Skip to main content

Advertisement

Log in

MicroRNA expression studies: challenge of selecting reliable reference controls for data normalization

  • Review
  • Published:
Cellular and Molecular Life Sciences Aims and scope Submit manuscript

Abstract

Accurate determination of microRNA expression levels is a prerequisite in using these small non-coding RNA molecules as novel biomarkers in disease diagnosis and prognosis. Quantitative PCR is the method of choice for measuring the expression levels of microRNAs. However, a major obstacle that affects the reliability of results is the lack of validated reference controls for data normalization. Various non-coding RNAs have previously been used as reference controls, but their use may lead to variations and lack of comparability of microRNA data among the studies. Despite the growing number of studies investigating microRNA profiles to discriminate between healthy and disease stages, robust reference controls for data normalization have so far not been established. In the present article, we provide an overview of different reference controls used in various diseases, and highlight the urgent need for the identification of suitable reference controls to produce reliable data. Our analysis shows, among others, that RNU6 is not an ideal normalizer in studies using patient material from different diseases. Finally, our article tries to disclose the challenges to find a reference control which is uniformly and stably expressed across all body tissues, fluids, and diseases.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

References

  1. Bartel DP (2004) MicroRNAs: genomics, biogenesis, mechanism, and function. Cell 116(2):281–297

    Article  CAS  Google Scholar 

  2. Sierzega M, Kaczor M, Kolodziejczyk P, Kulig J, Sanak M, Richter P (2017) Evaluation of serum microRNA biomarkers for gastric cancer based on blood and tissue pools profiling: the importance of miR-21 and miR-331. Br J Cancer 117(2):266

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Lin H-M, Mahon KL, Spielman C, Gurney H, Mallesara G, Stockler MR et al (2017) Phase 2 study of circulating microRNA biomarkers in castration-resistant prostate cancer. Br J Cancer 116(8):1002

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Kahraman M, Röske A, Laufer T, Fehlmann T, Backes C, Kern F et al (2018) MicroRNA in diagnosis and therapy monitoring of early-stage triple-negative breast cancer. Sci Rep 8(1):11584

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Schwarzenbach H, Nishida N, Calin GA, Pantel K (2014) Clinical relevance of circulating cell-free microRNAs in cancer. Nat Rev Clin Oncol 11(3):145

    Article  CAS  PubMed  Google Scholar 

  6. Cheng L, Doecke JD, Sharples R, Villemagne VL, Fowler CJ, Rembach A et al (2015) Prognostic serum miRNA biomarkers associated with Alzheimer’s disease shows concordance with neuropsychological and neuroimaging assessment. Mol Psychiatry 20(10):1188

    Article  CAS  PubMed  Google Scholar 

  7. Roser AE, Gomes LC, Schünemann J, Maass F, Lingor P (2018) Circulating miRNAs as diagnostic biomarkers for Parkinson’s disease. Front Neurosci 12:625

    Article  PubMed  PubMed Central  Google Scholar 

  8. Zhou S-S, Jin J-P, Wang J-Q, Zhang Z-G, Freedman JH, Zheng Y et al (2018) miRNAS in cardiovascular diseases: potential biomarkers, therapeutic targets and challenges. Acta Pharmacol Sin 39(7):1073

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Li H, Liu J, Chen J, Wang H, Yang L, Chen F et al (2018) A serum microRNA signature predicts trastuzumab benefit in HER2-positive metastatic breast cancer patients. Nat Commun 9(1):1614

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Bartels CL, Tsongalis GJ (2009) MicroRNAs: novel biomarkers for human cancer. Clin Chem 55(4):623–631

    Article  CAS  PubMed  Google Scholar 

  11. Schmittgen TD, Livak KJ (2008) Analyzing real-time PCR data by the comparative C T method. Nat Protoc 3(6):1101

    Article  CAS  PubMed  Google Scholar 

  12. Bustin SA, Benes V, Garson JA, Hellemans J, Huggett J, Kubista M et al (2009) The MIQE guidelines: minimum information for publication of quantitative real-time PCR experiments. Clin Chem 55(4):611–622

    Article  CAS  PubMed  Google Scholar 

  13. Schwarzenbach H, da Silva AM, Calin G, Pantel K (2015) Data normalization strategies for microRNA quantification. Clin Chem 61(11):1333–1342

