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<front>
<journal-meta>
<journal-id journal-id-type="publisher-id">Front. Genet.</journal-id>
<journal-title>Frontiers in Genetics</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Genet.</abbrev-journal-title>
<issn pub-type="epub">1664-8021</issn>
<publisher>
<publisher-name>Frontiers Media S.A.</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.3389/fgene.2013.00271</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Genetics</subject>
<subj-group>
<subject>Original Research Article</subject>
</subj-group>
</subj-group>
</article-categories>
<title-group>
<article-title>Aberrantly methylated genes in human papillary thyroid cancer and their association with <italic>BRAF</italic>/<italic>RAS</italic> mutation</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name><surname>Kikuchi</surname> <given-names>Yasuko</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="aff" rid="aff2"><sup>2</sup></xref>
</contrib>
<contrib contrib-type="author">
<name><surname>Tsuji</surname> <given-names>Eiichi</given-names></name>
<xref ref-type="aff" rid="aff2"><sup>2</sup></xref>
</contrib>
<contrib contrib-type="author">
<name><surname>Yagi</surname> <given-names>Koichi</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
</contrib>
<contrib contrib-type="author">
<name><surname>Matsusaka</surname> <given-names>Keisuke</given-names></name>
<xref ref-type="aff" rid="aff3"><sup>3</sup></xref>
</contrib>
<contrib contrib-type="author">
<name><surname>Tsuji</surname> <given-names>Shingo</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
</contrib>
<contrib contrib-type="author">
<name><surname>Kurebayashi</surname> <given-names>Junichi</given-names></name>
<xref ref-type="aff" rid="aff4"><sup>4</sup></xref>
</contrib>
<contrib contrib-type="author">
<name><surname>Ogawa</surname> <given-names>Toshihisa</given-names></name>
<xref ref-type="aff" rid="aff2"><sup>2</sup></xref>
</contrib>
<contrib contrib-type="author">
<name><surname>Aburatani</surname> <given-names>Hiroyuki</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
</contrib>
<contrib contrib-type="author" corresp="yes">
<name><surname>Kaneda</surname> <given-names>Atsushi</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="aff" rid="aff5"><sup>5</sup></xref>
<xref ref-type="aff" rid="aff6"><sup>6</sup></xref>
<xref ref-type="author-notes" rid="fn001"><sup>&#x0002A;</sup></xref>
</contrib>
</contrib-group>
<aff id="aff1"><sup>1</sup><institution>Genome Science Division, Research Center for Advanced Science and Technology, The University of Tokyo</institution> <country>Tokyo, Japan</country></aff>
<aff id="aff2"><sup>2</sup><institution>Department of Metabolic Care and Endocrine Surgery, Graduate School of Medicine, The University of Tokyo</institution> <country>Tokyo, Japan</country></aff>
<aff id="aff3"><sup>3</sup><institution>Department of Pathology, Graduate School of Medicine, The University of Tokyo</institution> <country>Tokyo, Japan</country></aff>
<aff id="aff4"><sup>4</sup><institution>Department of Breast and Endocrine Surgery, Kawasaki Medical University</institution> <country>Okayama, Japan</country></aff>
<aff id="aff5"><sup>5</sup><institution>Department of Molecular Oncology, Graduate School of Medicine, Chiba University</institution> <country>Chiba, Japan</country></aff>
<aff id="aff6"><sup>6</sup><institution>CREST, Japan Science and Technology Agency</institution> <country>Saitama, Japan</country></aff>
<author-notes>
<fn fn-type="edited-by"><p>Edited by: Yoshimasa Saito, Keio University Faculty of Pharmacy, Japan</p></fn>
<fn fn-type="edited-by"><p>Reviewed by: Craig A. Cooney, Central Arkansas Veterans Healthcare System, USA; Mich&#x000E8;le Amouyal, Centre National de la Recherche Scientifique, France</p></fn>
<fn fn-type="corresp" id="fn001"><p>&#x0002A;Correspondence: Atsushi Kaneda, Department of Molecular Oncology, Graduate School of Medicine, Chiba University, Inohana 1-8-1, Chuo-ku, Chiba-City 260-8670, Japan e-mail: <email>kaneda&#x00040;chiba-u.jp</email></p></fn>
<fn fn-type="other" id="fn002"><p>This article was submitted to Epigenomics and Epigenetics, a section of the journal Frontiers in Genetics.</p></fn>
</author-notes>
<pub-date pub-type="epub">
<day>05</day>
<month>12</month>
<year>2013</year>
</pub-date>
<pub-date pub-type="collection">
<year>2013</year>
</pub-date>
<volume>4</volume>
<elocation-id>271</elocation-id>
<history>
<date date-type="received">
<day>21</day>
<month>09</month>
<year>2013</year>
</date>
<date date-type="accepted">
<day>18</day>
<month>11</month>
<year>2013</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#x000A9; 2013 Kikuchi, Tsuji, Yagi, Matsusaka, Tsuji, Kurebayashi, Ogawa, Aburatani and Kaneda.</copyright-statement>
<copyright-year>2013</copyright-year>
<license license-type="open-access" xlink:href="http://creativecommons.org/licenses/by/3.0/"><p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</p>
</license>
</permissions>
<abstract><p>Cancer arises through accumulation of epigenetic and genetic alteration. Aberrant promoter methylation is a common epigenetic mechanism of gene silencing in cancer cells. We here performed genome-wide analysis of DNA methylation of promoter regions by Infinium HumanMethylation27 BeadChip, using 14 clinical papillary thyroid cancer samples and 10 normal thyroid samples. Among the 14 papillary cancer cases, 11 showed frequent aberrant methylation, but the other three cases showed no aberrant methylation at all. Distribution of the hypermethylation among cancer samples was non-random, which implied existence of a subset of preferentially methylated papillary thyroid cancer. Among 25 frequently methylated genes, methylation status of six genes (<italic>HIST1H3J, POU4F2, SHOX2, PHKG2, TLX3, HOXA7</italic>) was validated quantitatively by pyrosequencing. Epigenetic silencing of these genes in methylated papillary thyroid cancer cell lines was confirmed by gene re-expression following treatment with 5-aza-2&#x02032;-deoxycytidine and trichostatin A, and detected by real-time RT-PCR. Methylation of these six genes was validated by analysis of additional 20 papillary thyroid cancer and 10 normal samples. Among the 34 cancer samples in total, 26 cancer samples with preferential methylation were significantly associated with mutation of <italic>BRAF</italic>/<italic>RAS</italic> oncogene (<italic>P</italic> &#x0003D; 0.04, Fisher&#x00027;s exact test). Thus, we identified new genes with frequent epigenetic hypermethylation in papillary thyroid cancer, two subsets of either preferentially methylated or hardly methylated papillary thyroid cancer, with a concomitant occurrence of oncogene mutation and gene methylation. These hypermethylated genes may constitute potential biomarkers for papillary thyroid cancer.</p></abstract>
<kwd-group>
<kwd>DNA methylation</kwd>
<kwd>thyroid cancer</kwd>
<kwd>CIMP (CpG island methylator phenotype)</kwd>
<kwd>BRAF</kwd>
<kwd>RAS</kwd>
<kwd>oncogene mutation</kwd>
</kwd-group>
<counts>
<fig-count count="7"/>
<table-count count="4"/>
<equation-count count="1"/>
<ref-count count="53"/>
<page-count count="11"/>
<word-count count="7790"/>
</counts>
</article-meta>
</front>
<body>
<sec sec-type="introduction" id="s1">
<title>Introduction</title>
