International Journal of Endocrinology
Volume 2015 (2015), Article ID 439370, 11 pages
http://dx.doi.org/10.1155/2015/439370

Research Article

Anaplastic Thyroid Carcinoma: A ceRNA Analysis Pointed to a Crosstalk between SOX2, TP53, and microRNA Biogenesis

Walter Arancio,¹ Valeria Carina,1,2 Giuseppe Pizzolanti,¹ Laura Tomasello,¹ Maria Pitrone,¹ Concetta Baiamonte,¹ Marco Calogero Amato,¹ and Carla Giordano¹

¹Section of Cardio-Respiratory and Endocrine-Metabolic Diseases, Biomedical Department of Internal and Specialist Medicine (Di.Bi.M.I.S.), University of Palermo, Piazza delle Cliniche 2, 90127 Palermo, Italy
²Istituto Ortopedico Rizzoli (IOR), Section of Biology and Genetics, Department of Pathobiology and Medical and Forensic Biotechnology (Di.Bi.Me.F.), University of Palermo, Via Divisi 83, 90100 Palermo, Italy

Received 30 May 2014; Revised 28 August 2014; Accepted 10 September 2014

Academic Editor: Giuliana Salvatore

Copyright © 2015 Walter Arancio et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

It has been suggested that cancer stem cells (CSC) may play a central role in oncogenesis, especially in undifferentiated tumours. Anaplastic thyroid carcinoma (ATC) has characteristics suggestive of a tumour enriched in CSC. Previous studies suggested that the stem cell factor SOX2 has a preeminent hierarchical role in determining the characteristics of stem cells in SW1736 ATC cell line. In detail, silencing SOX2 in SW1736 is able to suppress the expression of the stem markers analysed, strongly sensitizing the line to treatment with chemotherapeutic agents. Therefore, in order to further investigate the role of SOX2 in ATC, a competing endogenous RNA (ceRNA) analysis was conducted in order to isolate new functional partners of SOX2. Among the interactors, of particular interest are genes involved in the biogenesis of miRNAs (DICER1, RNASEN, and EIF2C2), in the control cell cycle (TP53, CCND1), and in mitochondrial activity (COX8A). The data suggest that stemness, microRNA biogenesis and functions, p53 regulatory network, cyclin D1, and cell cycle control, together with mitochondrial activity, might be coregulated.

1. Introduction

Anaplastic thyroid carcinoma (ATC) is a rare endocrine tumour. Its morphological features resemble undifferentiated neoplasm. Due to severe metastasis development and to the rapid fatal course, surgery is rarely performed. Radiotherapy and chemotherapy are also not very effective. It has been suggested that those standard therapies are ineffective because they are not able to efficiently target a subpopulation of ATC cells, called the cancer-initiating cells or cancer stem cells (CSCs). It has been proposed that CSCs possess stem-cell-like features, are at the core of the development of many tumours, especially undifferentiated ones like ATC, are responsible for the recurrence of the tumour and metastasis formation, and usually are very resistant to classical therapies.

Despite many controversies regarding the cancer stem cell model, it has the potential to drive the discovery of innovative treatments that may eradicate the very chemoresistant core of cancer [1]. In this connection, the CSC model is the sum of many hypotheses that have arisen to explain the most vexing aspects of cancer: metastasis, relapse, and therapeutic resistance [2]. In this perspective, CSC research holds out promise for improved treatment outcomes, in particular, as regards overcoming resistance to chemotherapy on solid tumours [1].

The most accepted CSC model makes use of a new paradigm of cellular differentiation, in which cancer cells can dedifferentiate toward more primitive, stem-like phenotypes [2]. The dedifferentiation seems to be highly heterogeneous, giving an explanation to the observed discontinuous behaviour of many cancers [2]. Alternatively, CSCs might arise from transformed stem cells in the stem niche [1, 2].

Similar to normal stem cells, CSCs have the ability both to self-renew and to give rise to differentiated tumour cells, are responsible for the organization of a tumour mass [3], and are tumorigenic when transplanted into an animal host [4]. CSCs have been identified in a wide range of human tumours [3]. At the molecular level, CSCs are usually enriched in cell surface markers such as CD44, CD24, and CD133, while Wnt/β-catenin, Notch, and Hedgehog signalling pathways seem to have key roles in CSC properties [4]. Specific microRNA signatures have been identified in many CSCs [4] that seem to play a role in the epithelial-mesenchymal transition [4].

Regarding ATC, it has been hypothesized that the tumour initiates from transformed thyroid stem cells, rather than from differentiated thyrocytes undergoing a conventional multistep carcinogenesis model [5–7].

