Selection and validation of miRNAs as normalizers for profiling expression of microRNAs isolated from thyroid fine needle aspiration smears

Titov,Sergei E.; Demenkov,Pavel S.; Ivanov,Mikhail K.; Malakhina,Ekaterina S.; Poloz,Tatiana L.; Tsivlikova,Elena V.; Ganzha,Maria S.; Shevchenko,Sergei P.; Gulyaeva,Lyudmila F.; Kolesnikov,Nikolay N.

doi:10.3892/or.2016.5113

Selection and validation of miRNAs as normalizers for profiling expression of microRNAs isolated from thyroid fine needle aspiration smears

Authors:
- Sergei E. Titov
- Pavel S. Demenkov
- Mikhail K. Ivanov
- Ekaterina S. Malakhina
- Tatiana L. Poloz
- Elena V. Tsivlikova
- Maria S. Ganzha
- Sergei P. Shevchenko
- Lyudmila F. Gulyaeva
- Nikolay N. Kolesnikov
View Affiliations

Affiliations: Institute of Molecular and Cellular Biology, Siberian Branch of the Russian Academy of Sciences, Novosibirsk 630090, Russia, The Federal Research Center Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, Novosibirsk 630090, Russia, Institute of Chemical Biology and Fundamental Medicine, Siberian Branch of the Russian Academy of Sciences, Novosibirsk 630090, Russia, Non-governmental Healthcare Institution ‘Railroad Clinical Hospital on the Station Novosibirsk-Glavny’, Joint Stock Company ‘Russian Railways’, Novosibirsk 630003, Russia, AO Vector-Best, Novosibirsk 630117, Russia, Novosibirsk Municipal Budgetary Healthcare Institution ‘Municipal Clinical Hospital #1’, Novosibirsk 630047, Russia, Institute of Molecular Biology and Biophysics, Siberian Branch of the Russian Academy of Medical Sciences, Novosibirsk 630117, Russia
Published online on: September 20, 2016 https://doi.org/10.3892/or.2016.5113
Pages: 2501-2510
Copyright: © Titov et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

Metrics: Total Views: 0 (Spandidos Publications: | PMC Statistics: )

Metrics: Total PDF Downloads: 0 (Spandidos Publications: | PMC Statistics: )

Cited By (CrossRef): 0 citations Loading Articles...

Abstract

Fine needle aspiration cytology (FNAC) is currently the method of choice for malignancy prediction in thyroid nodules. Nevertheless, in some cases the interpretation of FNAC results may be problematic due to limitations of the method. The expression level of some microRNAs changes with the development of thyroid tumors, and its quantitation can be used to refine the FNAC results. For this quantitation to be reliable, the obtained data must be adequately normalized. Currently, no reference genes are universally recognized for quantitative assessments of microRNAs in thyroid nodules. The aim of the present study was the selection and validation of such reference genes. Expression of 800 microRNAs in 5 paired samples of thyroid surgical material corresponding to different histotypes of tumors was analyzed using Nanostring technology and four of these (hsa-miR-151a-3p, -197-3p, -99a-5p and -214-3p) with the relatively low variation coefficient were selected. The possibility of use of the selected microRNAs and their combination as references was estimated by RT-qPCR on a sampling of cytological smears: benign (n=226), atypia of undetermined significance (n=9), suspicious for follicular neoplasm (n=61), suspicious for malignancy (n=19), medullary thyroid carcinoma (MTC) (n=32), papillary thyroid carcinoma (PTC) (n=54) and non-diagnostic material (ND) (n=34). In order to assess the expression stability of the references, geNorm algorithm was used. The maximum stability was observed for the normalization factor obtained by the combination of all 4 microRNAs. Further validation of the complex normalizer and individual selected microRNAs was performed using 5 different classification methods on 3 groups of FNAC smears from the analyzed batch: benign neoplasms, MTC and PTC. In all cases, the use of the complex classifier resulted in the reduced number of errors. On using the complex microRNA normalizer, the decision-tree method C4.5 makes it possible to distinguish between malignant and benign thyroid neoplasms in cytological smears with high overall accuracy (>91%).

