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Abstract

PURPOSE:

DNA methylation plays an important role in thyroid oncogenesis. The aim of this study was to investigate the connection between global and local DNA methylation status and to establish the levels of important DNA methylation regulators (TET family and DNMT1) in thyroid tumours: follicular adenoma-FA, papillary thyroid carcinoma-PTC (classic papillary thyroid carcinoma-cPTC and papillary thyroid carcinoma follicular variant fvPTC).

METHODS:

Global DNA methylation profile in thyroid tumours tissue (41 paired samples) was assessed by 5-methylcytosine and 5-hydroxymethylcytosine levels evaluation (ELISA), along with TETs and DNMT1 genes expression quantification. Also, it was investigated for the first time TET1 and TET2 promoter’s methylation in thyroid tumours. BRAF V600E mutation and RET/PTC translocation testing were performed on all investigated samples. In vitro studies upon DNA methylation in K1 thyroid cancer cells were performed with demethylating agents (5-AzaC and vitamin C).

RESULTS:

TET1 and TET2 displayed a significantly reduced gene expression level in PTC, while DNMT1 gene presented a high level of expression. PTC samples presented increased levels of 5-methylcytosine and low levels of 5-hydroxymethylcytosine. 5-methylcytosine levels were associated with TET1/TET2 expression levels. TET1 gene expression was significantly lower in patients positive for BRAF mutation and with RET/PTC rearrangement. TET2 gene was found hypermethylated in thyroid carcinoma patients overall, especially in PTC-follicular variant samples ( $p=$ 0.0002), where TET2 gene expression levels were significantly reduced ( $p=$ 0.0031). Furthermore, the data indicate for all thyroid cancer patients a good sensitivity (81.08%) and specificity (86.49%) regarding the use of TET1 ( $p<$ 0.0001), and TET2 (71.79%, 64.10%, $p=$ 0.0001) hypermethylation as biomarkers for thyroid oncogenesis.

CONCLUSIONS:

These results suggest that TET1/TET2 gene expression and methylation may serve as potential diagnostic tools for thyroid neoplasia. Our study showed that the methylation of TET1 increases in malignant thyroid tumours. fvPTC patients presented lower methylation levels compared to cPTC and could be a discriminatory factor between two cancer types and benign lesions. TET2 is a poorer discriminator between FA and fvPTC, but it can be useful for cPTC identification. K1-cells treated with demethylating agents showed a demethylation effect, especially upon TET2 gene. The cumulative effect of L-AA and 5-AzaC proved to have a potent combined demethylating effect on genes promoter’s activation and could open new perspectives for thyroid cancer therapy.

Keywords

Papillary thyroid carcinoma DNA methylation TETs

1. Introduction

Thyroid carcinoma is one of the most frequent types of endocrine malignancy. Lately, worldwide thyroid cancer incidence has grown, the malignancy being more common among women and often diagnosed between ages 45–54 [1]. Compared with other types of cancer, it presents a low death rate and in the majority of cases is curable following surgery and treatment. According to WHO 2017 thyroid carcinomas are classified as follows: benign, borderline/uncertain and malignant types. For benign type there is only one subtype: follicular adenoma, borderline/uncertain includes hyalinizing trabecular tumour and other encapsulated follicular patterned tumours. As for malignant type: poorly differentiated thyroid carcinoma, anaplastic thyroid carcinoma, Hurthle (oncocytic) cell tumours, follicular thyroid carcinomas (FTC) and papillary thyroid carcinomas (PTC) subtypes [2].

The majority of thyroid differentiated tumours cases are curable while medullary and anaplastic types are more aggressive, presenting an early metastasis potential and overall a poor prognosis. The most commonly diagnosed type of thyroid cancer is papillary thyroid carcinomas up to approximately 85% of all cases [3].

PTC is characterized by genetic alterations such as mutations of BRAF, RAS genes or RET gene rearrangements (RET/PTC) [4]. For instance, BRAF (V600E) mutation is found in approximatively 45% of PTC cases and it is associated with aggressiveness and drug resistance due to heterogeneity [5, 6].

Besides genetic alterations, a key factor that contributes to cancer development and progression is played by epigenetic changes: DNA methylation, non-coding RNAs expression (specifically microRNAs) and in histone modifications, all of which affect the gene expression pattern regulation [7].

Such an important epigenetic alteration often seen in thyroid neoplasia is represented by DNA methylation. Commonly, in thyroid tumours, it has been shown that many tumour suppressor genes can be epigenetically inactivated by promoter’s methylation [8]. p16, PTEN, RAR $\beta$ 2, DAPK or RASSF1A are often found hypermethylated and were associated with thyroid tumours aggressiveness and progression [9, 10, 11, 12, 13, 14].

DNA methylation, a reversible process that occurs within the CpG dinucleotides at the position 5 of cytosine ring (C5) is mediated by a family of enzymes, DNA methyltransferases (DNMTs). DNMT1 is mainly involved in maintenance of DNA methylation pattern while DNMT3A and DNMT3B are responsible for the de novo methylation.