The rarity and rapid fatal nature of ATC has led to limited ex vivo studies. Here we describe an in vitro study on a well-validated ATC cell line: SW1736. The SW1736 cell line is characterized by a high percentage of population with stem cell-like properties and high expression of several stem markers (SOX2, OCT4, NANOG, C-MYC, SSEA4, and the ABCG2 transporter) [8]. Interestingly, SOX2 silencing downregulates in trans the expression of other stem cell markers and sensitizes ATC cells to treatment with classical chemotherapeutics such as cisplatin and doxorubicin [8]. This suggests that the stem cell factor SOX2 could have a preeminent hierarchical role in determining the characteristics of stem cells in SW1736 ATC cell line.

Therefore, in order to further investigate the role of SOX2 in ATC, a bioinformatic analysis of the functional network of SOX2 was performed. In detail, a competing endogenous RNA (ceRNA) analysis was conducted. This kind of analysis is able to predict genes functionally correlated with the bait gene rather than physically associated with it [9, 10]. The ceRNA hypothesis is based on the rationale that RNA molecules can regulate one another via microRNAs [9, 10]. ceRNAs are RNAs that share miRNA recognition elements, thereby regulating each other by influencing the available level of miRNA [9, 10]. In the past, ceRNA analysis made it possible to isolate several genes and functional networks related to cancer development, ageing, and homeostasis [11–19].

2. Materials and Methods

2.1. MirWALK Analysis

miRWalk is a comprehensive database that provides information on miRNA from humans, mice, and rats on their predicted as well as validated binding sites on their target genes. The validated targets module [20] hosts experimentally verified miRNA interaction with associated genes.

Using the miRWalk [20] data and embedded tools, we collected the microRNAs that have been reported in the literature to regulate the main transcript from the SOX2 locus (Table 1).

Table 1: microRNAs that have been reported in the literature to regulate the main transcript from the SOX2 locus.

hsa-let-7a
hsa-miR-125b-2*
hsa-miR-1914*
hsa-miR-30a*

hsa-let-7a*
hsa-miR-126
hsa-miR-1915
hsa-miR-30b

hsa-let-7b
hsa-miR-126*
hsa-miR-1915*
hsa-miR-30b*

hsa-let-7b*
hsa-miR-134
hsa-miR-200c
hsa-miR-30c

hsa-let-7c
hsa-miR-137
hsa-miR-200c*
hsa-miR-30c-1*

hsa-let-7c*
hsa-miR-142-3p
hsa-miR-203
hsa-miR-30c-2*

hsa-let-7d
hsa-miR-143
hsa-miR-204
hsa-miR-30d

hsa-let-7d*
hsa-miR-143*
hsa-miR-205
hsa-miR-30d*

hsa-let-7e
hsa-miR-145
hsa-miR-206
hsa-miR-30e

hsa-let-7e*
hsa-miR-145*
hsa-miR-21
hsa-miR-30e*

hsa-let-7f
hsa-miR-155
hsa-miR-21*
hsa-miR-452

hsa-let-7f-1*
hsa-miR-155*
hsa-miR-223
hsa-miR-452*

hsa-let-7f-2*
hsa-miR-17
hsa-miR-223*
hsa-miR-9

hsa-let-7g
hsa-miR-17*
hsa-miR-296-3p
hsa-miR-9*

hsa-let-7g*
hsa-miR-183
hsa-miR-296-5p
hsa-miR-92a

hsa-let-7i
hsa-miR-183*
hsa-miR-302a
hsa-miR-93

hsa-let-7i*
hsa-miR-1908
hsa-miR-302a*
hsa-miR-93*

hsa-miR-100
hsa-miR-1909
hsa-miR-302b

hsa-miR-100*
hsa-miR-1909*
hsa-miR-302b*

hsa-miR-106b
hsa-miR-1910
hsa-miR-302c

hsa-miR-106b*
hsa-miR-1911
hsa-miR-302c*

hsa-miR-125a-3p
hsa-miR-1911*
hsa-miR-302d

hsa-miR-125a-5p
hsa-miR-1912
hsa-miR-302d*

hsa-miR-125b
hsa-miR-1913
hsa-miR-302f

hsa-miR-125b-1*
hsa-miR-1914
hsa-miR-30a

This set of miRNAs was inserted into the miRWalk analysis tool [20] to collect any human mRNA that has been reported to be regulated by them. Then the genes collected were organized in a hierarchical order for the number of validated microRNA hits (Table 2). The more microRNAs are shared between the bait SOX2 gene and the candidate genes, the higher the possibility that the candidate gene transcripts can act as SOX2 ceRNAs. All analyses were updated to December 15, 2013.