<p>Papillary thyroid cancer is the most common cancer derived from follicular cells. It is estimated that approximately 23,500 cases of differentiated thyroid cancer occur per year in the United States (Jemal et al., <xref ref-type="bibr" rid="B16">2005</xref>), and 19,000 papillary thyroid cancer cases per year in the European Union (<ext-link ext-link-type="uri" xlink:href="http://globocan.iarc.fr/">http://globocan.iarc.fr/</ext-link>). In Japan, about 8000 patients suffer from thyroid cancer every year, 80% of which are papillary cancer. While prognosis for papillary thyroid cancer is generally good, with a 10-year survival rate above 90%, some patients die of distant metastases and/or repeated recurrence (Ezaki et al., <xref ref-type="bibr" rid="B8">1992</xref>; Yamashita et al., <xref ref-type="bibr" rid="B53">1998</xref>).</p>
<p>RET/PTC (Rearranged in Transformation/Papillary Thyroid Carcinoma) re-arrangement, <italic>BRAF</italic> (V-Raf murine sarcoma viral oncogene homolog B) and <italic>RAS</italic> (Rat Sarcoma viral oncogene homolog) point mutations are frequently observed in papillary thyroid cancer (Mitsutake et al., <xref ref-type="bibr" rid="B30">2006</xref>; Knauf and Fagin, <xref ref-type="bibr" rid="B21">2009</xref>). Mutation of T to A at 1799 in the exon 15 of the <italic>BRAF</italic> gene has been reported in 28&#x02013;69% of papillary thyroid cancer cases, while point mutations of <italic>RAS</italic> genes are detected in 5&#x02013;20% (Cohen et al., <xref ref-type="bibr" rid="B5">2003</xref>; Kimura et al., <xref ref-type="bibr" rid="B20">2003</xref>; Namba et al., <xref ref-type="bibr" rid="B33">2003</xref>; Kondo et al., <xref ref-type="bibr" rid="B22">2007</xref>). Papillary thyroid cancer with poor prognosis is associated with <italic>BRAF</italic> mutation (Xing et al., <xref ref-type="bibr" rid="B50">2005</xref>; Lee et al., <xref ref-type="bibr" rid="B26">2012</xref>), whereas concomitantly, lengthy disease-free interval is not (Ulisse et al., <xref ref-type="bibr" rid="B43">2012</xref>).</p>
<p>Patients with papillary thyroid cancer are generally treated by surgery. But it is difficult to decide whether total thyroidectomy, hemithyroidectomy, prophylactic central neck dissection or no dissection, should be performed in patients without preoperative or intraoperative evidence of metastatic lymph nodes (Xing et al., <xref ref-type="bibr" rid="B48">2013</xref>). Association of <italic>BRAF</italic> mutation with occult central neck lymph node metastases (Joo et al., <xref ref-type="bibr" rid="B17">2012</xref>) might support use of <italic>BRAF</italic> mutation as an indication for prophylactic central neck dissection for patients with conventionally low- to intermediate-risk papillary thyroid cancer. But precise diagnosis to define prognosis and suitable therapy is currently impossible. Molecular biomarkers would therefore simplify disease management (McLeod et al., <xref ref-type="bibr" rid="B29">2013</xref>).</p>
<p>Along with genetic alterations, accumulation of epigenetic alterations is known to affect cancer development (Baylin and Ohm, <xref ref-type="bibr" rid="B2">2006</xref>; Feinberg et al., <xref ref-type="bibr" rid="B9">2006</xref>; Esteller, <xref ref-type="bibr" rid="B7">2007</xref>). Aberrant DNA methylation at promoter regions is a major epigenetic alteration to silence tumor suppressor genes in many cancer types. <italic>RASSF1A</italic> (Ras association (RalGDS/AF-6) domain family member 1) is methylated in 20% of papillary thyroid cancer, leading to activation of the RAS-MAPK (Mitogen-Activated Protein Kinase) signal (Xing et al., <xref ref-type="bibr" rid="B47">2004</xref>). Papillary thyroid cancer is also reported to involve methylation of other genes, including <italic>RARB</italic> (Retinoic Acid Receptor, Beta), <italic>p16</italic><sup>INK4A</sup> (<italic>CDKN2A</italic>, Cyclin-Dependent Kinase Inhibitor 2A), <italic>TSHR</italic> (Thyroid Stimulating Hormone Receptor), <italic>CDH1</italic> (Cadherin 1, type 1, E-cadherin), <italic>DAPK</italic> (Death-Associated Protein Kinase 1), and <italic>MLH1</italic> (mutL Homolog 1) (Hoque et al., <xref ref-type="bibr" rid="B14">2005</xref>; Guan et al., <xref ref-type="bibr" rid="B10">2008</xref>; Mohammadi-asl et al., <xref ref-type="bibr" rid="B31">2011</xref>). While a few genes known to be aberrantly methylated in other cancers were analyzed in these studies, methylation frequencies ranged from 15 to 33%. Involvement of genes in aberrant DNA methylation, however, has not been well-clarified in papillary thyroid cancer. Whether any subset of papillary thyroid cancer shows preferential aberrant methylation, and whether such methylation and other clinicopathological factors are associated are also unclear.</p>
<p>We here analyzed DNA methylation status of promoter regions on a genome-wide scale, using the Illumina Infinium HumanMethylation27 BeadChip technique on 14 clinical papillary thyroid cancer samples and 10 normal thyroid samples. For genes frequently hypermethylated in cancer, methylation status was validated quantitatively by pyrosequencing, using 20 additional clinical cancer samples and 10 additional normal samples. Methylation-associated gene silencing was confirmed by gene re-expression following 5-aza-2&#x02032;-deoxycytidine and trichostatin A treatment, and by quantitative reverse transcription-polymerase chain reaction (RT-PCR) on thyroid cancer cell lines. We found a number of genes with frequent aberrant methylation and silencing in papillary thyroid cancer, and a subset of cancer with preferential aberrant methylation.</p>
</sec>
<sec sec-type="materials and methods" id="s2">
<title>Materials and methods</title>
<sec>
<title>Clinical samples and cell lines</title>
<p>We obtained 79 primary papillary thyroid cancer samples from patients who underwent thyroidectomy at The University of Tokyo, with written informed consent. These samples were immediately frozen with liquid nitrogen and stored at &#x02212;80&#x000B0;C. The frozen materials were microscopically examined for cancer cell contents by pathologists and were dissected to enrich cancer cells when necessary. Thirty-four cancer samples containing more than 40% of cancer cells were selected and used for further analysis. DNA was extracted by QIAamp DNA Micro Kit (QIAGEN, Valencia, CA). Thyroid cancer cell line BHT-101 was obtained from DSMZ (Leibniz Institute DSMZ-German Collection of Microorganisms and Cell Cultures), TPC-1 was provided from Dr. Mitsutake, University of Nagasaki (Ishizaka et al., <xref ref-type="bibr" rid="B15">1989</xref>) and KTC-1 and KTC-3 cell lines were provided from Dr. Kurebayashi, Kawasaki Medical University (Kurebayashi et al., <xref ref-type="bibr" rid="B24">2000</xref>, <xref ref-type="bibr" rid="B23">2006</xref>). These cell lines were maintained in RPMI 1640 (Gibco, Grand Island, NY) supplemented with 10% fetal bovine serum, streptomycin sulfate (100 mg/L), and penicillin G sodium (100 mg/L). Peripheral blood cell samples were purchased from The Coriell Cell Repositories. This research was certified by the Ethics Committee of The University of Tokyo and Chiba University.</p>
</sec>
<sec>
<title>Infinium assays</title>
