Pathology & Oncology Research

, Volume 25, Issue 1, pp 169–182 | Cite as

Limitations of high throughput methods for miRNA expression profiles in non-functioning pituitary adenomas

  • O. Darvasi
  • P. M. Szabo
  • K. Nemeth
  • K. Szabo
  • S. Spisak
  • I. Liko
  • S. Czirjak
  • K. Racz
  • P. Igaz
  • A. Patocs
  • Henriett ButzEmail author
Original Article


Microarray, RT-qPCR based arrays and next-generation-sequencing (NGS) are available high-throughput methods for miRNA profiling (miRNome). Analytical and biological performance of these methods were tested in identification of biologically relevant miRNAs in non-functioning pituitary adenomas (NFPA). miRNome of 4 normal pituitary (NP) and 8 NFPA samples was determined by these platforms and expression of 21 individual miRNAs was measured on 30 (20 NFPA and 10 NP) independent samples. Complex bioinformatics was used. 132 and 137 miRNAs were detected by all three platforms in NP and NFPA, respectively, of which 25 were differentially expressed (fold change > 2). The strongest correlation was observed between microarray and TaqMan-array, while the data obtained by NGS were the most discordant despite of various bioinformatics settings. As a technical validation we measured the expression of 21 selected miRNAs by individual RT-qPCR and we were able to validate 35.1%, 76.2% and 71.4% of the miRNAs revealed by SOLiD, TLDA and microarray result, respectively. We performed biological validation using an extended number of samples (20 NFPAs and 8 NPs). Technical and biological validation showed high correlation (p < 0.001; R = 0.96). Pathway and network analysis revealed several common pathways but no pathway showed the same activation score. Using the 25 platform-independent miRNAs developmental pathways were the top functional categories relevant for NFPA genesis. The difference among high-throughput platforms is of great importance and selection of screening method can influence experimental results. Validation by another platform is essential in order to avoid or to minimalize the platform specific errors.


miRNA miRNA profiling Non-functioning pituitary adenoma 



This work has been funded by National Research, Development and Innovation Office – NKFIH PD116093 to Henriett Butz. Attila Patocs is a recipient of “Lendulet” grant from Hungarian Academy of Sciences.

Compliance with Ethical Standards

Conflicts of Interest

The authors declare no potential conflicts of interest.

Supplementary material

12253_2017_330_MOESM1_ESM.docx (13 kb)
Supplemental Table 1 (DOCX 12 kb)
12253_2017_330_MOESM2_ESM.docx (13 kb)
Supplemental Table 2 (DOCX 13 kb)
12253_2017_330_MOESM3_ESM.docx (14 kb)
Supplemental Table 3 (DOCX 14 kb)
12253_2017_330_MOESM4_ESM.docx (19 kb)
Supplemental Table 4 (DOCX 19 kb)
12253_2017_330_Fig5_ESM.gif (182 kb)
Supplementary Figure 1

Numbers of differentially expressed miRNAs in the same direction with a fold change cut-off 2 identified by different platforms (GIF 182 kb)

12253_2017_330_MOESM5_ESM.tif (2.1 mb)
High Resolution Image (TIFF 2146 kb)
12253_2017_330_Fig6_ESM.gif (44 kb)
Supplementary Figure 2

a Pathway analysis of experimentally validated targets of differentially expressed miRNA lists obtained by different platforms b Comparison of most significant pathway activation z-scores of different platforms. Z-scores of pathways are presented where the scores showed the same direction at least two studies. Z-score infers the activation states of predicted pathway by investigating the activator or inhibitor function of the enriched genes in the particular pathway (GIF 43 kb)

12253_2017_330_MOESM6_ESM.tif (11.9 mb)
High Resolution Image (TIFF 12135 kb)
12253_2017_330_Fig7_ESM.gif (76 kb)
Supplementary Figure 3

Pathway analysis of “platform independent” miRNAs (GIF 75 kb)

12253_2017_330_MOESM7_ESM.tif (14.5 mb)
High Resolution Image (TIFF 14830 kb)
12253_2017_330_Fig8_ESM.gif (183 kb)
Supplementary Figure 4

Gene ontology analysis of “platform independent” miRNAs (GIF 183 kb)

12253_2017_330_MOESM8_ESM.tif (7.6 mb)
High Resolution Image (TIFF 7792 kb)