Table 2: ceRNA organized in hierarchical order for the number of validated microRNA hits.

Gene
Hits
Gene
Hits

DICER1
35
SLC27A4
10

TP53
26
RUNX1
10

RNASEN
22
RRBP1
10

EIF2C2
22
PAK3
10

COX8A
22
NFKB1
10

CCND1
22
LIN28
10

MYC
20
KLF4
10

CDKN1A
20
FRAP1
10

BCL2
20
EIF2C1
10

AKT1
19
CREB1
10

PTEN
18
CDK6
10

CDKN2A
18
APC
10

VEGFA
16
TWIST1
9

EGFR
16
SYNE1
9

TGFB1
14
SIRT1
9

KRAS
14
PRDM1
9

JUN
14
MCL1
9

HMGA2
14
HMOX1
9

ERBB2
14
DNMT1
9

ZEB1
13
DDX20
9

TLR4
13
CKAP4
9

SSSCA1
13
CDKN1C
9

MET
13
BRCA1
9

TNF
12
ZNF828
8

SOCS1
12
TP63
8

PIK3CA
12
TIMM8A
8

ESR1
12
STMN1
8

DGCR8
12
SCPEP1
8

CEBPB
12
ROS1
8

CD4
12
PSAT1
8

TGFBR2
11
PDCD4
8

STAT3
11
MYCN
8

PROM1
11
MAPK3
8

NPC1
11
JAK2
8

IL6
11
IL1B
8

EPHB2
11
IFNG
8

E2F3
11
IFNA1
8

E2F1
11
HMGA1
8

CDKN1B
11
GEMIN4
8

CTNNB1
8

CD19
8

BCL2L11
8

BAX
8

2.2. GeneMANIA Analysis

Arbitrarily, the top 6 genes together with SOX2 were analysed using the GeneMANIA [21] tool that helps to predict the functions of a set of genes and to predict in which gene ontology (GO) functions the set of genes might be involved (Figure 1) (Table 3). The GO functions reported are the ones with a false discovery rate (FDR) < 0.1. All analyses were updated to December 15, 2013.

Table 3: Gene ontology of SOX2 ceRNA network by GeneMANIA.

Function
False discovery rate
Coverage

Query genes
n/a
7/7

Gene silencing by RNA

9/33

Gene silencing

9/59

Gene silencing by miRNA

7/25

Posttranscriptional gene silencing by RNA

7/28

Posttranscriptional gene silencing

7/28

Regulation of gene expression, epigenetic

8/110

Production of miRNAs involved in gene silencing by miRNA

5/13

dsRNA fragmentation

5/14

Production of small RNA involved in gene silencing by RNA

5/14

Cellular response to dsRNA

5/19

Response to dsRNA

5/21

Respiratory electron transport chain

7/102

Electron transport chain

7/103

Cellular respiration

7/136

Mitochondrial membrane

8/274

ncRNA metabolic process

7/185

Mitochondrial envelope

8/297

Mitochondrial inner membrane

7/195

Organelle inner membrane

7/208

Posttranscriptional regulation of gene expression

7/259

Energy derivation by oxidation of organic compounds

7/279

Cellular response to organic cyclic compound

5/101

ncRNA processing

5/105

Endonuclease activity, active with either ribo- or deoxyribonucleic acids and producing 5′-phosphomonoesters

3/14

Endoribonuclease activity

3/17

Response to organic cyclic compound

5/183

Stem cell maintenance

3/35

Stem cell development

3/37

Ribonuclease activity

3/44

Endonuclease activity

3/44

Stem cell differentiation

3/57

Somatic stem cell maintenance

2/11

Germplasm

2/11

P granule

2/11

Pole plasm

2/11

Ribonucleoprotein granule

3/71

Endodermal cell fate commitment

2/13

Figure 1: SOX2 ceRNA network by GeneMANIA.

2.3. Cell Cultures

The SW1736, 8505C, C643, FRO, BCPAP, TPC-1, and WRO cell lines were cultured in Dulbecco’s modified Eagle’s medium high glucose medium supplemented with 10% fetal bovine serum and 5% glutamine. Cultures were maintained in 5% carbon dioxide at 37°C in a humidified incubator.

2.4. Small Interfering RNA (siRNA) Transfection

siRNA transfection in SW1736 cells was performed using INTERFERin transfection agent (Polyplus-Transfection, Illkirch, France), according to the manufacturer’s instructions. Briefly, the transfection agent and the siRNA complex were added to the cells and incubated for 72 hours for RNA extraction and analysis. The final concentration of SOX2 siRNA was 100 nM. Each assay was performed in triplicate in at least three independent experiments. SOX2 was silenced using Stealth SiRNA SOX2 HSS144045 (Invitrogen, Milan, Italy). siCONTROL Stealth siRNA Negative Control was used as a control (Invitrogen, Milan).