On the other hand, DNA demethylation enzymes have been identified, namely Ten-Eleven Translocation (TET) proteins. TET proteins family consists in three members: TET1, TET2 and TET3 and they are accountable to catalysing the conversion of 5-methylcytosine (5mC) to 5-hydroxymethylcytosine (5hmC), to 5-formylcytosine (5fC) and 5-carboxylcytosine (5caC) [15, 16]. DNA methylation, being a dynamic process and subjected to many changes, affects the cytosine forms (5hmC, 5fC, 5caC) distribution throughout the genome. 5mC levels correlate with transcriptional silencing and are generally found at repetitive and heterochromatic regions of the genome while 5hmC is enriched at promoter regions, ORFs (open reading frames) and intergenic regions and it associates with gene transcriptional activity [17, 18].

DNA methylation changes are connected with TETs and DNMTs functions in human cancers, but little is known about this interplay in thyroid cancer. The present study aims to investigate the connection between global and local DNA methylation status and to establish the levels of important DNA methylation re-gulators (TET family and DNMT1) in thyroid tumours: follicular adenoma-FA, papillary thyroid carcinoma-PTC (classic papillary thyroid carcinoma-cPTC and papillary thyroid carcinoma follicular variant fvPTC).

2. Materials and methods

2.1 Clinical samples and characterization of the study group

Forty-one patients (age: 17–74) with thyroid neoplasia were enrolled and 82 fresh tissue specimens (tumoral and matched nontumoral adjacent tissue) were obtained after surgical thyroidectomy. Inclusion criteria included patients presenting thyroid nodule pathology and suspicious for thyroid carcinoma. The pre-sence of any other malignancies in patient’s history or thyroid aggressive malignant types (anaplastic or medullary) represented the exclusion criteria. Based on the histopathological exam, patients were divided into three groups: patients with FA (follicular adenoma; $n=$ 11; median age $=$ 53), patients with cPTC (classic papillary thyroid carcinoma; $n=$ 9; median age $=$ 57), and patients with fvPTC (PTC follicular variant; $n=$ 21; median age $=$ 47). Clinical and pathological information for thyroid cancer (cPTC and fvPTC) patients is presented in Table 1 and patients were classified according to 7th edition of the TNM/AJCC staging system.

Table 1
Clinical and histopathological characteristics of studied groups

Histopathological characteristics of malignant samples
Characteristic	FA (follicular adenoma)	cPTC (classic papillary thyroid carcinoma)	fvPTC (PTC follicular variant)
Number of cases (N)	11	9	21
Median age (range)	53 (32 $\div$ 66)	57 (31 $\div$ 74)	47 (17 $\div$ 70)
Gender (female/male)	9/2	6/3	16/5
TMN classification/AJCC7
I a/b		2	3
II		1	7
III		3	11
IV a/b		3	0
Differentiation grade
G1		6	21
G2		2	0
G3		1	0
Median tumour size (cm)		2.1 (1.3 $\div$ 11.5)	3 (0.2 $\div$ 6.5)
Tumour encapsulation		3/9	16/21
Tumour invasion		6/9	5/21
Tumour focality (unifocal/multifocal)		8/1	12/9
Lymph node metastasis (yes/no)		4/5	6/15
BRAF mutation (yes/no)		4/5	2/19
RET/PTC rearrangement (yes/no)		8/1	17/4

Informed consent was obtained from each subject prior to the study enrolment.

2.2 Cell culture and drug treatment

Human thyroid carcinoma cell line K1 purchased from European Collection of Authenticated Cell Cultures (ECACC 92030501) is a papillary thyroid carcinoma cell line derived from GLAG-66 cell line [19, 20] and shown to exhibit BRAF V600E mutation [21]. Cells were grown in accordance with manufacturer’s indications: in DMEM Ham’s F12: MCBD (2:1:1) medium supplemented with 2 mM Glutamine and 10% Foetal Bovine Serum (FBS) at 37 ${}^{\circ}$ C and 5% CO ${}_{2}$ . Cells grown at 60–70 % confluence were treated with different concentrations: 2.5, 5, 7.5, 10, 15, 20 $\mu$ M of 5-AzaC (5-Azacytidine; A2385; Sigma-Aldrich; Germany) and 2.5, 5, 7.5, 10, 15, 20, 30 mM L-AA (L-Ascorbic acid; A5960; Sigma-Aldrich; Germany) either alone or in combination. As control, untreated cells were used.

2.3 Drug cytotoxicity assays and analysis of cell viability, apoptosis and cell cycle

This analysis was performed using MTT assay (Sigma-Aldrich; Germany) according to manufacturer’s indications and as previously described [22]. This experiment was done in triplicate and following this, the optimal concentrations and conditions were selected for cells treatment: 5 and 10 $\mu$ M 5-AzaC, 5 and 10 mM of L-AA for 24/48 h. Analysis of cell viability, apoptosis and cell cycle was performed using Apoptosis Detection Kit for flow cytometry (BD Biosciences; San Jose, CA, USA; 556547) according to manufacturer’s instructions using flow cytometer (BD FACSCanto II; BD Biosciences; San Jose, CA, USA). Data was analysed using DIVA 6.2 (Becton Dickinson) and shown as two-color dot plot with FITC-Annexin V (green fluorescence, X axis) vs. PI (red fluorescence, Y axis) [23, 24]. For cell cycle analysis ModFIT software (Becton Dickinson) was used. As a positive control and inducer of apoptosis, camptothecin was used and cells were treated at final concentration of 10 $\mu$ M for 3 h and at 37 ${}^{\circ}$ C.