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Hong TH, Park IY (2014) MicroRNA expression profiling of diagnostic needle aspirates from surgical pancreatic cancer specimens. Ann Surg Treat Res 87(6):290–297

    Article  PubMed  PubMed Central  Google Scholar 

  15. Du Rieu MC, Torrisani J, Selves J, Al Saati T, Souque A, Dufresne M et al (2010) MicroRNA-21 is induced early in pancreatic ductal adenocarcinoma precursor lesions. Clin Chem 56(4):603–612

    Article  CAS  PubMed  Google Scholar 

  16. Tiberio P, Callari M, Angeloni V, Daidone MG, Appierto V (2015) Challenges in using circulating miRNAs as cancer biomarkers. BioMed Res Int 2015:731479

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Schwarzenbach H (2017) Methods for quantification and characterization of microRNAs in cell-free plasma/serum, normal exosomes and tumor-derived exosomes. Transl Cancer Res 7(2):S253–S263

    Google Scholar 

  18. Ibberson D, Benes V, Muckenthaler MU, Castoldi M (2009) RNA degradation compromises the reliability of microRNA expression profiling. BMC Biotechnol 9(1):102

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Shingara J, Keiger K, Shelton J, Laosinchai-Wolf W, Powers P, Conrad R et al (2005) An optimized isolation and labeling platform for accurate microRNA expression profiling. RNA 11(9):1461–1470

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Kim D-J, Linnstaedt S, Palma J, Park JC, Ntrivalas E, Kwak-Kim JY et al (2012) Plasma components affect accuracy of circulating cancer-related microRNA quantitation. J Mol Diagn 14(1):71–80

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Benes V, Castoldi M (2010) Expression profiling of microRNA using real-time quantitative PCR, how to use it and what is available. Methods 50(4):244–249

    Article  CAS  PubMed  Google Scholar 

  22. Pritchard CC, Cheng HH, Tewari M (2012) MicroRNA profiling: approaches and considerations. Nat Rev Genet 13(5):358

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Haider BA, Baras AS, McCall MN, Hertel JA, Cornish TC, Halushka MK (2014) A critical evaluation of microRNA biomarkers in non-neoplastic disease. PLoS One 9(2):e89565

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Marabita F, de Candia P, Torri A, Tegner J, Abrignani S, Rossi RL (2015) Normalization of circulating microRNA expression data obtained by quantitative real-time RT-PCR. Brief Bioinform 17(2):204–212

    Article  PubMed  PubMed Central  Google Scholar 

  25. Kiss T (2002) Small nucleolar RNAs: an abundant group of noncoding RNAs with diverse cellular functions. Cell 109(2):145–148

    Article  CAS  PubMed  Google Scholar 

  26. Matera AG, Terns RM, Terns MP (2007) Non-coding RNAs: lessons from the small nuclear and small nucleolar RNAs. Nat Rev Mol Cell Biol 8(3):209

    Article  CAS  PubMed  Google Scholar 

  27. Qi R, Weiland M, Gao XH, Zhou L, Mi QS (2012) Identification of endogenous normalizers for serum microRNAs by microarray profiling: U6 small nuclear RNA is not a reliable normalizer. Hepatology 55(5):1640–1642

    Article  PubMed  Google Scholar 

  28. Zhu H-T, Dong Q-Z, Wang G, Zhou H-J, Ren N, Jia H-L et al (2012) Identification of suitable reference genes for qRT-PCR analysis of circulating microRNAs in hepatitis B virus-infected patients. Mol Biotechnol 50(1):49–56

    Article  CAS  PubMed  Google Scholar 

  29. Li Y, Zhang L, Liu F, Xiang G, Jiang D, Pu X (2015) Identification of endogenous controls for analyzing serum exosomal miRNA in patients with hepatitis B or hepatocellular carcinoma. Dis Markers. https://doi.org/10.1155/2015/893594

    Article  PubMed  PubMed Central  Google Scholar 

  30. Tang G, Shen X, Lv K, Wu Y, Bi J, Shen Q (2015) Different normalization strategies might cause inconsistent variation in circulating microRNAs in patients with hepatocellular carcinoma. Med Sci Monit 21:617