2.5. SOX2 Coding Sequence Vector and Transient Transfection

The vector used was taken from Addgen (http://www.addgene.org/) (Plasmid 26817): pcDNA3.3_SOX2; and transformation into SW1736 cells was performed using Xfect transfection agent (Clontech Laboratories, Inc. A Takara Bio Company) according to the manufacturer’s instructions. The transfection agent and plasmid were added to the cells and incubated for 72 hours for RNA extraction and analysis.

2.6. SOX2 3′ Untranslated Region (3′UTR) Vector and Transfection

The vector was synthesized in service by Eurofins genomics (https://www.eurofinsgenomics.eu) using a pcDNA 3.1 backbone and a chemically synthesized 3′UTR (as reported in http://mybioinfo.info/exon_display.php?tax_id=9606&gene_id=GeneID: 6657) (Table 4).

Table 4: SOX2 3′ untranslated region (3′UTR).

5′GGGCCGGACAGCGAACTGGAGGGGGGAGAAATTTTCAAAGAAAAACGAGGGAAATGGGAGGGGTGCAAAA

GAGGAGAGTAAGAAACAGCATGGAGAAAACCCGGTACGCTCAAAAAGAAAAAGGAAAAAAAAAAATCCCATC

ACCCACAGCAAATGACAGCTGCAAAAGAGAACACCAATCCCATCCACACTCACGCAAAAACCGCGATGCCGAC

AAGAAAACTTTTATGAGAGAGATCCTGGACTTCTTTTTGGGGGACTATTTTTGTACAGAGAAAACCTGGGGA

GGGTGGGGAGGGCGGGGGAATGGACCTTGTATAGATCTGGAGGAAAGAAAGCTACGAAAAACTTTTTAAAAG

TTCTAGTGGTACGGTAGGAGCTTTGCAGGAAGTTTGCAAAAGTCTTTACCAATAATATTTAGAGCTAGTCTCC

AAGCGACGAAAAAAATGTTTTAATATTTGCAAGCAACTTTTGTACAGTATTTATCGAGATAAACATGGCAAT

CAAAATGTCCATTGTTTATAAGCTGAGAATTTGCCAATATTTTTCAAGGAGAGGCTTCTTGCTGAATTTTGA

TTCTGCAGCTGAAATTTAGGACAGTTGCAAACGTGAAAAGAAGAAAATTATTCAAATTTGGACATTTTAATT

GTTTAAAAATTGTACAAAAGGAAAAAATTAGAATAAGTACTGGCGAACCATCTCTGTGGTCTTGTTTAAAAA

GGGCAAAAGTTTTAGACTGTACTAAATTTTATAACTTACTGTTAAAAGCAAAAATGGCCATGCAGGTTGACA

CCGTTGGTAATTTATAATAGCTTTTGTTCGATCCCAACTTTCCATTTTGTTCAGATAAAAAAAACCATGAAAT

TACTGTGTTTGAAATATTTTCTTATGGTTTGTAATATTTCTGTAAATTTATTGTGATATTTTAAGGTTTTCCC

CCCTTTATTTTCCGTAGTTGTATTTTAAAAGATTCGGCTCTGTATTATTTGAATCAGTCTGCCGAGAATCCAT

GTATATATTTGAACTAATATCATCCTTATAACAGGTACATTTTCAACTTAAGTTTTTACTCCATTATGCACAG

TTTGAGATAAATAAATTTTTGAAATATGGACACTGAAA3′

2.7. Reverse-Transcription PCR and Real-Time Quantitative PCR

Total RNA was extracted from cells using the RNeasy Mini Kit (Qiagen, Milan, Italy), including a digestion step with DNase I. RNA quantity and quality were assessed using the Nanodrop 2000 (Thermo Scientific, Wilmington, USA). The RNA extracted was reverse-transcribed with Random Hexamers (Applied Biosystems, Darmstadt, Germany) and Improm II Reverse Transcriptase (Promega Italia, Milan, Italy), according to the manufacturer’s protocol. Primer pair sequences are reported in Table 5.

Table 5: RT-PCR primer pairs.