<p>High-resolution methylation analysis was conducted on the Illumina Infinium HumanMethylation27 microarray platform. This BeadChip assay measures methylation, given as a &#x003B2;-value, at more than 27,000 CpG loci covering 15,000 genes. For each CpG site, the &#x003B2;-value is the ratio of the fluorescence signal from the methylated probe over the sum of methylated and unmethylated probe signals. The &#x003B2;-value, ranging from 0.00 to 1.00, reflects the methylation level of the individual CpG site. Bisulfite conversion, whole-genome amplification, labeling, hybridization, and scanning were carried out according to the manufacturer&#x00027;s protocols. According to the previously proposed classification (Weber et al., <xref ref-type="bibr" rid="B45">2007</xref>), Infinium probes were classified into three categories: high-CpG, intermediate-CpG, and low-CpG probes, on the basis of CpG ratio (the ratio of observed CpG count over expected CpG count) and GC contents within 500 bp region around the probe site (Matsusaka et al., <xref ref-type="bibr" rid="B28">2011</xref>). Genes in X and Y chromosomes were excluded to avoid gender differences. Infinium enables us to analyze DNA methylation levels systematically for more than 14,000 genes, but methylation level of a single CpG site detected by Infinium may not precisely represent methylation status of promoter CpG island. Some Infinium probes might be less quantitative; in analysis of control samples with known levels of methylation (0, 25, 50, 75, 100%), the observed &#x003B2;-values generally correlated with the expected &#x003B2;-values, while some probes showed lower &#x003B2;-values (0.0&#x02013;0.3) for 75% control or higher &#x003B2;-values (0.7&#x02013;1.0) for 25% control (Nagae et al., <xref ref-type="bibr" rid="B32">2011</xref>).</p>
</sec>
<sec>
<title>Pyrosequencing analysis</title>
<p>Quantitative validation for methylation status was carried out by pyrosequencing as previously reported (Matsusaka et al., <xref ref-type="bibr" rid="B28">2011</xref>). Primers were designed to include no or only one CpG site in a primer sequence using Pyro Q-CpG Software (QIAGEN), to amplify bisulfite-treated DNA regions containing several CpG sites. For C of CpG site within a primer sequence, a nucleotide which does not anneal to C or U was chosen, e.g., adenosine (A). Briefly, the biotinylated PCR product was bound to streptavidin sepharose beads HP (Amersham Biosciences, Sweden), washed, and denatured using a 0.2 mol/L NaOH solution. After addition of 0.3 &#x003BC;mol/L sequencing primer to the purified, single-stranded PCR product, pyrosequencing was carried out using PyroMark Q96 ID System (QIAGEN) according to the manufacturer&#x00027;s instructions. Primer sequences and conditions for <italic>HIST1H3J</italic> (Histone cluster 1, H3j), <italic>POU4F2</italic> (POU class 4 homeobox 2), <italic>SHOX2</italic> (Short stature homeobox 2), <italic>PHKG2</italic> (Phosphorylase Kinase, Gamma 2), <italic>TLX3</italic> (T-cell Leukemia homeobox 3), and <italic>HOXA7</italic> (Homeobox A7), are shown in Table <xref ref-type="table" rid="T1">1</xref>. Control samples with known levels of methylation (0, 25, 50, 75, 100%) were prepared as previously described (Yagi et al., <xref ref-type="bibr" rid="B51">2010</xref>). Pyrosequencing is not systematic, but highly quantitative (Matsusaka et al., <xref ref-type="bibr" rid="B28">2011</xref>), and enables us to precisely validate the methylation level at one CpG site, as determined by the Infinium assay, as well as at surrounding CpG sites. Mean methylation levels of these CpG sites were calculated to represent methylation status of each gene promoter, and were displayed in figures by heatmap or dot chart.</p>
<table-wrap position="float" id="T1">
<label>Table 1</label>
<caption><p><bold>Primer sequences for pyrosequencing of six potential methylation biomarkers</bold>.</p></caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left"><bold>Genes</bold></th>
<th align="left" colspan="2"><bold>Primer sequences for PCR (Forward/Reverse)</bold></th>
<th align="left"><bold>Sequencing primers</bold></th>
</tr>
</thead>
<tbody>
<tr>
<td align="left"><italic>HIST1H3J</italic></td>
<td align="left">F</td>
<td align="left">GTTATAAATTTTGGTAGAAGTTATTGT</td>
<td align="left">ATGGTTAGGAAGAAGTAGATAGT</td>
</tr>
<tr>
<td/>
<td align="left">R<xref ref-type="table-fn" rid="TN1"><sup>&#x0002A;</sup></xref></td>
<td align="left">ACCTTAATAACCAACTACTTCC</td>
<td/>
</tr>
<tr>
<td align="left"><italic>POU4F2</italic></td>
<td align="left">F</td>
<td align="left">GGGGAGAGGGGAGTATAA</td>
<td align="left">ATTAGTTTAGATTGATAGTAGAGG</td>
</tr>
<tr>
<td/>
<td align="left">R<xref ref-type="table-fn" rid="TN1"><sup>&#x0002A;</sup></xref></td>
<td align="left">AAAAAAAACTATACCAAATTAAACTCACCC</td>
<td/>
</tr>
<tr>
<td align="left"><italic>SHOX2</italic></td>
<td align="left">F<xref ref-type="table-fn" rid="TN1"><sup>&#x0002A;</sup></xref></td>
<td align="left">TTGGGGGGGTTGGAGTAGTAAA</td>
<td align="left">AACCCCCTAAATTCTTCCAT</td>
</tr>
<tr>
<td/>
<td align="left">R</td>
<td align="left">CTCCTTCTTCTCCTTCACTTTCTAATC</td>
<td/>
</tr>
<tr>
<td align="left"><italic>PHKG2</italic></td>
<td align="left">F</td>
<td align="left">GTTTGTAATTTTAGTATTTTGGGAGGTTGA</td>
<td align="left">AAGTTTAAGGTTGTAGTGA</td>
</tr>
<tr>
<td/>
<td align="left">R<xref ref-type="table-fn" rid="TN1"><sup>&#x0002A;</sup></xref></td>
<td align="left">TCCCTAACTAAATTCAACATTTTCTCTT</td>
<td/>
</tr>
<tr>
<td align="left"><italic>TLX3</italic></td>
<td align="left">F</td>
<td align="left">TGGTTGAGGTAGGAGAGGAATTAGTA</td>
<td align="left">GGTTTAAGAAAGATGATATAGAG</td>
</tr>
<tr>
<td/>
<td align="left">R<xref ref-type="table-fn" rid="TN1"><sup>&#x0002A;</sup></xref></td>
<td align="left">CACTAAAACTTTACCAAAAATAC</td>
<td/>
</tr>
<tr>
<td align="left"><italic>HOXA7</italic></td>
<td align="left">F<xref ref-type="table-fn" rid="TN1"><sup>&#x0002A;</sup></xref></td>
<td align="left">GGGAGTAAAGGAGTAAGAAGT</td>
<td align="left">CAACAAATCACAAATCAAAATTA</td>
</tr>
<tr>
<td/>
<td align="left">R</td>
<td align="left">ACCCAACAACAAATCACAAATCAAAATT</td>
<td/>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn id="TN1"><label>&#x0002A;</label><p><italic>Primers with 5 &#x02032;-biotin tag.</italic></p></fn>
</table-wrap-foot>
</table-wrap>
<sec>
<title>Mutation analysis</title>
<p>Mutations at <italic>BRAF</italic> 1799, <italic>KRAS</italic> (Kirsten Rat Sarcoma viral oncogene homolog) 34, 35, 38, <italic>NRAS</italic> (Neuroblastoma RAS viral oncogene homolog) 181, 182, 183, <italic>HRAS</italic> (Harvey Rat Sarcoma viral oncogene homolog) 35, 37, 181, 182, 183, were analyzed by genotyping assays on MassARRAY platform, by detecting mass difference of the extended nucleotide using matrix assisted laser desorption ionization-time of flight-mass spectrometry (MALDI-TOF-MS) (Yagi et al., <xref ref-type="bibr" rid="B52">2012</xref>). First, PCR amplification primers and a post-PCR extension primer were designed using MassARRAY Assay Design 3.0 software (Sequenom), and listed in Table <xref ref-type="table" rid="T2">2</xref>. Those mutations were analyzed in a single reaction by multiplex PCR. PCR amplification was performed in 5-&#x003BC;L volume containing 0.5 unit of Taq polymerase, 5 ng of genomic DNA, 0.5 pmol of PCR primer and 2.5 nmol of dNTPs. PCR reactions were cycled at 94&#x000B0;C for 15 min, followed by 45 cycles of 94&#x000B0;C for 20 s, 56&#x000B0;C for 30 s and 72&#x000B0;C for 1 min. Shrimp alkaline phosphatase treatment was performed at 37&#x000B0;C for 20 min and 85&#x000B0;C for 5 min. Post-PCR primer extension was carried out using 5.6 pmol of extension primer. Extension reaction was cycled at 94&#x000B0;C for 30 s, followed by 40 cycles of 94&#x000B0;C for 5 s, 5 cycles of 52&#x000B0;C for 5 s and 80&#x000B0;C for 5 s, and 72&#x000B0;C for 3 min. Reaction products were transferred to a SpectroCHIP (Sequenom) and mass difference was analyzed using MALDI-TOF-MS. Irradiation of the matrix-oligonucleotide-cocrystal with a laser beam ultimately results in desorption and ionization of the oligonucleotides, which then can be accelerated in an electrical field into the TOF device. The TOF device separates the accelerated analyte ions of different mass-to-charge (m/z) ratios by providing a field-free drift tube of defined length. After passing through the tube, ions are detected; every signal is assigned to a specific molecular mass calculated from the TOF. The extended bases at possible mutation sites are determined from the difference of nucleotide molecular masses (Vogel et al., <xref ref-type="bibr" rid="B44">2009</xref>).