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Benz F, Roderburg C, Cardenas DV, Vucur M, Gautheron J, Koch A et al (2013) U6 is unsuitable for normalization of serum miRNA levels in patients with sepsis or liver fibrosis. Exp Mol Med 45(9):e42

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Madadi S, Soleimani M (2019) The crucial need of internal control validation in the normalization of circulating microRNAs. Dig Liver Dis 51:610–611

    Article  PubMed  Google Scholar 

  33. Lange T, Stracke S, Rettig R, Lendeckel U, Kuhn J, Schlüter R et al (2017) Identification of miR-16 as an endogenous reference gene for the normalization of urinary exosomal miRNA expression data from CKD patients. PLoS One 12(8):e0183435

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Ling T-Y, Wang X-L, Chai Q, Lau T-W, Koestler CM, Park SJ et al (2013) Regulation of the SK3 channel by microRNA-499—potential role in atrial fibrillation. Heart Rhythm 10(7):1001–1009

    Article  PubMed  PubMed Central  Google Scholar 

  35. Masè M, Grasso M, Avogaro L, D’Amato E, Tessarolo F, Graffigna A et al (2017) Selection of reference genes is critical for miRNA expression analysis in human cardiac tissue. A focus on atrial fibrillation. Sci Rep 7:41127

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Zhang Y, Tang W, Peng L, Tang J, Yuan Z (2016) Identification and validation of microRNAs as endogenous controls for quantitative polymerase chain reaction in plasma for stable coronary artery disease. Cardiol J 23(6):694–703

    Article  PubMed  Google Scholar 

  37. Wang X, Zhang X, Yuan J, Wu J, Deng X, Peng J et al (2018) Evaluation of the performance of serum miRNAs as normalizers in microRNA studies focused on cardiovascular disease. J Thorac Dis 10(5):2599–2607

    Article  PubMed  PubMed Central  Google Scholar 

  38. Solayman MHM, Langaee T, Patel A, El-Wakeel L, El-Hamamsy M, Badary O et al (2016) Identification of suitable endogenous normalizers for qRT-PCR analysis of plasma microRNA expression in essential hypertension. Mol Biotechnol 58(3):179–187

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Barry SE, Chan B, Ellis M, Yang Y, Plit ML, Guan G et al (2015) Identification of miR-93 as a suitable miR for normalizing miRNA in plasma of tuberculosis patients. J Cell Mol Med 19(7):1606–1613

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Liu X, Zhang L, Cheng K, Wang X, Ren G, Xie P (2014) Identification of suitable plasma-based reference genes for miRNAome analysis of major depressive disorder. J Affect Disord 163:133–139

    Article  CAS  PubMed  Google Scholar 

  41. Serafin A, Foco L, Blankenburg H, Picard A, Zanigni S, Zanon A et al (2014) Identification of a set of endogenous reference genes for miRNA expression studies in Parkinson’s disease blood samples. BMC Res Notes 7(1):715

    Article  PubMed  PubMed Central  Google Scholar 

  42. Margis R, Margis R, Rieder CR (2011) Identification of blood microRNAs associated to Parkinsońs disease. J Biotechnol 152(3):96–101

    Article  CAS  PubMed  Google Scholar 

  43. Martins M, Rosa A, Guedes LC, Fonseca BV, Gotovac K, Violante S et al (2011) Convergence of miRNA expression profiling, α-synuclein interacton, and GWAS in Parkinson’s disease. PLoS One 6(10):e25443

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Eriksen AHM, Andersen RF, Pallisgaard N, Sørensen FB, Jakobsen A, Hansen TF (2016) MicroRNA expression profiling to identify and validate reference genes for the relative quantification of microRNA in rectal cancer. PLoS One 11(3):e0150593

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Zheng G, Wang H, Zhang X, Yang Y, Wang L, Du L et al (2013) Identification and validation of reference genes for qPCR detection of serum microRNAs in colorectal adenocarcinoma patients. PLoS One 8(12):e83025

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Iwaya T, Yokobori T, Nishida N, Kogo R, Sudo T, Tanaka F et al (2012) Downregulation of miR-144 is associated with colorectal cancer progression via activation of mTOR signaling pathway. Carcinogenesis 33(12):2391–2397

    Article  CAS  PubMed  Google Scholar 

  47. Niu Y, Wu Y, Huang J, Li Q, Kang K, Qu J et al (2016) Identification of reference genes for circulating microRNA analysis in colorectal cancer. Sci Rep 6:35611