2.4 DNA isolation and sodium bisulfite treatment

Genomic DNA from all patient’s fresh tissue spe-cimens (nontumoral adjacent/tumoral) as well as from cell cultures treatments was extracted using High Pure PCR Template Preparation kit (Roche, Mannheim, Germany). Nanodrop ND-1000 Spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA) was used for DNA concentration quantification for each sample. Afterwards, the samples presenting A260/280 value of approximately 1.8–2 and a quantity of 1 $\mu$ g of DNA was further used for sodium bisulfite conversion with EpiTect Bisulfite kit (Qiagen, Hilden, Germany) according to manufacturer’s instructions.

2.5 Global DNA methylation assay

Global DNA methylation and hydroxymethylation evaluation was performed on DNA isolated from thyroid tissues through quantification of 5mC and 5hmC genomic levels using ELISA method 5-mC DNA ELISA kit and Quest 5-hmC DNA ELISA kit (Zymo Research, CA, USA). 100 ng of genomic DNA/well from each sample was analysed in accordance with manufacturer’s protocol and absorbance at 420 nm was measured. In order to estimate the amount of 5mC and 5hmC for tested samples two standard curves using kit’s controls were generated. The results are presented as percentage of 5mC and 5hmC in total DNA. The experiments were performed in duplicate.

2.6 RNA isolation

Total RNA was extracted from investigated samples and K1 cell line using TRIzol Reagent (Thermo Fisher Scientific, Waltham, MA, USA) and later purified with RNeasy kit (Qiagen, Hilden, Germany). Analysis of isolated RNA (quantity and quality) was performed by using Experion RNA analysis kit (Bio-Rad, CA, USA), samples presenting RQI (RNA Quality Indicator) values $>$ 7 were used. Further, 2 $\mu$ g of RNA were used for cDNA synthesis using Transcriptor First Strand cDNA Synthesis kit (Roche, Mannheim, Germany) in accordance with manufacturer’s indications.

2.7 Quantitative real-time PCR and quantitative methylation specific real-time PCR (qMSPCR)

mRNA expression levels of TET family members and DNMT1 were quantified by qRT-PCR on ABI 7300 Real Time PCR System (Applied Biosystems, Foster City, CA, USA), using FastStart SYBR Green Master mix (Roche, Mannheim, Germany) following ma-nufacturer’s instructions. 50 ng of cDNA from each biological and cell cultures sample were used and each sample was run in duplicate. For relative normalization GAPDH gene was used as reference. Double normalization was performed to estimate target genes fold change using 2 ${}^{(-\Delta\Delta Cq)}$ method [25]. Specific set of primers were designed using Primer-BLAST (www.ncbi.nlm.nih.gov/tools/primer-blast) and their sequences are listed in Supplementary data (Table 1).

For TET1 and TET2 promoter methylation specific primers design and promoter’s CpG islands prediction, MethPrimer software was used [26]. To estimate TET1 and TET2 genes methylation levels for investigated samples, quantitative methylation specific PCR (qMSPCR) was done on ABI 7300 Real Time PCR System (Applied Biosystems, Foster City, CA, USA) using 50 ng/ $\mu$ l DNA and FastStart SYBR Green Master mix (Roche, Mannheim, Germany) following the manufacturer’s protocol. As positive and negative controls for methylation assays and for standard curves generation CpGenome Universal Methylated DNA and CpGenome Universal Unmethylated DNA were used (Merck Millipore, Massachusetts, USA). All experiments were performed in duplicate and the methylation percentage was calculated using the method described by Fackler et al., % methylation $=$ 100 X [ng methylated gene A/(ng methylated gene A $+$ ng unmethylated gene A)] [27].

Table 2
%5mC and %5hmC levels in Nontumoral adjacent tissue and tumour tissue

%5mC

Normal

PTC

Median, range

26.97 (7.37

\div

47.6)

25.40 (19.60

\div

53.50)

34.68 (18.70

\div

76.98)

P

-value

–

p=

ns.

p=

0.0031

%5mC

cPTC

fvPTC

Median,

range

24.75 (10.09

\div

42.67)

25.4 (19.63

\div

53.51)

29.5 (24.23

\div

43.26)

35.6 (24.23

\div

76.98)

28.96 (11.88

\div

53.51)

33.44 (18.75

\div

67.66)

P

-value

p=

ns.

p=

ns.

p=

ns.

%5hmC

Normal

PTC

Median, range

0.0825 (0.023

\div

0.15)

0.065 (0.029

\div

0.093)

0.049 (0.001

\div

0.084)

P

-value

–

p=

ns.

{\bm{p}}=

0.0078

%5hmC

cPTC

fvPTC

Median, range

0.03 (0.023

\div

0.034)

0.065 (0.029

\div

0.093)

0.0865 (0.026

\div

0.105)

0.0490 (0.001

\div

0.084)

0.0919 (0.02

\div

0.148)

0.0445 (0.003

\div

0.084)

P

-value

p=

ns.