</p>
<table-wrap position="float" id="T2">
<label>Table 2</label>
<caption><p><bold>Primer sequences used for mutation analysis (MALDI-TOF-MS assays)</bold>.</p></caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left"><bold>Mutation sites</bold></th>
<th align="left" colspan="2"><bold>Primer sequences (Forward/Reverse)</bold></th>
<th align="left"><bold>Extend primers</bold></th>
</tr>
</thead>
<tbody>
<tr>
<td align="left"><italic>BRAF</italic>_1799</td>
<td align="left">F</td>
<td align="left">ACGTTGGATGTTCAAACTGATGGGACCCAC</td>
<td align="left">TGATTTTGGTCTAGCTACAG</td>
</tr>
<tr>
<td/>
<td align="left">R</td>
<td align="left">ACGTTGGATGTCTTCATGAAGACCTCACAG</td>
<td/>
</tr>
<tr>
<td align="left"><italic>HRAS</italic>_34</td>
<td align="left">F</td>
<td align="left">ACGTTGGATGAATGGTTCTGGATCAGCTGG</td>
<td align="left">ACTCTTGCCCACACCGC</td>
</tr>
<tr>
<td/>
<td align="left">R</td>
<td align="left">ACGTTGGATGGACGGAATATAAGCTGGTGG</td>
<td/>
</tr>
<tr>
<td align="left"><italic>HRAS</italic>_35</td>
<td align="left">F</td>
<td align="left">ACGTTGGATGAATGGTTCTGGATCAGCTGG</td>
<td align="left">AGCGGGTGGTGGTGGGCGCCG</td>
</tr>
<tr>
<td/>
<td align="left">R</td>
<td align="left">ACGTTGGATGGACGGAATATAAGCTGGTGG</td>
<td/>
</tr>
<tr>
<td align="left"><italic>HRAS</italic>_37</td>
<td align="left">F</td>
<td align="left">ACGTTGGATGAATGGTTCTGGATCAGCTGG</td>
<td align="left">TCATCGCACTCTTGCCCACAC</td>
</tr>
<tr>
<td/>
<td align="left">R</td>
<td align="left">ACGTTGGATGGACGGAATATAAGCTGGTGG</td>
<td/>
</tr>
<tr>
<td align="left"><italic>HRAS</italic>_38</td>
<td align="left">F</td>
<td align="left">ACGTTGGATGAATGGTTCTGGATCAGCTGG</td>
<td align="left">CAGCGCACTCTTGCCCACA</td>
</tr>
<tr>
<td/>
<td align="left">R</td>
<td align="left">ACGTTGGATGGACGGAATATAAGCTGGTGG</td>
<td/>
</tr>
<tr>
<td align="left"><italic>HRAS</italic>_181</td>
<td align="left">F</td>
<td align="left">ACGTTGGATGTGGCAAACACACACAGGAAG</td>
<td align="left">CATGGCGCTGTACTCCTCCT</td>
</tr>
<tr>
<td/>
<td align="left">R</td>
<td align="left">ACGTTGGATGTGTTGGACATCCTGGATACC</td>
<td/>
</tr>
<tr>
<td align="left"><italic>HRAS</italic>_182</td>
<td align="left">F</td>
<td align="left">ACGTTGGATGTGGCAAACACACACAGGAAG</td>
<td align="left">CATGGCGCTGTACTCCTCC</td>
</tr>
<tr>
<td/>
<td align="left">R</td>
<td align="left">ACGTTGGATGTGTTGGACATCCTGGATACC</td>
<td/>
</tr>
<tr>
<td align="left"><italic>HRAS</italic>_183</td>
<td align="left">F</td>
<td align="left">ACGTTGGATGTGGCAAACACACACAGGAAG</td>
<td align="left">CGCATGGCGCTGTACTCCTC</td>
</tr>
<tr>
<td/>
<td align="left">R</td>
<td align="left">ACGTTGGATGTGTTGGACATCCTGGATACC</td>
<td/>
</tr>
<tr>
<td align="left"><italic>KRAS</italic>_34</td>
<td align="left">F</td>
<td align="left">ACGTTGGATGTAGCTGTATCGTCAAGGCAC</td>
<td align="left">ACTCTTGCCTACGCCAC</td>
</tr>
<tr>
<td/>
<td align="left">R</td>
<td align="left">ACGTTGGATGAGGCCTGCTGAAAATGACTG</td>
<td/>
</tr>
<tr>
<td align="left"><italic>KRAS</italic>_35</td>
<td align="left">F</td>
<td align="left">ACGTTGGATGTAGCTGTATCGTCAAGGCAC</td>
<td align="left">CTGTGGTAGTTGGAGCTG</td>
</tr>
<tr>
<td/>
<td align="left">R</td>
<td align="left">ACGTTGGATGAGGCCTGCTGAAAATGACTG</td>
<td/>
</tr>
<tr>
<td align="left"><italic>KRAS</italic>_37</td>
<td align="left">F</td>
<td align="left">ACGTTGGATGTAGCTGTATCGTCAAGGCAC</td>
<td align="left">GAGGGGCACTCTTGCCTACGC</td>
</tr>
<tr>
<td/>
<td align="left">R</td>
<td align="left">ACGTTGGATGAGGCCTGCTGAAAATGACTG</td>
<td/>
</tr>
<tr>
<td align="left"><italic>KRAS</italic>_38</td>
<td align="left">F</td>
<td align="left">ACGTTGGATGTAGCTGTATCGTCAAGGCAC</td>
<td align="left">AGGCACTCTTGCCTACG</td>
</tr>
<tr>
<td/>
<td align="left">R</td>
<td align="left">ACGTTGGATGAGGCCTGCTGAAAATGACTG</td>
<td/>
</tr>
<tr>
<td align="left"><italic>NRAS</italic>_181</td>
<td align="left">F</td>
<td align="left">ACGTTGGATGTCGCCTGTCCTCATGTATTG</td>
<td align="left">ATACTGGATACAGCTGGA</td>
</tr>
<tr>
<td/>
<td align="left">R</td>
<td align="left">ACGTTGGATGCCTGTTTGTTGGACATACTG</td>
<td/>
</tr>
<tr>
<td align="left"><italic>NRAS</italic>_182</td>
<td align="left">F</td>
<td align="left">ACGTTGGATGTCGCCTGTCCTCATGTATTG</td>
<td align="left">ATGGCACTGTACTCTTCT</td>
</tr>
<tr>
<td/>
<td align="left">R</td>
<td align="left">ACGTTGGATGCCTGTTTGTTGGACATACTG</td>
<td/>
</tr>
<tr>
<td align="left"><italic>NRAS</italic>_183</td>
<td align="left">F</td>
<td align="left">ACGTTGGATGTCGCCTGTCCTCATGTATTG</td>
<td align="left">CTGGATACAGCTGGACA</td>
</tr>
<tr>
<td/>
<td align="left">R</td>
<td align="left">ACGTTGGATGCCTGTTTGTTGGACATACTG</td>
<td/>
</tr>
</tbody>
</table>
</table-wrap>
</sec>
</sec>
<sec>
<title>5-aza-2&#x02032;-deoxycytidine and trichostatin a treatment</title>
<p>Thyroid cancer cell lines were cultured at a density of 3 &#x000D7; 10<sup>5</sup> cells/10-cm dish on Day 0. Cells were exposed to 3 &#x003BC;M 5-aza-2&#x02032;-deoxycytidine (Sigma-Aldrich, St. Louis, MO) on Days 1, 2, and 3, and to 300 nM trichostatin A (Sigma-Aldrich) on the Day 3. 5-Aza-2&#x02032;-deoxycytidine induces hypomethylation of DNA by inhibiting DNA methyltransferase, and re-expression of silenced genes by 5-aza-2&#x02032;-deoxycytidine treatment is synergistically enhanced by trichostatin A, a histone deacetylase inhibitor (Suzuki et al., <xref ref-type="bibr" rid="B40">2002</xref>). 5-Aza-2&#x02032;-deoxycytidine is unstable in aqueous solution, and thus a 20 mM solution in dimethyl sulfoxide (DMSO) was freshly prepared, and diluted in medium to 3 &#x003BC;M every day immediately before medium change. The medium was changed every 24 h, and the cells were harvested on Day 4.</p>
</sec>
<sec>
<title>Quantitative PCR analysis</title>
<p>RT-PCR was performed using CFX96 Touch TM Real-Time PCR Detection System (BIORAD Laboratories). cDNA was synthesized from 1 &#x003BC;g of total RNA treated with DNase I with a Superscript III kit (Invitrogen, Life Technologies). The quantity of cDNA of each gene in a sample was measured by comparing it with standard samples that contained 10<sup>1</sup> to 10<sup>6</sup> copies of the genes, and normalized to that of <italic>PPIA</italic> (Peptidylprolyl Isomerase A). Primer sequences are shown in Table <xref ref-type="table" rid="T3">3</xref>.</p>
<table-wrap position="float" id="T3">
<label>Table 3</label>