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Bandrés E, Cubedo E, Agirre X, Malumbres R, Zarate R, Ramirez N et al (2006) Identification by Real-time PCR of 13 mature microRNAs differentially expressed in colorectal cancer and non-tumoral tissues. Mol Cancer 5(1):29

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Madadi S, Soleimani M (2019) Comparison of miR-16 and cel-miR-39 as reference controls for serum miRNA normalization in colorectal cancer. J Cell Biochem 120:4802–4803

    Article  CAS  PubMed  Google Scholar 

  51. Danese E, Minicozzi A, Benati M, Paviati E, Lima-Oliveira G, Gusella M et al (2017) Reference miRNAs for colorectal cancer: analysis and verification of current data. Sci Rep 7(1):8413

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. McDermott AM, Kerin MJ, Miller N (2013) Identification and validation of miRNAs as endogenous controls for RQ-PCR in blood specimens for breast cancer studies. PLoS One 8(12):e83718

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Davoren PA, McNeill RE, Lowery AJ, Kerin MJ, Miller N (2008) Identification of suitable endogenous control genes for microRNA gene expression analysis in human breast cancer. BMC Mol Biol 9(1):76

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Appaiah HN, Goswami CP, Mina LA, Badve S, Sledge GW, Liu Y et al (2011) Persistent upregulation of U6: SNORD44 small RNA ratio in the serum of breast cancer patients. Breast Cancer Res 13(5):R86

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Stückrath I, Rack B, Janni W, Jäger B, Pantel K, Schwarzenbach H (2015) Aberrant plasma levels of circulating miR-16, miR-107, miR-130a and miR-146a are associated with lymph node metastasis and receptor status of breast cancer patients. Oncotarget 6(15):13387

    Article  PubMed  PubMed Central  Google Scholar 

  56. Madadi S, Soleimani M (2019) Evaluation of miR-16 as an internal control in the patients with breast cancer. Hum Pathol 85:329

    Article  CAS  PubMed  Google Scholar 

  57. McDermott AM, Miller N, Wall D, Martyn LM, Ball G, Sweeney KJ et al (2014) Identification and validation of oncologic miRNA biomarkers for luminal A-like breast cancer. PLoS One 9(1):e87032

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Lou G, Ma N, Xu Y, Jiang L, Yang J, Wang C et al (2015) Differential distribution of U6 (RNU6-1) expression in human carcinoma tissues demonstrates the requirement for caution in the internal control gene selection for microRNA quantification. Int J Mol Med 36(5):1400–1408

    Article  CAS  PubMed  Google Scholar 

  59. Gee H, Buffa F, Camps C, Ramachandran A, Leek R, Taylor M et al (2011) The small-nucleolar RNAs commonly used for microRNA normalisation correlate with tumour pathology and prognosis. Br J Cancer 104(7):1168

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Ratert N, Meyer H-A, Jung M, Mollenkopf H-J, Wagner I, Miller K et al (2012) Reference miRNAs for miRNAome analysis of urothelial carcinomas. PLoS One 7(6):e39309

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Adam L, Wszolek MF, Liu C-G, Jing W, Diao L, Zien A et al (eds) (2013) Plasma microRNA profiles for bladder cancer detection. Urologic Oncology: Seminars and Original Investigations. Elsevier, Amsterdam

    Google Scholar 

  62. Wang L, Liu Y, Du L, Li J, Jiang X, Zheng G et al (2015) Identification and validation of reference genes for the detection of serum microRNAs by reverse transcription-quantitative polymerase chain reaction in patients with bladder cancer. Mol Med Rep 12(1):615–622

    Article  CAS  PubMed  Google Scholar 

  63. Wotschofsky Z, Meyer H-A, Jung M, Fendler A, Wagner I, Stephan C et al (2011) Reference genes for the relative quantification of microRNAs in renal cell carcinomas and their metastases. Anal Biochem 417(2):233–241

    Article  CAS  PubMed  Google Scholar 

  64. Iwamoto H, Kanda Y, Sejima T, Osaki M, Okada F, Takenaka A (2014) Serum miR-210 as a potential biomarker of early clear cell renal cell carcinoma. Int J Oncol 44(1):53–58