Gene
Forward primer 5′ > 3′
Reverse primer 5′ > 3′

SOX2 CDS
GGAGACGGAGCTGAAGCCGC
GACGCGGTCCGGGCTGTTTT

DICER1
CTTTGCAACCCCTCAGCAT
TCATGAATTGCTTCTTGTTGC

TP53
ATCTACTGGGACGGAACAGC
GTGAGGCTCCCCTTTCTTG

RNASEN
CACCGAGATCACAGTCATGG
TGTCTTCTCCTGTCGGGACT

EIF2C2
TCCACCTAGACCCGACTTTG
AACTCTCCTCGGGCACTTCT

COX8A
TTACCTCCTGCTTCGTGACC
CACTCTGGCCTCCTGTAGGT

CCND1
ATGCCAACCTCCTCAACG
GGACCTCCTTCTGCACACAT

SOX2 3′UTR
CACCGGGCCGGACAGCGAACTGGAGGGGGG
TTTCAGTGTCCATATTTCAAAAATTTATTTATC

-ACTIN
GGACTTCGAGCAAGAGATGG
AGCACTGTGTTGGCGTACAG

The reactions were performed as follows: 5′ at 94°C, 30 cycles (30′′ at 94°C, 30′′ at 55°C, 30′′ at 72°C), 5′ at 72°C, and stocked at 4°C. The only exception was the amplification of the SOX2 3′UTR, for which the following was done: 5′ at 94°C, 30 cycles (30′′ at 94°C, 30′′ at 55°C, 90′′ at 72°C), 5′ at 72°C, and stocked at 4°C. Expression was analyzed by real-time quantitative PCR (qRT-PCR) using Quantitect SYBR Green PCR kit (Qiagen, Milan, Italy). All reactions were performed using a Rotor-gene Q Instrument (Qiagen, Milan, Italy). The data were analysed using the REST software [22].

2.8. Pool of Normal Thyroid Tissue

A pool of RNA from normal thyroid tissue specimens was used, as described in [23].

2.9. Limbal Stem Cell

A pool of RNA from limbal stem cells specimens was used, as described in [24].

2.10. Lymphocytes

Peripheral blood samples of a healthy volunteer were collected in tubes containing ethylenediaminetetraacetic acid (EDTA, 1 mg/mL) after 8 hours’ fasting. Lymphocytes were isolated by lympholyte (CEDARLANE, Burlington, Ontario, Canada), according to the manufacturer’s instructions.

2.11. Statistical Analysis

We used the SPSS 13 software, Windows edition, for all our statistical analyses. Correlations were determined using Spearman’s test (nonparametric equivalent for Pearson’s test). was considered statistically significant.

3. Results

Previous data [8] suggested that the stem cell factor SOX2 possesses a preeminent hierarchical role in determining stemness characteristics in the SW1736 ATC cell line. With the final aim of investigating the role of SOX2 in ATC, a bioinformatic ceRNA analysis [9, 10] of the functional network of SOX2 was performed.

Using the miRWalk [20] data and embedded tools, we collected the microRNAs that have been reported in the literature to regulate the main transcript from the SOX2 locus (Table 1).

This set of miRNAs was inserted into the miRWalk analysis tool [20] to collect any human mRNA that has been reported to be regulated by them. Then the collected genes were organized in a hierarchical order for the number of validated microRNA hits (Table 2). The more microRNAs are shared between SOX2 and the candidate genes, the stronger the putative competitive effect that is at the core of the ceRNA hypothesis. The first six top level interactors were arbitrary selected for further analyses. The top level SOX2 interactors in this ceRNA analysis are DICER1, EIF2C2, and RNASEN, involved in miRNA biogenesis and functions [25]; the most studied antioncogene TP53, worthy of note because of its suggested role in stemness [26]; the nuclear-coded mitochondrial Cytochrome C Oxidase Subunit VIIIA COX8A [27]; and CCND1, the cyclin D coding gene [28].

Amongst the lesser interactors reported in Table 2, other genes might be worth studying in the future, especially for their involvement in oncogenesis (such as MYC, BCL2, PTEN, KRAS, JUN, and many others).

This study aimed to analyse whether a relationship might exist between the 6 top level interactors (DICER1, EIF2C2, RNASEN, TP53, COX8A, and CCND1) and SOX2 in the ATC cell line SW1736.

With this purpose in mind, the six interactors together with SOX2 were analysed by GeneMANIA software [21] to verify whether their putative network (Figure 1) might be enriched in some GO annotations. Unsurprisingly, the analysis revealed a statistically significant enrichment of miRNA-mediated, posttranscriptional gene silencing activities (Table 3).