<caption><p><bold>Primer sequences for real-time RT-PCR in gene re-expression analysis</bold>.</p></caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left"><bold>Gene</bold></th>
<th align="left" colspan="2"><bold>Primer sequences (Forward/Reverse)</bold></th>
<th align="left"><bold>Anneal (&#x000B0;C)</bold></th>
<th align="left"><bold>Product (bp)</bold></th>
</tr>
</thead>
<tbody>
<tr>
<td align="left"><italic>HIST1H3J</italic></td>
<td align="left">F</td>
<td align="left">AAATCAAGCAGAGGCGAAGTCGGA</td>
<td align="left">58</td>
<td align="left">106</td>
</tr>
<tr>
<td/>
<td align="left">R</td>
<td align="left">GGATAGTGGGTCTCGTCAAAAAGC</td>
<td/>
<td/>
</tr>
<tr>
<td align="left"><italic>POU4F2</italic></td>
<td align="left">F</td>
<td align="left">CACCAAGCCTGAACTCTTCAAT</td>
<td align="left">58</td>
<td align="left">101</td>
</tr>
<tr>
<td/>
<td align="left">R</td>
<td align="left">GCTGAATGGCAAAGTAGGCTTCG</td>
<td/>
<td/>
</tr>
<tr>
<td align="left"><italic>SHOX2</italic></td>
<td align="left">F</td>
<td align="left">AAATCAAGCAGAGGCGAAGTCGGA</td>
<td align="left">58</td>
<td align="left">85</td>
</tr>
<tr>
<td/>
<td align="left">R</td>
<td align="left">GGATAGTGGGTCTCGTCAAAAAGC</td>
<td/>
<td/>
</tr>
<tr>
<td align="left"><italic>PHKG2</italic></td>
<td align="left">F</td>
<td align="left">TGATCTTGTTCACACTCCTGGCT</td>
<td align="left">58</td>
<td align="left">145</td>
</tr>
<tr>
<td/>
<td align="left">R</td>
<td align="left">GAGATCAGGTCTTTGACAGTGCT</td>
<td/>
<td/>
</tr>
<tr>
<td align="left"><italic>TLX3</italic></td>
<td align="left">F</td>
<td align="left">CTGTCTGCACAACTCGTCACTCTT</td>
<td align="left">60</td>
<td align="left">79</td>
</tr>
<tr>
<td/>
<td align="left">R</td>
<td align="left">GACAGCGGGAACCTTGGAACTATC</td>
<td/>
<td/>
</tr>
<tr>
<td align="left"><italic>HOXA7</italic></td>
<td align="left">F</td>
<td align="left">AGTTCCACTTCAACCGCTACCTGAC</td>
<td align="left">58</td>
<td align="left">131</td>
</tr>
<tr>
<td/>
<td align="left">R</td>
<td align="left">GTCCTTATGCTCTTTCTTCCACTTC</td>
<td/>
<td/>
</tr>
</tbody>
</table>
</table-wrap>
</sec>
<sec>
<title>Statistical analysis</title>
<p>P-values were calculated to compare methylation(&#x0002B;) cancer and methylation(&#x02212;) cancer and to analyze the correlation of the methylation status to clinicopathological features. Fisher&#x00027;s exact test was used for analysis of binary features such as sex, distant metastasis, recurrence, and mutation of <italic>BRAF</italic>/<italic>RAS</italic> oncogenes (with simple choice between male and female, occurrence and no occurrence); <italic>t</italic>-test was used for more descriptive features that do not imply a choice, such as age, tumor size, number of lymph nodes with metastasis, and thyroglobulin. When <italic>P</italic> &#x0003C; 0.05, the correlation was considered statistically significant. <italic>P</italic>-values were also calculated by <italic>t</italic>-test to compare distribution of methylation ratios between cancer and normal samples. When <italic>P</italic> &#x0003C; 0.05, the difference of the methylation ratios between cancer and normal samples was considered statistically significant. The dot chart and heatmap were drawn using Excel software and Java TreeView software (<ext-link ext-link-type="uri" xlink:href="http://jtreeview.sourceforge.net/">http://jtreeview.sourceforge.net/</ext-link>).</p>
</sec>
</sec>
<sec sec-type="results" id="s3">
<title>Results</title>
<sec>
<title>Oncogene mutation status in papillary thyroid cancer</title>
<p>We analyzed mutation status of <italic>BRAF</italic> and <italic>RAS</italic> (<italic>HRAS</italic>, <italic>NRAS</italic>, and <italic>KRAS</italic>) oncogenes in 34 papillary thyroid cancer samples using MALDI-TOF-MS (Figure <xref ref-type="fig" rid="F1">1</xref>). <italic>BRAF</italic> mutation was detected in 67% (23/34) of the 34 samples, whereas <italic>HRAS</italic>, <italic>NRAS</italic>, and <italic>KRAS</italic> mutations were detected less frequently, in 3% (1/34), 3% (1/34), and 0% (0/34) sample, respectively. Each oncogene mutation was mutually exclusive; 25 among the 34 samples (75%) were oncogene-mutation(&#x0002B;) cancer.</p>
<fig id="F1" position="float">
<label>Figure 1</label>
<caption><p><bold>Representative data obtained from MALDI-TOF-MS imaging for three cancer samples</bold>. Cancer samples &#x00023;1, &#x00023;4, and &#x00023;5 correspond to papillary cancer samples &#x00023;1, &#x00023;4, and &#x00023;5 in Figure <xref ref-type="fig" rid="F3">3</xref>. First column: photomicrograph of a tumor section with hematoxylin/eosin (H/E) staining. Second, third, and fourth columns: MALDI-TOF-MS profile with detection of <italic>BRAF</italic>/<italic>RAS</italic> mutations. <italic>X</italic>-axis indicates mass-to-charge ratio (m/z) to distinguish wild-type and mutant allele, and Y-axis indicates signal intensity.</p></caption>
<graphic xlink:href="fgene-04-00271-g0001.tif"/>
</fig>
</sec>
<sec>
<title>DNA methylation analysis using illumina infinium beadarray</title>
<p>Among 34 papillary thyroid cancer and 24 normal thyroid samples, 14 and 10 samples, respectively, were analyzed using Infinium 27K BeadArray. Methylation data of other cancer types (80 head and neck squamous cell cancers, 50 gastric cancers, 80 colorectal cancers, 80 prostate cancers, 24 chronic myeloid leukemias, 50 glioblastomas), and normal samples of corresponding tissues were collected from National Center for Biotechnology Information, Gene Expression Omnibus (<ext-link ext-link-type="uri" xlink:href="http://www.ncbi.nlm.nih.gov/gds">http://www.ncbi.nlm.nih.gov/gds</ext-link>: GSE25089 for head and neck squamous cell carcinoma, GSE31789 for gastric cancer, GSE27130 for colorectal cancer, GSE26126 for prostate cancer, GSE31600 for chronic myeloid leukemia, and GSE22867 for glioblastoma). To analyze aberrantly methylated genes in cancer samples, probes with &#x003B2;-value &#x0003C; 0.17 in all the normal samples and with standard deviation of &#x003B2;-values in cancer samples &#x0003E;0.15 were selected, and shown in Figure <xref ref-type="fig" rid="F2">2</xref>. Each cancer type including papillary thyroid cancer showed a unique pattern of aberrant promoter methylation.</p>
<fig id="F2" position="float">
<label>Figure 2</label>
<caption><p><bold>Heatmap for methylation status of several cancer types</bold>. Genes with &#x003B2;-value &#x0003C;0.17 in normal samples and with standard deviation of &#x003B2;-value in cancer samples &#x0003E;0.15, were selected and their &#x003B2;-values were shown by heatmap. Each cancer type showed different methylation profile, and papillary thyroid cancer displayed fewer aberrantly methylated genes than other cancer types. HNSCC, head and neck squamous cell carcinoma; CRC, colorectal cancer; PC, prostate cancer; CML, chronic myeloid leukemia; GC, gastric cancer; GBM, glioblastoma; PTC, papillary thyroid cancer; N, normal samples corresponding to these cancer types.</p></caption>
<graphic xlink:href="fgene-04-00271-g0002.tif"/>
</fig>
</sec>
<sec>
<title>Aberrantly methylated genes in papillary thyroid cancer</title>