    Article  CAS  PubMed  Google Scholar 

  65. Sanders I, Holdenrieder S, Walgenbach-Brünagel G, von Ruecker A, Kristiansen G, Müller SC et al (2012) Evaluation of reference genes for the analysis of serum miRNA in patients with prostate cancer, bladder cancer and renal cell carcinoma. Int J Urol 19(11):1017–1025

    Article  CAS  PubMed  Google Scholar 

  66. Shen Y, Li Y, Ye F, Wang F, Wan X, Lu W et al (2011) Identification of miR-23a as a novel microRNA normalizer for relative quantification in human uterine cervical tissues. Exp Mol Med 43(6):358

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Leitao MCG, Coimbra EC, de Lima RCP, de Lima Guimaraes M, de Andrade Heraclio S, Neto JCS et al (2014) Quantifying mRNA and microRNA with qPCR in cervical carcinogenesis: a validation of reference genes to ensure accurate data. PLoS One 9(11):e111021

    Article  CAS  PubMed Central  Google Scholar 

  68. Babion I, Snoek BC, van de Wiel MA, Wilting SM, Steenbergen RD (2017) A strategy to find suitable reference genes for miRNA quantitative PCR analysis and its application to cervical specimens. J Mol Diagn 19(5):625–637

    Article  CAS  PubMed  Google Scholar 

  69. Hansen CN, Ketabi Z, Rosenstierne MW, Palle C, Boesen HC, Norrild B (2009) Expression of CPEB, GAPDH and U6snRNA in cervical and ovarian tissue during cancer development. APMIS 117(1):53–59

    Article  CAS  PubMed  Google Scholar 

  70. Li Y, Xiang GM, Liu LL, Liu C, Liu F, Jiang DN et al (2015) Assessment of endogenous reference gene suitability for serum exosomal microRNA expression analysis in liver carcinoma resection studies. Mol Med Rep 12(3):4683–4691

    Article  CAS  PubMed  Google Scholar 

  71. Ding X, Ding J, Ning J, Yi F, Chen J, Zhao D et al (2012) Circulating microRNA-122 as a potential biomarker for liver injury. Mol Med Rep 5(6):1428–1432

    CAS  PubMed  Google Scholar 

  72. Schaefer A, Jung M, Miller K, Lein M, Kristiansen G, Erbersdobler A et al (2010) Suitable reference genes for relative quantification of miRNA expression in prostate cancer. Exp Mol Med 42(11):749

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Carlsson J, Helenius G, Karlsson M, Lubovac Z, Andrén O, Olsson B et al (2010) Validation of suitable endogenous control genes for expression studies of miRNA in prostate cancer tissues. Cancer Genet Cytogenet 202(2):71–75

    Article  CAS  PubMed  Google Scholar 

  74. Egidi MG, Cochetti G, Guelfi G, Zampini D, Diverio S, Poli G et al (2015) Stability assessment of candidate reference genes in urine sediment of prostate cancer patients for miRNA applications. Dis Markers. https://doi.org/10.1155/2015/973597

    Article  PubMed  PubMed Central  Google Scholar 

  75. Chen L, Jin Y, Wang L, Sun F, Yang X, Shi M et al (2017) Identification of reference genes and miRNAs for qRT-PCR in human esophageal squamous cell carcinoma. Med Oncol 34(1):2

    Article  CAS  PubMed  Google Scholar 

  76. Popov A, Szabo A, Mandys V (2015) Small nucleolar RNA U91 is a new internal control for accurate microRNAs quantification in pancreatic cancer. BMC Cancer 15(1):774

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Que R, Ding G, Chen J, Cao L (2013) Analysis of serum exosomal microRNAs and clinicopathologic features of patients with pancreatic adenocarcinoma. World J Surg Oncol 11(1):219

    Article  PubMed  PubMed Central  Google Scholar 

  78. Madadi S, Soleimani M (2019) Plasma microRNA investigation: the impact of selecting a suitable internal control on data normalization in pancreatic cancer. J Hepato-Biliary-Pancreatic Sci 26(2):E1

    Article  Google Scholar 

  79. Sperveslage J, Hoffmeister M, Henopp T, Klöppel G, Sipos B (2014) Establishment of robust controls for the normalization of miRNA expression in neuroendocrine tumors of the ileum and pancreas. Endocrine 46(2):226–230