Then we tried to establish in the SW1736 ATC cell line whether perturbations in the transcriptional state of SOX2 might alter in trans the transcriptional state of the ceRNAs identified. When we knocked down SOX2 transcripts via specific siRNA, all the ceRNAs were coherently downregulated in trans in RT-PCR analyses, as expected. The effect of the downregulation varied from one ceRNA to another but was always statistically significant [22] (Figure 2(a)) (Table 6). To further investigate whether the effect could be mediated by the impaired transcriptional factor activity of the protein coded by SOX2, we evaluated whether the overexpression of the coding sequence of SOX2 could have some in trans effects on ceRNAs. The coding sequence lacks the 3′ untranslated region (3′UTR) that mainly bears the regulation mediated by miRNAs [29]. Our data indicate that no such effect occurs (Figure 2(b)) (Table 7), so the trans effect highlighted in (Figure 2(a)) is likely to be due to the endogenous miRNA competition, as in our hypothesis, rather than a classical interaction mediated by the proteic transcriptional factor SOX2. Finally, we investigated whether the overexpression of SOX2 3′UTR might have any effects in trans in the SW1736 ATC cell line. The effects were very modest, if present at all, but in line with the modulation that occurs during the competing events [9, 10]. The most notable effect was the positive correlation, as expected, with the expression of EIF2C2 and SOX2 itself (Figure 2(c)) (Table 8).

Table 6: Example of REST analysis on SOX2 and SOX2 ceRNAs levels of transcription quantified by RT-PCR compared to controls and normalized against -actin expression in SW1736 ATC cell line after SOX2 silencing. P(H1) is the probability of the alternative hypothesis that the difference between sample and control groups is due only to chance.

Gene
Reaction efficiency
Expression
Std. error
95% C.I.
P(H1)
Result

SOX2
0.6375
0.471
0.416–0.538
0.384–0.580
0.000
DOWN

DICER1
0.6125
0.442
0.373–0.523
0.366–0.533
0.000
DOWN

TP53
0.7025
0.394
0.364–0.427
0.348–0.445
0.170
DOWN

RNASEN
0.73
0.458
0.444–0.472
0.444–0.472
0.000
DOWN

EIF2C2
0.7475
0.355
0.326–0.386
0.320–0.394
0.000
DOWN

COX8A
0.69
0.635
0.572–0.705
0.560–0.720
0.000
DOWN

CCND1
0.6875
0.572
0.556–0.588
0.556–0.588
0.000
DOWN

Table 7: Example of REST analysis on SOX2 and SOX2 ceRNAs levels of transcription quantified by RT-PCR compared to controls and normalized against -actin expression in SW1736 ATC cell line after SOX2 coding sequence (CDS) overexpression. P(H1) is the probability of the alternative hypothesis that the difference between sample and control groups is due only to chance.

Gene
Reaction efficiency
Expression
Std. error
95% C.I.
P(H1)
Result

SOX2
0.63
66.783
41.627–98.592
37.461–120.196
0.000
UP

DICER1
0.67
0.847
0.750–0.981
0.677–1.034
0.131

TP53
0.8175
1.099
1.000–1.198
0.953–1.261
0.136

RNASEN
0.7475
0.840
0.707–1.001
0.672–1.059
0.131

EIF2C2
0.7725
1.015
0.829–1.246
0.788–1.320
0.854

COX8A
0.765
1.430
1.197–1.699
1.136–1.788
0.000
UP

CCND1
0.755
0.861
0.763–0.947
0.721–1.056
0.080

Table 8: Example of REST analysis on SOX2 and SOX2 ceRNAs levels of transcription quantified by RT-PCR compared to controls and normalized against -actin expression in SW1736 ATC cell line after SOX2 3′UTR overexpression. P(H1) is the probability of the alternative hypothesis that the difference between sample and control groups is due only to chance.

Gene
Reaction efficiency
Expression
Std. error
95% C.I.
P(H1)
Result

SOX2
0.655
1.414
1.176–1.710
1.102–1.818
0.000
UP

DICER1
0.7025
1.207
0.952–1.536
0.912–1.600
0.680

TP53
0.77
1.105
0.918–1.332
0.881–1.386
0.661

RNASEN
0.75
1.039
0.889–1.222
0.833–1.299
0.830

EIF2C2
0.765
1.508
1.393–1.641
1.314–1.733
0.169
UP

COX8A
0.785
0.879
0.733–1.068
0.669–1.160
0.680

CCND1
0.7425
1.226
1.045–1.462
0.937–1.611
0.341

SOX2 3′UTR
0.64
3.092
2.577–3.762
2.330–4.119
0.000
UP

(a)

(b)

(c)

Figure 2: Example of SOX2 and SOX2 ceRNAs levels of transcription quantified by RT-PCR compared to controls and normalized against β-actin expression in SW1736 ATC cell line. Whiskers represent the standard errors. (a) Analysis of SOX2 silencing. (b) Analysis of SOX2 coding sequence overexpression. (c) Analysis of SOX2 3'UTR overexpression.