<p>While the number of aberrantly methylated genes was relatively small in papillary thyroid cancer (Figure <xref ref-type="fig" rid="F2">2</xref>), 25 genes showed frequent hypermethylation (&#x003B2; &#x0003E; 0.25) in three or more samples among the 14 papillary thyroid cancer samples, and no methylation (&#x003B2; &#x0003C; 0.2) in all the 10 normal samples (Figure <xref ref-type="fig" rid="F3">3</xref>). To check that the hypermethylation status was not due to contaminated blood cells, the methylation status of these genes in peripheral blood cells was also analyzed to see that none of them were methylated in blood (Figure <xref ref-type="fig" rid="F3">3</xref>).</p>
<fig id="F3" position="float">
<label>Figure 3</label>
<caption><p><bold>Aberrantly methylated genes frequently observed in papillary thyroid cancer</bold>. PBC, peripheral blood cells. We detected 25 genes that were hypermethylated (&#x003B2; &#x0003E; 0.25) in at least three of 14 papillary thyroid cancer samples, but not methylated (&#x003B2; &#x0003C; 0.2) in 10 normal samples. Among the 14 papillary cancer samples, 11 showed aberrant methylation in three or more genes, whereas the other three samples had no aberrant methylation. Purple, tumor size; brown, number of metastatic lymph nodes &#x0003E;3; gray, distant metastasis(&#x0002B;); green, age over 45 years old; pink, female; pale blue, stage III or IV; red, recurrence (&#x0002B;); orange, <italic>BRAF</italic> mutation(&#x0002B;); blue, <italic>RAS</italic> mutation (&#x0002B;). The two samples with recurrence (red) had no aberrant methylation (<italic>P</italic> &#x0003D; 0.03, Fisher&#x00027;s exact test). <italic>BRAF</italic>/<italic>RAS</italic> oncogene mutations were all observed in methylation(&#x0002B;) samples (<italic>P</italic> &#x0003D; 0.03, Fisher&#x00027;s exact test).</p></caption>
<graphic xlink:href="fgene-04-00271-g0003.tif"/>
</fig>
<p>Among 14 papillary cancer samples, 11 samples showed aberrant methylation in three or more genes, whereas the other three samples showed no aberrant methylation at all (Figure <xref ref-type="fig" rid="F3">3</xref>). When methylation status was compared with clinicopathological factors, the two cancer cases with recurrence were both methylation-negative (<italic>P</italic> &#x0003D; 0.03, Fisher&#x00027;s exact test) (Figure <xref ref-type="fig" rid="F3">3</xref>). Nine of the 11 frequently methylated samples showed mutation of <italic>BRAF</italic>/<italic>RAS</italic> oncogenes, whereas none of the three methylation-negative samples showed oncogene mutation (<italic>P</italic> &#x0003D; 0.03, Fisher&#x00027;s exact test). Other clinicopathological factors, including tumor size, lymph node metastasis, distant metastasis, tumor stage, age, or sex, did not show significant difference.</p>
<p>To validate the methylation status of these genes, six out of the 25 genes, <italic>HIST1H3J</italic>, <italic>POU4F2</italic>, <italic>SHOX2</italic>, <italic>PHKG2</italic>, <italic>TLX3</italic>, and <italic>HOXA7</italic>, were randomly chosen and analyzed by pyrosequencing, a highly quantitative method (Figure <xref ref-type="fig" rid="F4">4</xref>). Although one normal sample showed high methylation in <italic>POU4F2</italic>, frequent hypermethylation of these genes in papillary cancer samples was confirmed, while normal thyroid samples were generally unmethylated.</p>
<fig id="F4" position="float">
<label>Figure 4</label>
<caption><p><bold>Confirmation of aberrant methylation by pyrosequencing</bold>. Six genes were randomly chosen among the 25 frequently methylated genes, and their methylation status was quantitatively validated by pyrosequencing, using the 14 papillary thyroid cancer samples and 10 normal thyroid samples that were analyzed by Infinium. Although one normal sample showed high methylation in <italic>POU4F2</italic>, aberrant methylation of the six genes in the papillary cancer samples was confirmed. <bold>Bottom</bold>, the corresponding data for aberrant methylation obtained by Infinium analysis (Figure <xref ref-type="fig" rid="F3">3</xref>).</p></caption>
<graphic xlink:href="fgene-04-00271-g0004.tif"/>
</fig>
</sec>
<sec>
<title>Evaluation of gene silencing</title>
<p>The analyzed tissue samples include a part of non-tumor cells (see Materials and Methods). To evaluate whether these aberrantly methylated genes were silenced in cancer cells, we analyzed methylation status of these six genes in papillary thyroid cancer cell lines (TPC1, KTC1, and KTC3) and anaplastic thyroid cancer cell line BHT-101 (Figure <xref ref-type="fig" rid="F5">5A</xref>). All the genes except <italic>SHOX2</italic> showed dense methylation in at least one papillary thyroid cancer cell line, confirming that hypermethylation detected in cancer tissue samples is due to hypermethylation in cancer cells.</p>
<fig id="F5" position="float">
<label>Figure 5</label>
<caption><p><bold>Silencing of aberrantly methylated genes. (A)</bold> Methylation status of the <italic>HOXA7</italic>, <italic>POU4F2</italic>, <italic>HIST1H3J</italic>, <italic>TLX3</italic>, <italic>PHKG2</italic>, <italic>SHOX2</italic> genes was analyzed in papillary thyroid cancer cell lines TPC1, KTC1, and KTC3 and anaplastic thyroid cancer cell line BHT-101 by pyrosequencing. <italic>Open box</italic>, 0&#x02013;30% methylation, presumably no allele methylation. <italic>Hatched box</italic>, 30&#x02013;70% methylation, presumably hemi-allelic or partial methylation. <italic>Closed box</italic>, 70&#x02013;100% methylation, presumably bi-allelic and dense methylation. All the genes except <italic>SHOX2</italic> showed dense methylation in at least one papillary thyroid cancer cell line. <bold>(B)</bold> Real-time RT-PCR analysis of the methylated genes. Cells were treated with 5-aza-2&#x02032;-deoxycytidine (5-AZA) and/or trichostatin A (TSA). Gene expression levels were normalized to that of <italic>PPIA</italic> (<italic>Peptidylprolyl Isomerase A</italic>, or <italic>cyclophilin A</italic>). All the genes except <italic>SHOX2</italic> showed no or very low expression in the analyzed cancer cell line, and showed re-expression in cells treated with 5-AZA/TSA.</p></caption>
<graphic xlink:href="fgene-04-00271-g0005.tif"/>
</fig>
<p>We next performed real-time RT-PCR for the six genes. All the genes except <italic>SHOX2</italic> showed no or very low expression in the analyzed, methylated cancer cell line, and showed re-expression in cells treated with 5-aza-2&#x02032;-deoxycytidine and/or trichostatin A (Figure <xref ref-type="fig" rid="F5">5B</xref>). <italic>SHOX2</italic> was neither expressed, nor methylated in KTC1 (Figure <xref ref-type="fig" rid="F5">5A</xref>). Consequently, its expression was not reversed by the deoxycytidine/trichostatin treatment (Figure <xref ref-type="fig" rid="F5">5B</xref>). This is presumably because <italic>SHOX2</italic> was silenced in KTC1 by mechanisms other than promoter methylation, e.g., by depletion of appropriate transcription factors.</p>
</sec>
<sec>
<title>Methylation analysis of the six genes in additional samples</title>
<p>To statistically extend the validation of aberrant methylation of the six genes, we analyzed the methylation status in 20 additional papillary thyroid cancer samples and 10 additional normal thyroid samples by pyrosequencing (Figure <xref ref-type="fig" rid="F6">6</xref>). A similar fraction of cancer samples showed high methylation in each gene (2/20 for <italic>HIST1H3J</italic>, 8/20 for <italic>POU4F2</italic>, 4/20 for <italic>SHOX2</italic>, 5/20 for <italic>PHKG2</italic>, 6/20 for <italic>TLX3</italic>, and 4/20 for <italic>HOXA7</italic>).</p>