    Article  CAS  PubMed  Google Scholar 

  80. Song J, Bai Z, Han W, Zhang J, Meng H, Bi J et al (2012) Identification of suitable reference genes for qPCR analysis of serum microRNA in gastric cancer patients. Dig Dis Sci 57(4):897–904

    Article  CAS  PubMed  Google Scholar 

  81. Torres A, Torres K, Wdowiak P, Paszkowski T, Maciejewski R (2013) Selection and validation of endogenous controls for microRNA expression studies in endometrioid endometrial cancer tissues. Gynecol Oncol 130(3):588–594

    Article  CAS  PubMed  Google Scholar 

  82. Huber PJ (2011) Robust statistics. Springer, Berlin

    Google Scholar 

  83. Schuirmann D (ed) (1981) On hypothesis-testing to determine if the mean of a normal-distribution is contained in a known interval. Biometrics. International Biometric Society, Washington DC

    Google Scholar 

  84. Bignotti E, Calza S, Tassi RA, Zanotti L, Bandiera E, Sartori E et al (2016) Identification of stably expressed reference small non-coding RNA s for micro RNA quantification in high-grade serous ovarian carcinoma tissues. J Cell Mol Med 20(12):2341–2348

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Hu J, Wang Z, Liao BY, Yu L, Gao X, Lu S et al (2014) Human miR-1228 as a stable endogenous control for the quantification of circulating microRNAs in cancer patients. Int J Cancer 135(5):1187–1194

    Article  CAS  PubMed  Google Scholar 

  86. Peltier HJ, Latham GJ (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(5):844–852

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. Rice J, Roberts H, Rai SN, Galandiuk S (2015) Housekeeping genes for studies of plasma microRNA: a need for more precise standardization. Surgery 158(5):1345–1351

    Article  PubMed  Google Scholar 

  88. Chen X, Liang H, Guan D, Wang C, Hu X, Cui L et al (2013) A combination of Let-7d, Let-7g and Let-7i serves as a stable reference for normalization of serum microRNAs. PLoS One 8(11):e79652

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. Hu Z, Dong J, Wang L-E, Ma H, Liu J, Zhao Y et al (2012) Serum microRNA profiling and breast cancer risk: the use of miR-484/191 as endogenous controls. Carcinogenesis 33(4):828–834

    Article  CAS  PubMed  Google Scholar 

  90. Xiang M, Zeng Y, Yang R, Xu H, Chen Z, Zhong J et al (2014) U6 is not a suitable endogenous control for the quantification of circulating microRNAs. Biochem Biophys Res Commun 454(1):210–214

    Article  CAS  PubMed  Google Scholar 

  91. Inada K, Okoshi Y, Cho-Isoda Y, Ishiguro S, Suzuki H, Oki A et al (2018) Endogenous reference RNAs for microRNA quantitation in formalin-fixed, paraffin-embedded lymph node tissue. Sci Rep 8(1):5918

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  92. Lamba V, Ghodke-Puranik Y, Guan W, Lamba JK (2014) Identification of suitable reference genes for hepatic microRNA quantitation. BMC Res Notes 7(1):129

    Article  PubMed  PubMed Central  Google Scholar 

  93. Armand-Labit V, Pradines A (2017) Circulating cell-free microRNAs as clinical cancer biomarkers. Biomol Concepts 8(2):61–81

    Article  CAS  PubMed  Google Scholar 

  94. Stevic I, Müller V, Weber K, Fasching PA, Karn T, Marmé F et al (2018) Specific microRNA signatures in exosomes of triple-negative and HER2-positive breast cancer patients undergoing neoadjuvant therapy within the GeparSixto trial. BMC Med 16(1):179

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  95. Kuhlmann JD, Baraniskin A, Hahn SA, Mosel F, Bredemeier M, Wimberger P et al (2014) Circulating U2 small nuclear RNA fragments as a novel diagnostic tool for patients with epithelial ovarian cancer. Clin Chem 60(1):206–213

    Article  CAS  PubMed  Google Scholar 

  96. Liu M, Liu J, Wang L, Wu H, Zhou C, Zhu H et al (2014) Association of serum microRNA expression in hepatocellular carcinomas treated with transarterial chemoembolization and patient survival. PLoS One 9(10):e109347

