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

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, 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

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%).

Figures

Figure 1
Figure 1
Box-whisker plot of Cq variation for reference and classifying short RNAs in the analyzed sample of 435 FNACs. For Cq values figure presents: inner line, the median value; box, upper and lower quartiles; whisker, non-outlier range; outliers designated by circles. Horizontal dash line separates classifying (miR-551-199b) from reference (miR-99a-197) miRNAs.
Figure 2
Figure 2
geNorm expression stability plot for analyzed miRNAs and RNU6.
Figure 3
Figure 3
Determination of the optimal number of reference genes for normalization by geNorm analysis. Every bar represents the change in normalization accuracy when stepwise adding more reference genes according to the ranking in Fig. 2.
Figure 4
Figure 4
(A) Expression stability of reference miRNAs, RNU6, and NF4 (geometric mean of miR-197, -151a, -214 and -99a) obtained for FNAC smears. (B) The same for the surgical material.
Figure 4
Figure 4
(A) Expression stability of reference miRNAs, RNU6, and NF4 (geometric mean of miR-197, -151a, -214 and -99a) obtained for FNAC smears. (B) The same for the surgical material.
Figure 5
Figure 5
Pairwise variations between the normalization factors NFn and NF(n+1) in the analysis of surgical material.
Figure 6
Figure 6
The content of RNU6 and miR-197 in FNACs, corresponding to different types of neoplasms. (A) Raw quantification cycles; (B) Cq values normalized to NF4.
Figure 6
Figure 6
The content of RNU6 and miR-197 in FNACs, corresponding to different types of neoplasms. (A) Raw quantification cycles; (B) Cq values normalized to NF4.
Figure 7
Figure 7
M-values for: (A) reference miRNAs, NF4 and RNU6; (B) miRNA classifiers. Black columns, the entire sampling of FNAC smears; shaded columns, non-diagnostic material; and blank columns, benign neoplasms.
Figure 7
Figure 7
M-values for: (A) reference miRNAs, NF4 and RNU6; (B) miRNA classifiers. Black columns, the entire sampling of FNAC smears; shaded columns, non-diagnostic material; and blank columns, benign neoplasms.