  1. 1.
    Lagos-Quintana M, Rauhut R, Lendeckel W, Tuschl T (2001) Identification of novel genes coding for small expressed RNAs. Science 294:853–858. CrossRefGoogle Scholar
  2. 2.
    Place RF, Li L-C, Pookot D et al (2008) MicroRNA-373 induces expression of genes with complementary promoter sequences. Proc Natl Acad Sci U S A 105:1608–1613. CrossRefGoogle Scholar
  3. 3.
    Ørom UA, Nielsen FC, Lund AH (2008) MicroRNA-10a binds the 5’UTR of ribosomal protein mRNAs and enhances their translation. Mol Cell 30:460–471. CrossRefGoogle Scholar
  4. 4.
    Zhang Z, Florez S, Gutierrez-Hartmann A et al (2010) MicroRNAs regulate pituitary development, and microRNA 26b specifically targets lymphoid enhancer factor 1 (Lef-1), which modulates pituitary transcription factor 1 (Pit-1) expression. J Biol Chem 285:34718–34728. CrossRefGoogle Scholar
  5. 5.
    Chen K, Rajewsky N (2006) Natural selection on human microRNA binding sites inferred from SNP data. Nat Genet 38:1452–1456. CrossRefGoogle Scholar
  6. 6.
    Lewis BP, Burge CB, Bartel DP (2005) Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets. Cell 120:15–20. CrossRefGoogle Scholar
  7. 7.
    Dolecek TA, Propp JM, Stroup NE, Kruchko C (2012) CBTRUS statistical report: primary brain and central nervous system tumors diagnosed in the United States in 2005-2009. Neuro-Oncol 14(Suppl 5):v1–49. CrossRefGoogle Scholar
  8. 8.
    Dworakowska D, Grossman AB (2009) The pathophysiology of pituitary adenomas. Best Pract Res Clin Endocrinol Metab 23:525–541. CrossRefGoogle Scholar
  9. 9.
    Mayson SE, Snyder PJ (2014) Silent (clinically nonfunctioning) pituitary adenomas. J Neuro-Oncol 117:429–436. CrossRefGoogle Scholar
  10. 10.
    Sivapragasam M, Rotondo F, Lloyd RV et al (2011) MicroRNAs in the human pituitary. Endocr Pathol 22:134–143. CrossRefGoogle Scholar
  11. 11.
    Li X-H, Wang EL, Zhou H-M et al (2014) MicroRNAs in Human Pituitary Adenomas. Int J Endocrinol 2014:435171. Google Scholar
  12. 12.
    Pritchard CC, Cheng HH, Tewari M (2012) MicroRNA profiling: approaches and considerations. Nat Rev Genet 13:358–369. CrossRefGoogle Scholar
  13. 13.
    Mestdagh P, Hartmann N, Baeriswyl L et al (2014) Evaluation of quantitative miRNA expression platforms in the microRNA quality control (miRQC) study. Nat Methods 11:809–815. CrossRefGoogle Scholar
  14. 14.
    Git A, Dvinge H, Salmon-Divon M et al (2010) Systematic comparison of microarray profiling, real-time PCR, and next-generation sequencing technologies for measuring differential microRNA expression. RNA N Y N 16:991–1006. CrossRefGoogle Scholar
  15. 15.
    Butz H, Likó I, Czirják S et al (2010) Down-regulation of Wee1 kinase by a specific subset of microRNA in human sporadic pituitary adenomas. J Clin Endocrinol Metab 95:E181–E191. CrossRefGoogle Scholar
  16. 16.
    Butz H, Németh K, Czenke D et al (2016) Systematic Investigation of Expression of G2/M Transition Genes Reveals CDC25 Alteration in Nonfunctioning Pituitary Adenomas. Pathol Oncol Res POR.
  17. 17.
    Thompson IR, Chand AN, King PJ et al (2012) Expression of guanylyl cyclase-B (GC-B/NPR2) receptors in normal human fetal pituitaries and human pituitary adenomas implicates a role for C-type natriuretic peptide. Endocr Relat Cancer 19:497–508. CrossRefGoogle Scholar
  18. 18.
    Trivellin G, Butz H, Delhove J et al (2012) MicroRNA miR-107 is overexpressed in pituitary adenomas and inhibits the expression of aryl hydrocarbon receptor-interacting protein in vitro. Am J Physiol Endocrinol Metab 303:E708–E719. CrossRefGoogle Scholar
  19. 19.
    Harmati M, Tarnai Z, Decsi G et al (2016) Stressors alter intercellular communication and exosome profile of nasopharyngeal carcinoma cells. J Oral Pathol Med Off Publ Int Assoc Oral Pathol Am Acad Oral Pathol.