The experiments previously described looked into the effects of perturbation of the expression of SOX2 on the expression of ceRNA genes in an ATC cell line. We then endeavoured to see whether any correlation might exist between the basal expression of SOX2 and the ceRNA genes in different specimens. In detail, we analysed by RT-PCR the relative expression of SOX2 and SOX2 ceRNAs compared to β-ACTIN expression in SW1736 ATC cell line, in 8505C ATC cell line, C643 ATC cell line, FRO ATC cell line, BCPAP papillary thyroid carcinoma (PTC) cell line, TPC-1 PTC cell line, WRO follicular thyroid carcinoma, and a pool of normal thyroid tissues present in the laboratory from previous experiments [23], a pool of limbal stem cells [24], and isolated lymphocytes from a male donor of 36 years old (see Supplementary Table 1 in Supplementary Material available online at http://dx.doi.org/10.1155/2014/439370). Interestingly, this analysis suggested a correlation in the basal expression of DICER1, RNASEN, and EIF2C2 (Table 9), as can be expected of genes whose functions are strictly coregulated in the biogenesis and function of microRNA, but surprisingly their basal expression seemed also to be somehow related to the basal expression of TP53 (Table 9), suggesting interesting scenarios that will be discussed shortly.

Table 9: Spearman two-tailed test correlations between basal gene expressions (as reported in Supplementary Table 1) among SW1736, 8505C, C643, FRO, BCPAP, TPC-1, WRO, normal thyroid pool, limbal stem cells, and lymphocytes.

SOX2
DICER1
TP53
RNASEN
EIF2C2
COX8A
CCND1

SOX2

    Rho
1
0.152
0.2
−0.176
0.042
0.321
−0.467

0.676
0.580
0.627
0.907
0.365
0.174

DICER1

    Rho
0.152
1
0.939
0.709
0.952
0.030
−0.067

0.676
—
<0.001
0.022
<0.001
0.934
0.855

TP53

    Rho
0.200
0.939
1
0.770
0.939
−0.115
−0.042

0.580
<0.001
—
0.009
<0.001
0.751
0.907

RNASEN

    Rho
−0.176
0.709
0.770
1
0.842
−0.382
0.261

0.627
0.022
0.009
—
0.002
0.276
0.467

EIF2C2

    Rho
0.042
0.952
0.939
0.842
1
−0.127
0.067

0.907
<0.001
<0.001
0.002
—
0.726
0.855

COX8A

    Rho
0.321
0.030
−0.115
−0.382
−0.127
1
−0.127

0.365
0.934
0.751
0.276
0.726
—
0.726

CCND1

    Rho
−0.467
−0.067
−0.042
0.261
0.067
−0.127
1

0.174
0.855
0.907
0.467
0.855
0.726
—

4. Discussion

The ceRNA bioinformatics analysis pointed to a list of genes that could be functionally coregulated with the stem transcriptional factor SOX2 by a crosstalk mediated by several miRNAs. In our analysis, we used interactions reported in the literature instead of bioinformatically predicted ones as done in the past [17–19]. This approach makes it possible to harvest more solid and reliable data, though it is easier to collect genes that have been previously analysed.

Our experiments were pursued in an ATC cell line that has previously been demonstrated to constitutively express SOX2 that functionally possesses a preeminent hierarchical role on many other stem cell factors [8], suggesting a leading role in the maintenance of the stemness feature in this cell line. ATC represents a very good candidate for a cancer highly enriched in CSCs, which probably are at the core of its unfavourable outcome [1–7]. For these reasons, it is both important to understand the regulatory network that underlies the functions of SOX2 in ATC, and at the same time an ATC cell line is a very good candidate for studying the SOX2 network.

Looking at the cross-regulation between SOX2 and the most probable ceRNAs that we isolated, many if not all the ceRNAs analysed seem to be responsive to alterations in the transcriptional state of SOX2 transcripts, independently of the coded protein, suggesting a regulatory network strictly based on noncoding-RNAs (ncRNAs). The most striking evidence is the effect of siRNA-mediated silencing on SOX2, where all the ceRNAs are accordingly downregulated (Figure 1(a)) (Table 6). By contrast, the overexpression of the SOX2 CDS alone seems to have almost no effect at all (Figure 1(b)) (Table 7). In contrast, the overexpression of the 3′UTR of the SOX2 transcripts seems to have an upregulation effect in trans, even if not to a great degree (Figure 1(c)) (Table 8). The 3′UTR of transcripts is the portion of messengers that is likely to bear the vast majority of regulation mediated by microRNAs [30]. The data reported here are consistent with our hypothesis, so it is reasonable to point to the ceRNAs isolated as potential functional interactors with SOX2, at least in the SW1736 ATC cell line.