{\bm{p}}=

0.046

{\bm{p}}=

0.0063

2.8 BRAF V600E mutation and RET/PTC translocation in clinical samples

Detection of BRAF V600E mutation in patient’s tissue samples was done through RFLP-PCR technique using specific primers (Supplementary Table S1). For PCR 10 pmol/ $\mu$ l of each primer, 100 ng DNA and GoTaq Master Mix (Promega, Madison, WI, USA) were used following manufacturers recommendation. TspRI restriction enzyme (20 U/ $\mu$ L) was used to digest PCR amplicons by incubation at 65 ${}^{\circ}$ C for 16 h, resulting in the following restriction products: mutant homozygote (2 fragments: 204 bp and 33 bp), mutant heterozygote (4 fragments: 204 bp, 117 bp, 87 bp and 33 bp) and wildtype (3 fragments: 117 bp, 87 bp and 33 bp). The restriction fragments were analysed in 2% agarose gel electrophoresis.

RET/PTC rearrangements were detected using FISH technique with cDNA probes double labelled with fluorochromes (ZytoVision GmbH – Germany) according with manufacturer’s instructions.

2.9 Statistical analysis

Statistical analysis was performed using GraphPad Prism 6 software (GraphPad Software Inc., San Diego, USA). The results are presented as median and, in order to compare the difference between the investigated groups, t-test and Kruskal-Wallis test were used. For correlation analysis Pearson’s correlation test was used. P-values $<$ 0.05 were considered statistically significant.

3. Results

3.1 Evaluation of global methylation profile and the expression of regulatory factors in thyroid neoplasia tissues

Global DNA methylation profile in thyroid tumours was estimated by assessment of 5mC and 5hmC levels as well as gene expression levels of important key factors involved in regulation of DNA methylation (TET family members and DNMT1).

All thyroid tumour samples displayed a significantly higher level ( $p=$ 0.0031) of 5mC (median: 34.68) than nontumoral adjacent tissue (median: 26.97). PTC subjects presented the lowest percentage of 5hmC (median: 0.049) as compared with nontumoral adjacent tissue (median: 0.0825) ( $p=$ 0.0078) (Table 2). For FA no significant differences were noted regarding 5mC and 5hmC levels compared to nontumoral adjacent tissue.

Figure 1.

Expression levels of factors involved in DNA methylation regulation in thyroid tumours. TET1 (A), TET2 (B), TET3 (C) and DNMT1 (D) fold change in studied groups. ${}^{*}p<$ 0.05.

Table 3

Expression levels of DNMT1 and TETs in association with clinical and pathological parameters for PTC (cPTC and fvPTC) samples

	TET1 (median $n$ -fold)		TET2 (median $n$ -fold)		TET3 (median $n$ -fold)		DNMT1 (median $n$ -fold)
Age
$\leqslant$ 55 years	0	.33	$-$ 0	.29	0	.75	1	.18
$>$ 55 years	0	.44	$-$ 0	.42	1	.20	1	.29
${}^{*}p$ -value	0	.40	0	.71	0	.53	0	.56
Gender
F	0	.44	$-$ 0	.39	0	.76	1	.35
M	0	.32	$-$ 0	.29	1	.30	0	.16
${}^{*}p$ -value	0	.87	0	.42	0	.20	0	.019
TNM classification
I	0	.78	$-$ 0	.26	0	.74	1	.22
II	0	.24	$-$ 0	.432	0	.59	0	.62
III	0	.51	$-$ 0	.54	1	.48	1	.83
IV	0	.10	$-$ 0	.06	1	.5	1	.55
${}^{**}p$ -value	0	.31	0	.77	0	.038	0	.22
Tumour differentiation grade
G1	0	.44	$-$ 0	.33	1	.09	1	.14
G2	0	.54	$-$ 0	.64	0	.76	2	.22
G3	0	.072	$-$ 0	.07	2	.62	1	.14
${}^{**}p$ -value	0	.16	0	.305	0	.11	0	.38
Tumour dimension
$\leqslant$ 1.5 cm	0	.83	$-$ 0	.36	1	.11	1	.50
$>$ 1.5 cm	0	.25	$-$ 0	.30	0	.88	1	.09
${}^{*}p$ -value	0	.38	0	.61	0	.84	0	.17
Tumour invasion
Yes	0	.24	$-$ 0	.29	2	.16	1	.50
No	0	.64	$-$ 0	.46	0	.74	0	.95
${}^{*}p$ -value	0	.32	0	.64	0	.0012	0	.30
Lymph node metastasis
Yes	0	.44	$-$ 0	.3	1	.44	1	.1
No	0	.39	$-$ 0	.36	0	.76	1	.2
${}^{*}p$ -value	0	.48	0	.63	0	.1	0	.61
BRAF mutation
Yes	0	.14	$-$ 0	.26	2	.01	1	.29
No	0	.68	$-$ 0	.301	0	.75	1	.18
${}^{*}p$ -value	0	.0269	0	.73	0	.0071	0	.80
RET/PTC rearrangement
Yes	0	.24	$-$ 0	.34	0	.99	1	.12
No	1	.25	$-$ 0	.29	0	.46	2	.0
${}^{*}p$ -value	0	.0156	0	.61	0	.37	0	.29

${}^{*}p$ -value was calculated with unpaired $t$ -test. ${}^{**}p$ -value was calculated with Kruskal-Wallis test.