<fig id="F6" position="float">
<label>Figure 6</label>
<caption><p><bold>Aberrant methylation of <italic>HOXA7</italic>, <italic>POU4F2</italic>, <italic>HIST1H3J</italic>, <italic>TLX3</italic>, <italic>PHKG2</italic>, <italic>SHOX2</italic> genes in additional samples: 20 cancer samples and 10 normal ones</bold>. Methylation status of the six genes was confirmed by pyrosequencing. Bottom, color scale with known methylation degrees (0&#x02013;100%).</p></caption>
<graphic xlink:href="fgene-04-00271-g0006.tif"/>
</fig>
<p>When methylation ratios were compared between 34 cancer samples in total and 20 normal samples in total, five genes (<italic>HIST1H3J</italic>, <italic>SHOX2</italic>, <italic>PHKG2</italic>, <italic>TLX3</italic>, and <italic>HOXA7</italic>) showed significantly higher methylation in cancer (<italic>P</italic> &#x0003C; 0.05, ranging from 0.0001 to 0.004, <italic>t</italic>-test), and <italic>POU4F2</italic> tended to show higher methylation in cancer (<italic>P</italic> &#x0003D; 0.07, <italic>t</italic>-test) (Figure <xref ref-type="fig" rid="F7">7A</xref>). Among the 34 cancer samples, 26 showed aberrant methylation in at least one gene, but eight showed no aberrant methylation at all (Figure <xref ref-type="fig" rid="F7">7B</xref>). When clinicopathological features were compared between methylation(&#x0002B;) cancer and methylation(&#x02212;) cancer, mutations of <italic>BRAF</italic>/<italic>RAS</italic> oncogenes significantly correlated to methylation(&#x0002B;) groups (<italic>P</italic> &#x0003D; 0.04, Fisher&#x00027;s exact test) (Table <xref ref-type="table" rid="T4">4</xref>). Although it was not statistically significant, methylation(&#x0002B;) cancer tended to show larger size of tumor (<italic>P</italic> &#x0003D; 0.06, <italic>t</italic>-test) and higher levels of thyroglobulin (<italic>P</italic> &#x0003D; 0.08, <italic>t</italic>-test).</p>
<fig id="F7" position="float">
<label>Figure 7</label>
<caption><p><bold>Methylation status of the six genes in 54 samples analyzed by pyrosequencing. (A)</bold> Each gene was methylated to a different extent in each sample. <italic>C</italic>, 34 cancer samples. <italic>N</italic>, 20 normal samples. <italic>Circle</italic>, methylation ratio of each sample. Filled circles indicate that the gene is methylated with the methylation ratio &#x0003E;25%. <italic>Bars</italic>, the mean and standard error of methylation ratios. <italic>P</italic>-values were calculated by <italic>t</italic>-test to compare distribution of methylation ratios between cancer (<italic>C</italic>) and normal (<italic>N</italic>) samples. Five genes (<italic>HIST1H3J</italic>, <italic>SHOX2</italic>, <italic>PHKG2</italic>, <italic>TLX3</italic>, and <italic>HOXA7</italic>) showed significantly higher methylation in cancer (<italic>P</italic> &#x0003C; 0.05, <italic>t</italic>-test). <italic>POU4F2</italic> tended to show higher methylation in cancer (<italic>P</italic> &#x0003D; 0.07, <italic>t</italic>-test). <bold>(B)</bold> Heatmap for methylation status of the 54 analyzed samples. Among the 34 papillary thyroid cancer samples, 26 were aberrant methylation(&#x0002B;) in at least one of six genes, while eight had no methylation. In Table <xref ref-type="table" rid="T4">4</xref>, the number of cancer samples with aberrant methylation (<italic>n</italic> &#x0003D; 26) and the number of samples without aberrant methylation (<italic>n</italic> &#x0003D; 8) refer to these data.</p></caption>
<graphic xlink:href="fgene-04-00271-g0007.tif"/>
</fig>
<table-wrap position="float" id="T4">
<label>Table 4</label>
<caption><p><bold>Aberrant methylation and clinicopathological features</bold>.</p></caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left"><bold>Clinical features</bold></th>
<th align="left"><bold>All Cases (<italic>n</italic> &#x0003D; 34)</bold></th>
<th align="left"><bold>Aberrant Methylation(&#x0002B;) (<italic>n</italic> &#x0003D; 26, Figure <xref ref-type="fig" rid="F7">7B</xref>)</bold></th>
<th align="left"><bold>Aberrant Methylation(&#x02212;) (<italic>n</italic> &#x0003D; 8, Figure <xref ref-type="fig" rid="F7">7B</xref>)</bold></th>
<th align="left"><bold><italic>P</italic>-values</bold></th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" colspan="5"><bold>SEX</bold></td>
</tr>
<tr>
<td align="left">Male/female</td>
<td align="left">11/23</td>
<td align="left">10/16</td>
<td align="left">1/7</td>
<td align="left">0.17 (Fisher)</td>
</tr>
<tr>
<td align="left" colspan="5"><bold>AGE (YEAR)</bold></td>
</tr>
<tr>
<td align="left">Mean &#x000B1; SE</td>
<td align="left">56.0 &#x000B1; 2.7</td>
<td align="left">57.2 &#x000B1; 3.1</td>
<td align="left">52.4 &#x000B1; 4.9</td>
<td align="left">0.45 (<italic>t</italic>-test)</td>
</tr>
<tr>
<td align="left" colspan="5"><bold>TUMOR SIZE (mm)</bold></td>
</tr>
<tr>
<td align="left">Mean &#x000B1; SE</td>
<td align="left">26.2 &#x000B1; 2.6</td>
<td align="left">28.3 &#x000B1; 3.3</td>
<td align="left">20.1 &#x000B1; 2.0</td>
<td align="left">0.06 (<italic>t</italic>-test)</td>
</tr>
<tr>
<td align="left" colspan="5"><bold>NUMBER OF LYMPH NODES WITH METASTASIS</bold></td>
</tr>
<tr>
<td align="left">Mean &#x000B1; SE</td>
<td align="left">2.6 &#x000B1; 0.7</td>
<td align="left">2.2 &#x000B1; 0.6</td>
<td align="left">3.3 &#x000B1; 1.6</td>
<td align="left">0.53 (<italic>t</italic>-test)</td>
</tr>
<tr>
<td align="left" colspan="5"><bold>DISTANT METASTASIS</bold></td>
</tr>
<tr>
<td align="left">(&#x0002B;)/(&#x02212;)</td>
<td align="left">0/34</td>
<td align="left">0/26</td>
<td align="left">0/8</td>
<td align="left">1 (Fisher)</td>
</tr>
<tr>
<td align="left" colspan="5"><bold>RECURRENCE</bold></td>
</tr>
<tr>
<td align="left">(&#x0002B;)/(&#x02212;)</td>
<td align="left">5/28</td>
<td align="left">3/22</td>
<td align="left">2/6</td>
<td align="left">0.37 (Fisher)</td>
</tr>
<tr>
<td align="left">Unknown</td>
<td align="left">1</td>
<td align="left">1</td>
<td align="left">0</td>
<td/>
</tr>
<tr>
<td align="left" colspan="2"><bold>THYROGLOBULIN (ng/ml)</bold></td>
<td/>
<td/>
<td/>
</tr>
<tr>
<td align="left">Mean &#x000B1; SE</td>
<td align="left">104.6 &#x000B1; 52.1</td>
<td align="left">129.3 &#x000B1; 68.6</td>
<td align="left">30.5 &#x000B1; 9.3</td>
<td align="left">0.08 (<italic>t</italic>-test)</td>
</tr>
<tr>
<td align="left" colspan="5"><bold>MUTATION OF BRAF/RAS ONCOGENES</bold></td>
</tr>
<tr>
<td align="left">(&#x0002B;)/(&#x02212;)</td>
<td align="left">26/7</td>
<td align="left">22/3</td>
<td align="left">4/4</td>
<td align="left">0.04<xref ref-type="table-fn" rid="TN2"><sup>&#x0002A;</sup></xref> (Fisher)</td>
</tr>
<tr>
<td align="left">Unknown</td>
<td align="left">1</td>
<td align="left">1</td>
<td align="left">0</td>
<td/>
</tr>
</tbody>
</table>
<table-wrap-foot>
<p><italic>SE, standard error. P-values were calculated to compare methylation(&#x0002B;) group and methylation(&#x02212;) groups and to analyze the correlation of methylation status to clinicopathological features. Fisher, calculated by Fisher&#x00027;s exact test. t-test, calculated by t-test.</italic></p>
<fn id="TN2"><label>&#x0002A;</label><p><italic>P &#x0003C; 0.05, which is considered as statistically significant. Mutations of BRAF/RAS oncogenes are thus considered to correlate significantly with methylation(&#x0002B;) groups.</italic></p></fn>