View Figures

View References

1	Guth S, Theune U, Aberle J, Galach A and Bamberger CM: Very high prevalence of thyroid nodules detected by high frequency (13 MHz) ultrasound examination. Eur J Clin Invest. 39:699–706. 2009. View Article : Google Scholar : PubMed/NCBI
2	Orlandi A, Puscar A, Capriata E and Fideleff H: Repeated fine-needle aspiration of the thyroid in benign nodular thyroid disease: Critical evaluation of long-term follow-up. Thyroid. 15:274–278. 2005. View Article : Google Scholar : PubMed/NCBI
3	Stevens C, Lee JK, Sadatsafavi M and Blair GK: Pediatric thyroid fine-needle aspiration cytology: A meta-analysis. J Pediatr Surg. 44:2184–2191. 2009. View Article : Google Scholar : PubMed/NCBI
4	Witt RL, Ferris RL, Pribitkin EA, Sherman SI, Steward DL and Nikiforov YE: Diagnosis and management of differentiated thyroid cancer using molecular biology. Laryngoscope. 123:1059–1064. 2013. View Article : Google Scholar : PubMed/NCBI
5	Pallante P, Visone R, Ferracin M, Ferraro A, Berlingieri MT, Troncone G, Chiappetta G, Liu CG, Santoro M, Negrini M, et al: MicroRNA deregulation in human thyroid papillary carcinomas. Endocr Relat Cancer. 13:497–508. 2006. View Article : Google Scholar : PubMed/NCBI
6	Kitano M, Rahbari R, Patterson EE, Steinberg SM, Prasad NB, Wang Y, Zeiger MA and Kebebew E: Evaluation of candidate diagnostic microRNAs in thyroid fine-needle aspiration biopsy samples. Thyroid. 22:285–291. 2012. View Article : Google Scholar : PubMed/NCBI
7	Swierniak M, Wojcicka A, Czetwertynska M, Stachlewska E, Maciag M, Wiechno W, Gornicka B, Bogdanska M, Koperski L, de la Chapelle A, et al: In-depth characterization of the microRNA transcriptome in normal thyroid and papillary thyroid carcinoma. J Clin Endocrinol Metab. 98:E1401–E1409. 2013. View Article : Google Scholar : PubMed/NCBI
8	Cancer Genome Atlas Research Network: Integrated genomic characterization of papillary thyroid carcinoma. Cell. 159:676–690. 2014. View Article : Google Scholar : PubMed/NCBI
9	Meyer SU, Pfaffl MW and Ulbrich SE: Normalization strategies for microRNA profiling experiments: A 'normal' way to a hidden layer of complexity? Biotechnol Lett. 32:1777–1788. 2010. View Article : Google Scholar : PubMed/NCBI
10	Huggett J, Dheda K, Bustin S and Zumla A: Real-time RT-PCR normalisation; strategies and considerations. Genes Immun. 6:279–284. 2005. View Article : Google Scholar : PubMed/NCBI
11	Bustin SA, Benes V, Garson JA, Hellemans J, Huggett J, Kubista M, Mueller R, Nolan T, Pfaffl MW, Shipley GL, et al: The MIQE guidelines: Minimum information for publication of quantitative real-time PCR experiments. Clin Chem. 55:611–622. 2009. View Article : Google Scholar : PubMed/NCBI
12	Spanakis E: Problems related to the interpretation of autoradiographic data on gene expression using common constitutive transcripts as controls. Nucleic Acids Res. 21:3809–3819. 1993. View Article : Google Scholar : PubMed/NCBI
13	Vandesompele J, De Preter K, Pattyn F, Poppe B, Van Roy N, De Paepe A and Speleman F: Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biol. 3:H00342002. View Article : Google Scholar
14	Dijkstra JR, van Kempen LC, Nagtegaal ID and Bustin SA: Critical appraisal of quantitative PCR results in colorectal cancer research: Can we rely on published qPCR results? Mol Oncol. 8:813–818. 2014. View Article : Google Scholar : PubMed/NCBI
15	Gee HE, Buffa FM, Camps C, Ramachandran A, Leek R, Taylor M, Patil M, Sheldon H, Betts G, Homer J, et al: The small-nucleolar RNAs commonly used for microRNA normalisation correlate with tumour pathology and prognosis. Br J Cancer. 104:1168–1177. 2011. View Article : Google Scholar : PubMed/NCBI
16	Appaiah HN, Goswami CP, Mina LA, Badve S, Sledge GW Jr, Liu Y and Nakshatri H: Persistent upregulation of U6:SNORD44 small RNA ratio in the serum of breast cancer patients. Breast Cancer Res. 13:R862011. View Article : Google Scholar : PubMed/NCBI
17	Cibas ES and Ali SZ: NCI Thyroid FNA State of the Science Conference: The Bethesda System For Reporting Thyroid Cytopathology. Am J Clin Pathol. 132:658–665. 2009. View Article : Google Scholar : PubMed/NCBI
18	Titov SE, Ivanov MK, Karpinskaya EV, Tsivlikova EV, Shevchenko SP, Veryaskina YA, Akhmerova LG, Poloz TL, Klimova OA, Gulyaeva LF, et al: miRNA profiling, detection of BRAF V600E mutation and RET-PTC1 translocation in patients from Novosibirsk oblast (Russia) with different types of thyroid tumors. BMC Cancer. 16:2012016. View Article : Google Scholar : PubMed/NCBI