Further on, analysing TETs and DNMT1 expression levels in thyroid cancer, it was observed that TET1 ( $p=$ 0.0480) and TET2 ( $p=$ 0.0305) display a significantly reduced gene expression level in fvPTC (median TET1 $=$ 0.456, $p=$ 0.0202; median TET2 $=$ 0.300, $p=$ 0.0111) and cPTC (median TET1 $=$ 0.272, $p=$ ns.; median TET2 $=$ 0.475, $p=$ 0.05) and as compared to FA (median TET1 $=$ 1.153; median TET2 $=$ 0.0113). On the other hand, TET3 exhibit a statistically significant high expression in cPTC samples (median TET3 $=$ 2.58, $p=$ 0.0101) as well as a high level of DNMT1 (median DNMT1 $=$ 2.126, $p=$ 0.0196) expression was noted compared to FA (median TET3 $=$ 0.749; median DNMT1 $=$ 0.536) (Fig. 1).

Testing the investigated factors levels in malignant tumour samples showed that patients positive for BRAF mutation exhibit a significantly lower level of TET1 ( $p=$ 0.026) and higher TET3 levels ( $p=$ 0.0071) (Table 3). Also, PTC samples with RET/PTC rearrangement displayed more reduced TET1 levels ( $p=$ 0.015). Moreover, TET3 gene expression was significantly higher in advanced stages (IV) ( $p=$ 0.038) and in patients with tumour invasion ( $p=$ 0.0012).

No significant modifications between % 5mC, % 5hmC levels and mRNA expression levels TETs, DNMT1 were observed in FA samples. For thyroid carcinoma patients a significantly negative correlation was observed between % 5mC and expression levels of TET1 ( $r=$ $-$ 0.4485; $p=$ 0.0147), TET2 ( $r=$ $-$ 0.5832; $p=$ 0.0009) and DNMT1 ( $r=$ $-$ 0.4118; $p=$ 0.0264). Also, in this group of patients a positive correlation with a borderline statistically significance was noticed between % 5hmC and TET3 expression ( $r=$ 0.366; $p=$ 0.0509).

3.2 TET1 and TET2 promoter’s methylation in thyroid cancer

Taking into account the low expression levels for TET1, 2 genes, we analysed promoters’ sequences using MethPrimer bioinformatics tool [26] and the CpG islands were identified. Next, in our study we examined the promoter’s methylation levels of TET1 and TET2 genes in thyroid specimens and their paired nontumoral adjacent tissue.

TET1 promoter’s methylation had the highest percentage of methylation in tumoral samples. For cPTC and fvPTC patients, a statistically significant difference was found between paired nontumoral adjacent and tumoral tissue ( $p=$ 0.0023), respectively $p=$ 0.0002). TET1 gene promoter methylation is an early event in thyroid oncogenesis process being found also in FA samples. Furthermore, cPTC tumoral samples exhibit a significant decreased TET1 mRNA expression level as compared with corresponding nontumoral adjacent tissue ( $p=$ 0.0425) (Table 3).

On the other hand, investigating TET2 methylation in thyroid tumours vs neighbouring nontumoral tissue, it was found that tumoral samples display higher percentage of TET2 methylation, especially fvPTC samples ( $p=$ 0.0016) for which a significantly lower TET2 mRNA expression levels were detected (Table 4).

Table 4
TET1, TET2 gene methylation and expression levels in thyroid tissue samples

Sample

cPTC

fvPTC

Nontumoral adjacent

Tumoral

Nontumoral adjacent

Tumoral

Nontumoral adjacent

Tumoral

TET1

Methylation degree

Median (range)

4.12 (1.04

\div

37.87)

28.55 (10.82

\div

56.36)

19.17 (4.64

\div

48.18)

52.1 (26.09

\div

67.85)

9.95 (0.96

\div

38.61)

29.4 (7.07

\div

65.93)

{}^{*}p

-value

0.0045

0.0023

0.0002

mRNA expression levels

Median

(range)

-

1.82 (

-

2.39

\div

-

0.52)

-

2.06 (

-

2.84

\div

0.06)

-

1.83 (

-

2.31

\div

-

1.08)

-

2.26 (

-

3.1

\div

-

1.48)

-

1.94 (

-

2.76

\div

-

1.06)

-

2.15 (

-

2.9

\div

-

0.68)

{}^{*}p

-value

0.9249

0.0425

0.3721

TET2

Methylation degree

Median

(range)

18.45 (4.14

\div

36.22)

30.45 (6.97

\div

53.3)

21.5 (6.85

\div

36.07)

35.82 (16.66

\div

44.3)

19 (0.58

\div

35.12)

26.74 (10.35

\div

87.01)

{}^{*}p

-value

0.2547

0.0623

0.0016

mRNA expression levels

-

1.41 (

-

1.78

\div

-

1.02)

-

1.59 (

-

\div

-

0.52)

-

1.4 (

-

1.65

\div

-

0.59)

-

1.64 (

-

1.87

\div

-

0.96)

-

1.39 (

-

2.12

\div

-

0.65)

-

1.66 (

-

2.36

\div

-

0.77)

{}^{*}p

-value

0.5953

0.3288

0.0031

${}^{*}p$ -value was calculated using paired $t$ test.

