., Westermann, F., Speleman, F., et al. (2009). A novel and universal method for microRNA RT-qPCR data normalization. Genome Biology, 10, R64.CrossRefGoogle Scholar
  22. 22.
    Lagos-Quintana, M., Rauhut, R., Yalcin, A., Meyer, J., Lendeckel, W., & Tuschl, T. (2002). Identification of tissue-specific microRNAs from mouse. Current Biology, 12, 735–739.CrossRefGoogle Scholar
  23. 23.
    Chang, J., Nicolas, E., Marks, D., Sander, C., Lerro, A., Buendia, M. A., et al. (2004). miR-122, a mammalian liver-specific microRNA, is processed from hcr mRNA and may downregulate the high affinity cationic amino acid transporter CAT-1. RNA Biol, 1, 106–113.CrossRefGoogle Scholar
  24. 24.
    Jopling, C. L., Yi, M., Lancaster, A. M., Lemon, S. M., & Sarnow, P. (2005). Modulation of hepatitis C virus RNA abundance by a liver-specific MicroRNA. Science, 309, 1577–1581.CrossRefGoogle Scholar
  25. 25.
    Vandesompele J, De Preter K, Pattyn F, Poppe B, Van Roy N, De Paepe A, Speleman F. (2002). Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biol 3: RESEARCH0034.1–RESEARCH0034.11.Google Scholar
  26. 26.
    Andersen, C. L., Jensen, J. L., & Orntoft, T. F. (2004). Normalization of real-time quantitative reverse transcription-PCR data: a model-based variance estimation approach to identify genes suited for normalization, applied to bladder and colon cancer data sets. Cancer Research, 64, 5245–5250.CrossRefGoogle Scholar
  27. 27.
    Haller, F., Kulle, B., Schwager, S., Gunawan, B., von Heydebreck, A., Sultmann, H., et al. (2004). Equivalence test in quantitative reverse transcription polymerase chain reaction: confirmation of reference genes suitable for normalization. Analytical Biochemistry, 335, 1–9.CrossRefGoogle Scholar
  28. 28.
    Ura, S., Honda, M., Yamashita, T., Ueda, T., Takatori, H., Nishino, R., et al. (2009). Differential microRNA expression between hepatitis B and hepatitis C leading disease progression to hepatocellular carcinoma. Hepatology, 49, 1098–1112.CrossRefGoogle Scholar
  29. 29.
    Qiu, L., Fan, H., Jin, W., Zhao, B., Wang, Y., Ju, Y., et al. (2010). miR-122-induced down-regulation of HO-1 negatively affects miR-122-mediated suppression of HBV. Biochemical and Biophysical Research Communications, 398, 771–777.CrossRefGoogle Scholar
  30. 30.
    Derveaux, S., Vandesompele, J., & Hellemans, J. (2010). How to do successful gene expression analysis using real-time PCR. Methods, 50, 227–230.CrossRefGoogle Scholar
  31. 31.
    Peltier, H. J., & 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.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Hai-Tao Zhu
    • 1
    • 2
  • Qiong-Zhu Dong
    • 1
    • 2
  • Guan Wang
    • 1
    • 2
  • Hai-Jun Zhou
    • 1
    • 2
  • Ning Ren
    • 1
    • 2
  • Hu-Liang Jia
    • 1
    • 2
  • Qing-Hai Ye
    • 1
    • 2
  • Lun-Xiu Qin
    • 1
    • 2
  1. 1.Live Cancer Institute and Zhongshan Hospital, Institutes of Biomedical SciencesFudan UniversityShanghaiChina
  2. 2.Key Laboratory of Carcinogenesis and Cancer InvasionMinistry of EducationShanghaiChina

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