The interactors isolated pointed to a central role of microRNA biogenesis and functions in SOX2 activities (Table 3) and hence in stemness, as other authors have recently suggested [29]. Here we report that probably the transcription of SOX2 stem factors and of Dicer (DICER1), Ago2 (EIF2C2), and Drosha (RNASEN) is coregulated by a microRNA network. In detail, Drosha is a RNA-specific endoribonuclease that is involved in the initial nuclear step of microRNA biogenesis. Dicer is a cytoplasmic endoribonuclease that plays a central role in the production of short interfering RNAs (siRNA) and mature microRNAs. SiRNAs and microRNAs serve as a guide to directing the RNA-induced silencing complex (RISC) to complementary RNAs to degrade them or prevent their translation. Ago2 is the essential proteic core of the RISC complex. Overall, the miRNA pathway is a means to specifically regulate the expression of target genes that seem to directly and indirectly affect tumorigenesis [31].

The SOX2 ceRNA TP53 gene codes for p53, one of the most studied genes in relation to cancer development [32]. It is also often mutated in ATC towards a nonfunctional form [33]. Nevertheless, even a mutated form, if transcribed, can still exert its regulatory functions via its transcript (e.g., its 3′UTR). In this perspective, homozygous deletion of the locus or full silencing of the gene perturbs the network differently from a null mutation, which still permits transcription from the locus. In the authors’ opinion, this distinction is often not taken into account. It is interesting to note that some authors have suggested a role of p53 in homoeostasis of the stem niche [26] and in microRNA biogenesis [34], setting it at a crossroads between cancer, stemness, and microRNA biogenesis and functions. Our data are in support of this interpretation, all the more so because the basal transcription of TP53 seems to be correlated with the basal transcription of DICER1, EIF2C2, and RNASEN in the specimens that we analysed, many of them from ATC and other thyroid cell lines (Table 9).

The SOX2 ceRNA CCND1 codes for cyclin D1, the regulatory subunit that promotes G1/S cell-cycle progression and is involved in oncogenesis. It has been reported that cyclin D1 induces Dicer expression in vitro and in vivo and vice versa and their expression significantly correlates each other (at least in some subtypes of human breast cancer). It has been suggested that cyclin D1 induction of Dicer coordinates microRNA biogenesis [28, 35]. Our data are in line with the previous results and add a new level of possible crosstalk between DICER1 and CCND1, suggesting novel actors in the network previously isolated, such as SOX2 or TP53. It is likely that cross-regulation between cyclin D1 and Dicer might occur in other cancers, especially in ATC, which are enriched in SOX2 producing cells [8], which we suggest is part of the network.

The role of COX8A is more difficult to appraise. The protein encoded by this gene is the terminal enzyme of the respiratory chain that leads to the production of the electrochemical gradient across the inner mitochondrial membrane. Recent discoveries suggest central roles of mitochondria in the maintenance of pluripotency, differentiation, reprogramming, and ageing [36]. Our data suggest possible crosstalk between a crucial nuclear coded mitochondrial factor and cell fate determinants such as SOX2 and TP53.

5. Conclusions

The SW1736 ATC cell line was used to investigate functional SOX2 interactors isolated by a novel bioinformatics analysis. Because SOX2 seems to have a central role in the maintenance of stem features in the SW1736 ATC cell line, the interactors are likely to play a role in stemness regulation.

The analysis pointed to DICER1, EIF2C2, RNASEN, TP53, COX8A, and CCND1 genes, suggesting that stemness, microRNA biogenesis and functions, p53 regulatory network, cyclin D1, and cell cycle control, together with mitochondrial activity, might be coregulated as a whole in their functions. Our data and previous data from the literature indicate that those functions are strictly interlinked and that deregulation of them might lead to cancer transformation, especially in cancers such as ATC that possess an undifferentiated nature suggestive of cancer stem cell enrichment.

Conflict of Interests

The authors declare that there is no conflict of interests regarding the publication of this paper.

Acknowledgments

The authors would like to thank Dr. S.I. Genovese and D. Gailor for the help and advice given and Giordano Bruno, Nikola Tesla, and Barbara McClintock for their inspiring vision. This work has been partially funded by PON01_00829.

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