ROC (receiver operating characteristic) analysis was used to evaluate the potential utility of TET1/TET2 hypermethylation as biomarkers for thyroid oncogenesis. In this purpose we calculated area under the ROC (AUROC) curve and sensitivity/specificity. The results showed a good correlation for TET1 gene (AUROC $=$ 0.8685, $p<$ 0.0001) and a fair correlation for TET2 (AUROC $=$ 0.7535, $p=$ 0.0001). The cut-off for TET1 gene promoter methylation was established $>$ 22.44% (Sensitivity $=$ 81.08%, Specificity $=$ 86.49%), and $>$ 22.78% (Sensitivity $=$ 71.79%, Specificity $=$ 64.10%) for TET2 gene (Fig. 2).

Figure 2.

AUROC analysis for TET1 (A) and TET2 (B) gene promoter methylation.

3.3 In vitro studies upon DNA methylation in K1 thyroid cancer cells

Tumour cells might undergo various epigenetic changes, such as DNA hypermethylation, responsible for inducing an imbalance in pro- and anti-apoptotic processes regulation, and so influencing both progression and response to cancer therapy. Several studies have shown that 5-AzaC, a DNA demethylating agent, induces cell cycle arrest and cell death in various type of tumour cells [28, 29] by altering the epigenetic state, but the details of 5-AzaC-induced apoptosis or cell cycle arrest in cancer are not completely understood in thyroid cancer.

In order to evaluate the role of aberrant DNA methylation processes in thyroid cancer 5-AzaC, along with L-AA (known to have a demethylating effect) was used. Analysis of K1 tumour cells viability revealed that 5-AzaC treatment induced a significantly decrease in cell viability dependent of concentration and time (24 or 48 h), which was further enhanced by L-AA treatment combination (Fig. 3 and S1). The cytotoxicity increases with the exposure time, higher concentrations and when the cells are exposed to a combination of the two drugs.

Figure 3.

The effects of 5-AzaC and L-AA treatments in K1 cell line A. Cell viability (%); B. Cytotoxicity (%); C. Early apoptosis $=$ Q4 (%).

Flow cytometric analysis showed that the early apoptosis rate of K1 tumour cells increased about 3–6-fold after exposure for 24 h or 48 h to 5 $\mu$ M and 10 $\mu$ M 5-AzaC, while the early apoptosis rate of K1 tumour cells increased only slightly (1.5-fold) upon treatment with 5 mM or 10 mM L-AA. Further analysis confirmed a synergy between L-AA and 5-AzaC effects in inducing apoptosis in K1 cell line, as evidenced by a robust increase in early apoptosis rate when the cells were co-treated with higher doses of (10 mM) L-AA and (10 $\mu$ M) 5-AzaC for 24 h, but this effect is no longer observed after 48 h treatment (Supplementary Fig. S1).

Figure 4.

Evaluation of TET1 and TET2 factors after 5-AzaC and L-AA treatments in K1 cell line. (A) Modifications in TET1and TET2 relative expression levels in K1 treated cells. (B) TET1 and TET2 genes promoter methylation in K1 with different treatments ${}^{*}p<$ 0.05; ${}^{**}p<$ 0.01; ${}^{***}p<$ 0.001.

Cell cycle distribution analysis of K1 cells treated with 5-AzaC or L-AA individually indicated an increase in the population of cells in S phase after 24 h treatment. In addition, an increase in the S phase of the cell cycle was noted in cells treated 24 h with a combination of 5 $\mu$ M 5-AzaC $+$ 5 mM L-AA without significant differences from the effect induced by each agent alone (Supplementary Fig. S2).

Furthermore, we observed an important increase of G1 phase in cells treated 48 h with both concentrations of L-AA used, accompanied by a decrease of S phase. Also, 5 $\mu$ M/48 h of 5-AzaC induces an increase of G2 phase, while 10 $\mu$ M/48 h of 5-AzaC determines cells arrest in G1 phase. When K1 cells were treated with the combination of the 5-AzaC and L-AA at higher concentrations the results indicate an increase in G2 phase of cell cycle compared with effect of either agent alone (Supplementary Fig. S2).

In K1 cells treated with both agents it was noted a significantly increase of TET1 and TET2 genes expression levels as compared with control. The best results were obtained when cells were treated with high concentrations of both substances (respectively 10 $\mu$ M 5-AzaC and 10 mM L-AA) and after 48 h (Fig. 4). Our results indicate that L-AA enhances the demethylation effect exercised by 5-AzaC this effect being noticeable especially in case of TET2 methylation (Fig. 4).

It has been found also, that combined treatments (5-AzaC and L-AA) lead to a significantly decrease in 5mC content, concomitantly with an increase in 5hmC levels in K1 cell line (Supplementary Fig. S3).

4. Discussion

In recent years, a particularly high interest in cancer research has been attributed to epigenetic changes that occur in tumorigenesis. One of these modifications is DNA methylation and it is now acknowledged that aberrant methylation of genomic DNA represents a signature of the malignant process. Among the epigenetic events that occur in tumorigenesis are: local CpG islands hypermethylation which often takes place at genes promoter’s level (especially in the case of tumour suppressor genes) leading to loss of gene expression, and a global hypomethylation that is related with gene activation (e.g. oncogenes, DNA repetitive elements), loss of genomic imprinting and chromosomal instability [30]. In our previous study we found TP73, WIF1 and PDLIM4 genes promoters are hypermethylated in PTC [31].