19	Ricco R: TANAGRA: a free software for research and academic purposes. Proceedings of EGC'2005, RNTI-E-3; 2. pp. 697–702. 2005, in French.
20	Rao RC: The utilization of multiple measurements in problems of biological classification. J R Stat Soc B. 10:159–203. 1948.
21	Hand DJ and Yu Y: Idiots Bayes - not so stupid after all? Int Stat Rev. 69:385–389. 2001.
22	Rumelhart DE and McClelland JL; the PDP research group: Parallel distributed processing: Explorations in the micro-structure of cognition I. MIT Press; Cambridge, MA: 1986
23	Chang CC and Lin CJ: LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol. 2(3, article 27): 1–39. 2011. View Article : Google Scholar
24	Quinlan JR: C4.5: programs for machine learning. Morgan Kaufmann Publishers Inc; San Francisco, CA: 1993
25	Kulkarni MM: Digital multiplexed gene expression analysis using the NanoString nCounter system. Curr Protoc Mol Biol. 94:25B.10.1–25B.10.17. 2011.
26	Nikiforova MN, Tseng GC, Steward D, Diorio D and Nikiforov YE: MicroRNA expression profiling of thyroid tumors: Biological significance and diagnostic utility. J Clin Endocrinol Metab. 93:1600–1608. 2008. View Article : Google Scholar : PubMed/NCBI
27	Abraham D, Jackson N, Gundara JS, Zhao J, Gill AJ, Delbridge L, Robinson BG and Sidhu SB: MicroRNA profiling of sporadic and hereditary medullary thyroid cancer identifies predictors of nodal metastasis, prognosis, and potential therapeutic targets. Clin Cancer Res. 17:4772–4781. 2011. View Article : Google Scholar : PubMed/NCBI
28	Rossing M, Borup R, Henao R, Winther O, Vikesaa J, Niazi O, Godballe C, Krogdahl A, Glud M, Hjort-Sørensen C, et al: Down-regulation of microRNAs controlling tumourigenic factors in follicular thyroid carcinoma. J Mol Endocrinol. 48:11–23. 2012. View Article : Google Scholar
29	Dettmer M, Perren A, Moch H, Komminoth P, Nikiforov YE and Nikiforova MN: Comprehensive MicroRNA expression profiling identifies novel markers in follicular variant of papillary thyroid carcinoma. Thyroid. 23:1383–1389. 2013. View Article : Google Scholar : PubMed/NCBI
30	Hellemans J and Vandesompele J: Selection of reliable reference genes for RT-qPCR analysis. Quantitative Real-Time PCR: Methods and Protocols, Methods in Molecular Biology. 1160. Biassoni R and Raso A: Springer Science Business Media; New York: pp. 19–26. 2014, View Article : Google Scholar
31	Pallante P, Battista S, Pierantoni GM and Fusco A: Deregulation of microRNA expression in thyroid neoplasias. Nat Rev Endocrinol. 10:88–101. 2014. View Article : Google Scholar
32	Bargaje R, Hariharan M, Scaria V and Pillai B: Consensus miRNA expression profiles derived from interplatform normalization of microarray data. RNA. 16:16–25. 2010. View Article : Google Scholar :
33	Shen Y, Li Y, Ye F, Wang F, Wan X, Lu W and Xie X: Identification of miR-23a as a novel microRNA normalizer for relative quantification in human uterine cervical tissues. Exp Mol Med. 43:358–366. 2011. View Article : Google Scholar : PubMed/NCBI
34	Thorenoor N and Slaby O: Small nucleolar RNAs functioning and potential roles in cancer. Tumour Biol. 36:41–53. 2015. View Article : Google Scholar
35	Weber F, Teresi RE, Broelsch CE, Frilling A and Eng C: A limited set of human MicroRNA is deregulated in follicular thyroid carcinoma. J Clin Endocrinol Metab. 91:3584–3591. 2006. View Article : Google Scholar : PubMed/NCBI
36	Du L, Schageman JJ, Subauste MC, Saber B, Hammond SM, Prudkin L, Wistuba II, Ji L, Roth JA, Minna JD, et al: miR-93, miR-98, and miR-197 regulate expression of tumor suppressor gene FUS1. Mol Cancer Res. 7:1234–1243. 2009. View Article : Google Scholar : PubMed/NCBI
37	Yang H, Kong W, He L, Zhao JJ, O'Donnell JD, Wang J, Wenham RM, Coppola D, Kruk PA, Nicosia SV, et al: MicroRNA expression profiling in human ovarian cancer: miR-214 induces cell survival and cisplatin resistance by targeting PTEN. Cancer Res. 68:425–433. 2008. View Article : Google Scholar : PubMed/NCBI
38	Zhang XJ, Ye H, Zeng CW, He B, Zhang H and Chen YQ: Dysregulation of miR-15a and miR-214 in human pancreatic cancer. J Hematol Oncol. 3:462010. View Article : Google Scholar : PubMed/NCBI