References

    1. Guth S, Theune U, Aberle J, Galach A, Bamberger CM. Very high prevalence of thyroid nodules detected by high frequency (13 MHz) ultrasound examination. Eur J Clin Invest. 2009;39:699–706. doi: 10.1111/j.1365-2362.2009.02162.x.
    1. Orlandi A, Puscar A, Capriata E, Fideleff H. Repeated fine-needle aspiration of the thyroid in benign nodular thyroid disease: Critical evaluation of long-term follow-up. Thyroid. 2005;15:274–278. doi: 10.1089/thy.2005.15.274.
    1. Stevens C, Lee JK, Sadatsafavi M, Blair GK. Pediatric thyroid fine-needle aspiration cytology: A meta-analysis. J Pediatr Surg. 2009;44:2184–2191. doi: 10.1016/j.jpedsurg.2009.07.022.
    1. Witt RL, Ferris RL, Pribitkin EA, Sherman SI, Steward DL, Nikiforov YE. Diagnosis and management of differentiated thyroid cancer using molecular biology. Laryngoscope. 2013;123:1059–1064. doi: 10.1002/lary.23838.
    1. 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. 2006;13:497–508. doi: 10.1677/erc.1.01209.
    1. Kitano M, Rahbari R, Patterson EE, Steinberg SM, Prasad NB, Wang Y, Zeiger MA, Kebebew E. Evaluation of candidate diagnostic microRNAs in thyroid fine-needle aspiration biopsy samples. Thyroid. 2012;22:285–291. doi: 10.1089/thy.2011.0313.
    1. 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. 2013;98:E1401–E1409. doi: 10.1210/jc.2013-1214.
    1. Cancer Genome Atlas Research Network Integrated genomic characterization of papillary thyroid carcinoma. Cell. 2014;159:676–690. doi: 10.1016/j.cell.2014.09.050.
    1. Meyer SU, Pfaffl MW, Ulbrich SE. Normalization strategies for microRNA profiling experiments: A 'normal' way to a hidden layer of complexity? Biotechnol Lett. 2010;32:1777–1788. doi: 10.1007/s10529-010-0380-z.
    1. Huggett J, Dheda K, Bustin S, Zumla A. Real-time RT-PCR normalisation; strategies and considerations. Genes Immun. 2005;6:279–284. doi: 10.1038/sj.gene.6364190.
    1. 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. 2009;55:611–622. doi: 10.1373/clinchem.2008.112797.
    1. Spanakis E. Problems related to the interpretation of autoradiographic data on gene expression using common constitutive transcripts as controls. Nucleic Acids Res. 1993;21:3809–3819. doi: 10.1093/nar/21.16.3809.
    1. Vandesompele J, De Preter K, Pattyn F, Poppe B, Van Roy N, De Paepe A, Speleman F. Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biol. 2002;3:H0034. doi: 10.1186/gb-2002-3-7-research0034.
    1. Dijkstra JR, van Kempen LC, Nagtegaal ID, Bustin SA. Critical appraisal of quantitative PCR results in colorectal cancer research: Can we rely on published qPCR results? Mol Oncol. 2014;8:813–818. doi: 10.1016/j.molonc.2013.12.016.
    1. 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. 2011;104:1168–1177. doi: 10.1038/sj.bjc.6606076.
    1. Appaiah HN, Goswami CP, Mina LA, Badve S, Sledge GW, Jr, Liu Y, Nakshatri H. Persistent upregulation of U6:SNORD44 small RNA ratio in the serum of breast cancer patients. Breast Cancer Res. 2011;13:R86. doi: 10.1186/bcr2943.
    1. Cibas ES, Ali SZ. NCI Thyroid FNA State of the Science Conference: The Bethesda System For Reporting Thyroid Cytopathology. Am J Clin Pathol. 2009;132:658–665. doi: 10.1309/AJCPPHLWMI3JV4LA.
    1. 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. 2016;16:201. doi: 10.1186/s12885-016-2240-2.
    1. Ricco R. TANAGRA: a free software for research and academic purposes; Proceedings of EGC'2005, RNTI-E-3; 2005. pp. 697–702. in French.
    1. Rao RC. The utilization of multiple measurements in problems of biological classification. J R Stat Soc B. 1948;10:159–203.
    1. Hand DJ, Yu Y. Idiots Bayes - not so stupid after all? Int Stat Rev. 2001;69:385–389.
    1. Rumelhart DE, McClelland JL, the PDP research group . Parallel distributed processing: Explorations in the micro-structure of cognition I. MIT Press; Cambridge, MA: 1986.
    1. Chang CC, Lin CJ. LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol. 2011;2(3, article 27):1–39. doi: 10.1145/1961189.1961199.
    1. Quinlan JR. C4.5: programs for machine learning. Morgan Kaufmann Publishers Inc; San Francisco, CA: 1993.
    1. Kulkarni MM. Digital multiplexed gene expression analysis using the NanoString nCounter system. Curr Protoc Mol Biol. 2011;94:25B.10.1–25B.10.17.
    1. Nikiforova MN, Tseng GC, Steward D, Diorio D, Nikiforov YE. MicroRNA expression profiling of thyroid tumors: Biological significance and diagnostic utility. J Clin Endocrinol Metab. 2008;93:1600–1608. doi: 10.1210/jc.2007-2696.
    1. Abraham D, Jackson N, Gundara JS, Zhao J, Gill AJ, Delbridge L, Robinson BG, Sidhu SB. MicroRNA profiling of sporadic and hereditary medullary thyroid cancer identifies predictors of nodal metastasis, prognosis, and potential therapeutic targets. Clin Cancer Res. 2011;17:4772–4781. doi: 10.1158/1078-0432.CCR-11-0242.
    1. 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. 2012;48:11–23. doi: 10.1530/JME-11-0039.
    1. Dettmer M, Perren A, Moch H, Komminoth P, Nikiforov YE, Nikiforova MN. Comprehensive MicroRNA expression profiling identifies novel markers in follicular variant of papillary thyroid carcinoma. Thyroid. 2013;23:1383–1389. doi: 10.1089/thy.2012.0632.
    1. Hellemans J, Vandesompele J. Selection of reliable reference genes for RT-qPCR analysis. In: Biassoni R, Raso A, editors. Quantitative Real-Time PCR: Methods and Protocols, Methods in Molecular Biology. Vol. 1160. Springer Science Business Media; New York: 2014. pp. 19–26.
    1. Pallante P, Battista S, Pierantoni GM, Fusco A. Deregulation of microRNA expression in thyroid neoplasias. Nat Rev Endocrinol. 2014;10:88–101. doi: 10.1038/nrendo.2013.223.
    1. Bargaje R, Hariharan M, Scaria V, Pillai B. Consensus miRNA expression profiles derived from interplatform normalization of microarray data. RNA. 2010;16:16–25. doi: 10.1261/rna.1688110.
    1. Shen Y, Li Y, Ye F, Wang F, Wan X, Lu W, Xie X. Identification of miR-23a as a novel microRNA normalizer for relative quantification in human uterine cervical tissues. Exp Mol Med. 2011;43:358–366. doi: 10.3858/emm.2011.43.6.039.
    1. Thorenoor N, Slaby O. Small nucleolar RNAs functioning and potential roles in cancer. Tumour Biol. 2015;36:41–53. doi: 10.1007/s13277-014-2818-8.
    1. Weber F, Teresi RE, Broelsch CE, Frilling A, Eng C. A limited set of human MicroRNA is deregulated in follicular thyroid carcinoma. J Clin Endocrinol Metab. 2006;91:3584–3591. doi: 10.1210/jc.2006-0693.
    1. 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. 2009;7:1234–1243. doi: 10.1158/1541-7786.MCR-08-0507.
    1. 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. 2008;68:425–433. doi: 10.1158/0008-5472.CAN-07-2488.
    1. Zhang XJ, Ye H, Zeng CW, He B, Zhang H, Chen YQ. Dysregulation of miR-15a and miR-214 in human pancreatic cancer. J Hematol Oncol. 2010;3:46. doi: 10.1186/1756-8722-3-46.

Source: PubMed

Sponsor e collaboratori

Condizioni mediche

Interventi farmacologici

3
Sottoscrivi