Lately studies have highlighted the particular importance of epigenetic changes in thyroid cancer and their prognostic potential, among the most investigated being DNA methylation [32].

Regarding global DNA methylation patterns in thyroid cancer, the available data are rather contradictory and this is mainly due to different analysis methods used in studies. For example, it was showed that 5-methylcytidine immunostaining levels are more reduced in thyroid cancer samples as compared with benign or adjacent normal samples [33]. By contrast, Keelawat et al. found that 5-methylcytidine is significantly higher in well-differentiated thyroid cancer types (FA, FTC, cPTC) than in nontumoral adjacent tissue [34].

A study made by Rodríguez-Rodero et al. using DNA methylation arrays illustrated a different promoter methylation profile in thyroid cancer depending on cancer subtype and that promoter hypomethylation is more frequently found in non-differentiated subtypes [35].

In this study, investigating DNA methylation profile in thyroid cancer we found that PTC tumoral samples present significantly increased levels of 5mC and low levels of 5hmC. An important role for hydro-xymethylation has been showed in cancer where decreased levels of 5hmC are frequently detected [36], like in esophageal squamous cell carcinoma, parathyroid carcinoma, melanoma or glioma [37, 38, 39, 40].

It is now believed that an important feature of malignant transformation along with global reduction of 5hmC levels is the alteration of TET enzymes frequently found mutated or downregulated [10].

The present study revealed alterations in expression levels of TETs genes and DNMT1 in thyroid pathology. Data show that thyroid cancer samples display significantly reduced mRNA levels of TET1 and TET2 compared with benign samples but high mRNA levels of TET3 and DNMT1. Moreover, for PTC patients, TET1 lower levels correlate with BRAF V600E mutation and RET/PTC rearrangement. Decreased levels of TET1 and TET2 have been reported in numerous pathologies. For example, it was demonstrated that low levels of TET1 correlate with tumour grade, metastasis and a poor outcome in renal cancer patients as well in colorectal cancer [41, 42]. At the same time, an association between TET1, TET2 reduced expression levels and cancer progression and a poorer prognosis has been proved in endometrial and breast cancer [43, 44].

The current study showed a good correlation between 5mC and 5hmC levels and TETs expression levels in thyroid carcinoma patients. It was noticed that 5mC le-vels were significantly associated with TET1 and TET2 mRNA expression levels while 5hmC levels correlated with TET3 expression, thus suggesting a role for all these factors in thyroid cancer.

We highlighted for the first-time that higher degrees of methylation in TET1 and TET2 genes promoters are associated with a decrease in their expression levels in thyroid tumours.

The obtained results are supported by other reports that show that TET1 promoter is found hypermethylated in many types of cancer. One such example is breast cancer where TET1 hypermethylation is inversely correlated with its expression and associated with metastasis for which could serve as a potential diagnosis marker [45]. TET1 hypermethylation has been suggested as potential biomarker in colorectal cancer. This gene was found frequently methylated early in colorectal tumorigenesis and is associated also with BRAF mutation presence [46].

An important role in tumorigenesis has been reported for TET2 gene decreased expression or inactivation in haematological cancer [47, 48, 49]. A correlation between reduced levels of TET2 and 5hmC in cancer was found in esophageal squamous cell carcinoma (ESCC) [37] and in parathyroid carcinoma where TET2 levels are reduced due to its promoter hypermethylation [50]. Another mechanism for TET2 gene repression has been demonstrated and it is related to lysine demethylase-KDM2A action that targets and inhibits TET2 in breast cancer leading to DNA methylation and tumour progression [51].

Our study showed that the methylation of TET1 is slightly increased in FA group, but increases in malignant thyroid tumours. fvPTC patients presented lower methylation levels compared to cPTC and could be a discriminatory factor between two cancer types and benign lesions. TET2 is a poorer discriminator between FA and fvPTC, but it can be useful for cPTC identification.

In vitro experiments with demethylating agents demonstrated that the decreased TET1 and TET2 gene expression was due to aberrant gene promoter methylation, the treatments being able to increase the expression levels for both genes. Moreover, investigation of the effect of demethylating agents on K1 cell line showed that vitamin C can enhance the demethylation effect of 5-AzaC and this was best noticed for TET2 gene. Also, an increase in 5hmC levels and a decrease in 5mC content in K1 treated cells were observed. Our findings are consistent with previous studies in ES cells and melanoma cells, that indicate TETs activation and global increase of 5hmC content in treatments with ascorbic acid [52, 53]. The same effect was also noticed in HLE and Huh7 hepatocellular carcinoma cell lines where cells treated with both vitamin C and 5-AzaC displayed increased levels of TET2 and TET3 [54].