  20. 20.
    Butz H, Likó I, Czirják S et al (2011) MicroRNA profile indicates downregulation of the TGFβ pathway in sporadic non-functioning pituitary adenomas. Pituitary 14:112–124. CrossRefGoogle Scholar
  21. 21.
    Butz H, Szabó PM, Nofech-Mozes R et al (2014) Integrative bioinformatics analysis reveals new prognostic biomarkers of clear cell renal cell carcinoma. Clin Chem 60:1314–1326. CrossRefGoogle Scholar
  22. 22.
    Butz H, Szabó PM, Khella HWZ et al (2015) miRNA-target network reveals miR-124as a key miRNA contributing to clear cell renal cell carcinoma aggressive behaviour by targeting CAV1 and FLOT1. Oncotarget 6:12543–12557.  10.18632/oncotarget.3815 CrossRefGoogle Scholar
  23. 23.
    Wang B, Howel P, Bruheim S et al (2011) Systematic evaluation of three microRNA profiling platforms: microarray, beads array, and quantitative real-time PCR array. PLoS One 6:e17167. CrossRefGoogle Scholar
  24. 24.
    Chevillet JR, Lee I, Briggs HA et al (2014) Issues and prospects of microRNA-based biomarkers in blood and other body fluids. Mol Basel Switz 19:6080–6105. Google Scholar
  25. 25.
    Farr RJ, Januszewski AS, Joglekar MV et al (2015) A comparative analysis of high-throughput platforms for validation of a circulating microRNA signature in diabetic retinopathy. Sci Rep 5:10375. CrossRefGoogle Scholar
  26. 26.
    Bottoni A, Zatelli MC, Ferracin M et al (2007) Identification of differentially expressed microRNAs by microarray: a possible role for microRNA genes in pituitary adenomas. J Cell Physiol 210:370–377. CrossRefGoogle Scholar
  27. 27.
    Liang S, Chen L, Huang H, Zhi D (2013) The experimental study of miRNA in pituitary adenomas. Turk Neurosurg 23:721–727. Google Scholar
  28. 28.
    Chambers TJG, Giles A, Brabant G, Davis JRE (2013) Wnt signalling in pituitary development and tumorigenesis. Endocr Relat Cancer 20:R101–R111. CrossRefGoogle Scholar
  29. 29.
    Elston MS, Gill AJ, Conaglen JV et al (2008) Wnt pathway inhibitors are strongly down-regulated in pituitary tumors. Endocrinology 149:1235–1242. CrossRefGoogle Scholar
  30. 30.
    Colli LM, Saggioro F, Serafini LN et al (2013) Components of the canonical and non-canonical Wnt pathways are not mis-expressed in pituitary tumors. PLoS One 8:e62424. CrossRefGoogle Scholar
  31. 31.
    Formosa R, Gruppetta M, Falzon S et al (2012) Expression and clinical significance of Wnt players and survivin in pituitary tumours. Endocr Pathol 23:123–131. CrossRefGoogle Scholar
  32. 32.
    Lebrun J-J (2009) Activin, TGF-beta and menin in pituitary tumorigenesis. Adv Exp Med Biol 668:69–78CrossRefGoogle Scholar
  33. 33.
    Ruebel KH, Leontovich AA, Tanizaki Y et al (2008) Effects of TGFbeta1 on gene expression in the HP75 human pituitary tumor cell line identified by gene expression profiling. Endocrine 33:62–76. CrossRefGoogle Scholar
  34. 34.
    Zhenye L, Chuzhong L, Youtu W et al (2014) The expression of TGF-β1, Smad3, phospho-Smad3 and Smad7 is correlated with the development and invasion of nonfunctioning pituitary adenomas. J Transl Med 12:71. CrossRefGoogle Scholar

Copyright information

© Arányi Lajos Foundation 2017

Authors and Affiliations

  1. 1.Hereditary Endocrine Tumors Research GroupHungarian Academy of Sciences and Semmelweis UniversityBudapestHungary
  2. 2.Molecular Medicine Research GroupHungarian Academy of Sciences and Semmelweis UniversityBudapestHungary
  3. 3.2nd Department of Medicine, Faculty of MedicineSemmelweis UniversityBudapestHungary
  4. 4.National Institute of NeurosurgeryBudapestHungary
  5. 5.Semmelweis UniversityDepartment of Laboratory MedicineBudapestHungary

Personalised recommendations