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  97. Anadol E, Schierwagen R, Elfimova N, Tack K, Schwarze-Zander C, Eischeid H et al (2015) Circulating microRNAs as a marker for liver injury in human immunodeficiency virus patients. Hepatology 61(1):46–55

    Article  CAS  PubMed  Google Scholar 

  98. Sourvinou IS, Markou A, Lianidou ES (2013) Quantification of circulating miRNAs in plasma: effect of preanalytical and analytical parameters on their isolation and stability. J Mol Diagn 15(6):827–834

    Article  CAS  PubMed  Google Scholar 

  99. Luque A, Farwati A, Crovetto F, Crispi F, Figueras F, Gratacós E et al (2014) Usefulness of circulating microRNAs for the prediction of early preeclampsia at first-trimester of pregnancy. Sci Rep 4:4882

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  100. Zhang J, Raju GS, Chang DW, Lin SH, Chen Z, Wu X (2018) Global and targeted circulating microRNA profiling of colorectal adenoma and colorectal cancer. Cancer 124(4):785–796

    Article  CAS  PubMed  Google Scholar 

  101. Xu Y-F, Hannafon BN, Zhao YD, Postier RG, Ding W-Q (2017) Plasma exosome miR-196a and miR-1246 are potential indicators of localized pancreatic cancer. Oncotarget 8(44):77028

    PubMed  PubMed Central  Google Scholar 

  102. Sohn W, Kim J, Kang SH, Yang SR, Cho J-Y, Cho HC et al (2015) Serum exosomal microRNAs as novel biomarkers for hepatocellular carcinoma. Exp Mol Med 47(9):e184

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  103. Appourchaux K, Dokmak S, Resche-Rigon M, Treton X, Lapalus M, Gattolliat C-H et al (2016) MicroRNA-based diagnostic tools for advanced fibrosis and cirrhosis in patients with chronic hepatitis B and C. Sci Rep 6:34935

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  104. Meng X, Müller V, Milde-Langosch K, Trillsch F, Pantel K, Schwarzenbach H (2016) Diagnostic and prognostic relevance of circulating exosomal miR-373, miR-200a, miR-200b and miR-200c in patients with epithelial ovarian cancer. Oncotarget 7(13):16923

    Article  PubMed  PubMed Central  Google Scholar 

  105. Meng X, Joosse SA, Müller V, Trillsch F, Milde-Langosch K, Mahner S et al (2015) Diagnostic and prognostic potential of serum miR-7, miR-16, miR-25, miR-93, miR-182, miR-376a and miR-429 in ovarian cancer patients. Br J Cancer 113(9):1358

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  106. Li J, Wang Y, Yu W, Chen J, Luo J (2011) Expression of serum miR-221 in human hepatocellular carcinoma and its prognostic significance. Biochem Biophys Res Commun 406(1):70–73

    Article  CAS  PubMed  Google Scholar 

  107. Sode J, Krintel SB, Carlsen AL, Hetland ML, Johansen JS, Hørslev-Petersen K et al (2018) Plasma microRNA profiles in patients with early rheumatoid arthritis responding to adalimumab plus methotrexate vs methotrexate alone: a placebo-controlled clinical trial. J Rheumatol 45(1):53–61

    Article  CAS  PubMed  Google Scholar 

  108. Madadi S, Soleimani M (2019) Study of serum microRNA expression in an amyotrophic lateral sclerosis patient: challenge of selecting suitable internal control for normalization. Muscle Nerve 59:E2–E3

    Article  PubMed  Google Scholar 

  109. Madadi S, Soleimani M (2018) U6 as a microRNA normalizer in serum of patients with hepatocellular carcinoma. Ann Clin Biochem 2018:4563218808820

    Google Scholar 

  110. Vandesompele J, De Preter K, Pattyn F, Poppe B, Van Roy N, De Paepe A et al (2002) Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biol. 3(7):research0034.1

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Meysam Soleimani.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Madadi, S., Schwarzenbach, H., Lorenzen, J. et al. MicroRNA expression studies: challenge of selecting reliable reference controls for data normalization. Cell. Mol. Life Sci. 76, 3497–3514 (2019). https://doi.org/10.1007/s00018-019-03136-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00018-019-03136-y

Keywords

Navigation