The present study shows that 5-AzaC can suppress in vitro viability, induces cytotoxicity, influences cell cycle phases distributions dependent of time and concentration, increases apoptosis of K1 tumour cells and these data may be correlated with the differences observed in the methylation pattern. L-AA supplementation amplified the 5-AzaC effect, reducing viability, increasing apoptosis and inducing G2-cell cycle arrest. Our finding has added value to the growing evidences of L-AA as an important co-factor which influences the activity of 5-AzaC and provides experimental support for novel therapies for thyroid cancer in order to ensure prolonged patient’s survival. Regarding the future therapy, further studies are necessary to evaluate the role of the combination of demethylating agents and optimal dose in vivo animal model.

5. Conclusions

The hypermethylation of TET1/TET2 genes promoters associated with down-regulation of their expression alters global DNA methylation pattern in thyroid cancer. These results may indicate global and local DNA methylation assessment as potential diagnostic tool for this pathology.

Our data demonstrated that L-AA is able to modify the activity of the 5-AzaC in K1 and induce cell cycle arrest which is considered essential in limiting tumour cell progression. The cumulative effect of L-AA and 5-AzaC as demethylating agents on methylation process, apoptosis and cell cycle could open new perspective for thyroid cancer therapy.

Footnotes

Acknowledgments

This research was supported by national partnership research projects: PNII-PCCA 135/2012, Project 433/ID 929/SMIS code 14049, SOP-IEC, Operation 2.2.1, POSCCE 2.2.1, ID 918, SMIS 14045.

Conflict of interest

The authors declare no conflict of interest.

Supplementary data

Supplement Table S1
List of primers used
Gene (NCBI reference sequence)	Forward primer (5’-3’)	Reverse primer (5’-3)	Annealing temperature
BRAF V600E (NG_007873.3)	GCTTGCTCTGATAGGAAAATGAG	GATAGACAGCAGCATCTCAGG	57 ${}^{\circ}$ C
TET1 (NM_030625.2)	ACCTGCAGCTGTCTTGATCG	TTTCCCTGACAGCAGCAACA	60 ${}^{\circ}$ C
TET2 (NM_001127208.2)	GAAAGGAGACCCGACTGCAA	CTGAATGTTTGCCAGCCTCG	60 ${}^{\circ}$ C
TET3 (NM_001287491)	CCTGGACAGGGCATTCGG	GCAGTGCTGCCGAGTCA	60 ${}^{\circ}$ C
DNMT1 (NM_001130823.2)	GCGGTATACCCACCATGACA	AGGCTTTGCCGGCTTCC	60 ${}^{\circ}$ C
GAPDH (NM_002046.5)	CCATCTTCCAGGAGCGAGATCCCT	TGAGCCCCAGCCTTCTTCATGGT	60 ${}^{\circ}$ C
TET1M (NC_000010.11)	GTTTTGCGTTTTTGGTTTTTTC	CCGAAAACATTATTTATCTCCGA	59 ${}^{\circ}$ C
TET1U (NC_000010.11)	GTTTTGTGTTTTTGGTTTTTTTGT	CAAAAACATTATTTATCTCCAAC	57 ${}^{\circ}$ C
TET2M (NG_028191.1)	TTTTTATTTCGTTTTTAATTTTCGC	AATAAAAACCTAAACTACCCTCACG	59 ${}^{\circ}$ C
TET2U (NG_028191.1)	TTTTATTTTGTTTTTAATTTTTGTGT	TAAAAACCTAAACTACCCTCACACC	57 ${}^{\circ}$ C

Supplementary Fig. S1.

Flow cytometric analysis of on cell viability and apoptosis processes in K1 cells treated with 5-AzaC or/and L-AA.

Supplementary Fig. S2.

DNA content analysis of K1 cells treated with 5-AzaC and L-AA by flow cytometry. A-24 h treatment, B-48 h treatment.

Supplementary Fig. S3.

The effects of 5-AzaC and L-AA treatments on DNA methylation in K1 cell line. (A) Modifications in TET3 and (B) DNMT1 relative expression levels in K1 treated cells. (C) 5mC and (D) 5hmC content in K1 treated cells. ${}^{*}p<$ 0.05; ${}^{**}p<$ 0.01; ${}^{***}p<$ 0.001.

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Alterations of regulatory factors and DNA methylation pattern in thyroid cancer

Abstract

PURPOSE:

METHODS:

RESULTS:

CONCLUSIONS:

Keywords

1. Introduction

2. Materials and methods

2.1 Clinical samples and characterization of the study group

Table 1 Clinical and histopathological characteristics of studied groups

2.3 Drug cytotoxicity assays and analysis of cell viability, apoptosis and cell cycle

2.4 DNA isolation and sodium bisulfite treatment

2.5 Global DNA methylation assay

2.6 RNA isolation

2.7 Quantitative real-time PCR and quantitative methylation specific real-time PCR (qMSPCR)

Table 2 %5mC and %5hmC levels in Nontumoral adjacent tissue and tumour tissue

2.9 Statistical analysis

3. Results

3.1 Evaluation of global methylation profile and the expression of regulatory factors in thyroid neoplasia tissues

Table 4 TET1, TET2 gene methylation and expression levels in thyroid tissue samples

5. Conclusions

Footnotes

Acknowledgments

Conflict of interest

Supplementary data

References

Table 1
Clinical and histopathological characteristics of studied groups

Table 2
%5mC and %5hmC levels in Nontumoral adjacent tissue and tumour tissue

Table 4
TET1, TET2 gene methylation and expression levels in thyroid tissue samples