</table-wrap-foot>
</table-wrap>
</sec>
</sec>
<sec sec-type="discussion" id="s4">
<title>Discussion</title>
<p>In this study, we performed genome-wide DNA methylation analysis in 14 human papillary thyroid cancer samples and 10 normal samples. Although papillary thyroid cancer apparently involves fewer aberrantly methylated genes than other types of cancers, we detected 25 genes frequently hypermethylated in papillary thyroid cancer. Methylation status was quantitatively validated in six out of the 25 genes by pyrosequencing, using the genome-widely analyzed samples and additional samples. Gene silencing in papillary thyroid cancer cell lines was confirmed by real-time RT-PCR. While a subset of cancer cases had no aberrant methylation at all, cancer with preferential methylation tended to have oncogene mutation and to be larger tumor.</p>
<p>Papillary thyroid cancer displayed fewer aberrantly methylated genes, compared with other cancer types (Figure <xref ref-type="fig" rid="F2">2</xref>). For genes previously reported to be methylated in thyroid cancer, such as <italic>TSHR</italic> (Xing et al., <xref ref-type="bibr" rid="B49">2003</xref>), or in other cancer types, such as <italic>RASSF1A</italic>, <italic>RAR</italic>-&#x003B2;<italic>2</italic>, <italic>p16</italic>, <italic>CDH1</italic>, <italic>DAPK</italic>, and <italic>MLH1</italic>, the methylation frequency in papillary thyroid cancer ranges from 15 to 33% (Hoque et al., <xref ref-type="bibr" rid="B14">2005</xref>; Guan et al., <xref ref-type="bibr" rid="B10">2008</xref>; Mohammadi-asl et al., <xref ref-type="bibr" rid="B31">2011</xref>). In these reports, no or few normal samples were analyzed (Guan et al., <xref ref-type="bibr" rid="B10">2008</xref>; Mohammadi-asl et al., <xref ref-type="bibr" rid="B31">2011</xref>), methylation was also detected in normal samples (Hoque et al., <xref ref-type="bibr" rid="B14">2005</xref>), or a non-quantitative method, i.e., standard methylation-specific PCR, was used (Guan et al., <xref ref-type="bibr" rid="B10">2008</xref>). Standard methylation-specific PCR (Herman et al., <xref ref-type="bibr" rid="B12">1996</xref>) can amplify and detect minor fraction of methylated alleles, but its high sensitivity can lead to overestimation of methylation frequency. Our analysis did not select these genes as frequently methylated ones in the first 14 cancer samples, because normal thyroid tissues also showed high methylation levels or because methylation frequencies in papillary thyroid cancer samples were low (&#x02264;2 of 14 cancer samples). Instead, we detected 25 novel genes that were frequently aberrantly methylated (&#x003B2; &#x0003E; 0.25) in at least three of the 14 cancer samples, and not methylated in any of the 10 normal thyroid samples (&#x003B2; &#x0003C; 0.2).</p>
<p>Interestingly, three of the 14 papillary thyroid cancer samples showed no aberrant methylation in the 25 genes, but the other 11 cancer samples showed hypermethylation in at least three of the 25 genes. No cancer sample showed aberrant methylation in just one or two genes. This unusual distribution of aberrant methylation is similar to the CpG island methylator phenotype (CIMP), which was first proposed in colorectal cancer (Toyota et al., <xref ref-type="bibr" rid="B42">1999</xref>). As Toyota et al. demonstrated in colorectal cancer (Toyota et al., <xref ref-type="bibr" rid="B42">1999</xref>), we calculated probability of methylation distribution in papillary thyroid cancer using these 25 genes. The fraction of methylated tumors in each gene was 3/14 for <italic>ATP8A2</italic>, 3/14 for <italic>C19orf4</italic>, &#x02026;, 5/14 for <italic>ANKRD45</italic>, &#x02026;, 6/14 for <italic>MYLK</italic>, &#x02026;, 4/14 for <italic>NRN1</italic>, &#x02026;, 5/14 for <italic>SHOX2</italic>, and 3/14 for <italic>SST</italic> (Figure <xref ref-type="fig" rid="F3">3</xref>). Assuming that methylation of these genes is random, the probability that none of the 25 genes would be methylated in three cancer samples is <italic>P</italic> &#x0003D; 1.2 &#x000D7; 10<sup>&#x02212;8</sup>. This was calculated using the following formula:
<disp-formula id="E1"><mml:math id="M1"><mml:mrow><mml:mi>p</mml:mi><mml:mo stretchy='false'>(</mml:mo><mml:mi>x</mml:mi><mml:mo stretchy='false'>)</mml:mo><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mtable columnalign='left'><mml:mtr columnalign='left'><mml:mtd columnalign='left'><mml:mi>N</mml:mi></mml:mtd></mml:mtr><mml:mtr columnalign='left'><mml:mtd columnalign='left'><mml:mi>x</mml:mi></mml:mtd></mml:mtr></mml:mtable></mml:mrow><mml:mo>)</mml:mo></mml:mrow><mml:mstyle displaystyle='true'><mml:msub><mml:mo>&#x0220F;</mml:mo><mml:mi>g</mml:mi></mml:msub><mml:mrow><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mtable columnalign='left'><mml:mtr columnalign='left'><mml:mtd columnalign='left'><mml:mrow><mml:mi>N</mml:mi><mml:mo>&#x02212;</mml:mo><mml:mi>x</mml:mi></mml:mrow></mml:mtd></mml:mtr><mml:mtr columnalign='left'><mml:mtd columnalign='left'><mml:mrow><mml:mi>f</mml:mi><mml:mo stretchy='false'>(</mml:mo><mml:mi>g</mml:mi><mml:mo stretchy='false'>)</mml:mo></mml:mrow></mml:mtd></mml:mtr></mml:mtable></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:mrow></mml:mstyle></mml:mrow><mml:mrow><mml:mstyle displaystyle='true'><mml:msub><mml:mo>&#x0220F;</mml:mo><mml:mi>g</mml:mi></mml:msub><mml:mrow><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mtable columnalign='left'><mml:mtr columnalign='left'><mml:mtd columnalign='left'><mml:mrow><mml:mi>N</mml:mi></mml:mrow></mml:mtd></mml:mtr><mml:mtr columnalign='left'><mml:mtd columnalign='left'><mml:mrow><mml:mi>f</mml:mi><mml:mo stretchy='false'>(</mml:mo><mml:mi>g</mml:mi><mml:mo stretchy='false'>)</mml:mo></mml:mrow></mml:mtd></mml:mtr></mml:mtable></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:mrow></mml:mstyle></mml:mrow></mml:mfrac></mml:mrow></mml:math></disp-formula>
where <italic>x</italic> indicates number of samples which do not have methylated genes (<italic>x</italic> &#x0003D; 3 in the present case), <italic>N</italic> indicates number of cancer samples (<italic>N</italic> &#x0003D; 14 here), <italic>g</italic> indicates one of the 25 genes, and <italic>f(g)</italic> indicates the number of samples in which <italic>g</italic> is methylated. Similarly, the probability that at least three of the 25 genes are methylated in 11 cancer samples would be <italic>P</italic> &#x0003D; 0.0028. In each case, a random event is highly unlikely. We rather observe that the associated methylation or un-methylation events both occur at relatively high frequencies. Our data thus suggest that there are two distinct classes of papillary thyroid cancer. One is a subset of hardly methylated cancer. The second one is a subset of preferentially methylated cancer, prone to transcriptional silencing and with the potential to inactivate several genes simultaneously, as CIMP has been proposed in colorectal cancer and other cancers (Toyota et al., <xref ref-type="bibr" rid="B42">1999</xref>; Kaneda et al., <xref ref-type="bibr" rid="B18">2002</xref>; Noushmehr et al., <xref ref-type="bibr" rid="B35">2010</xref>).</p>
<p>Although the number of analyzed samples was not large, preferentially methylated papillary thyroid cancer showed mutation of <italic>BRAF</italic>/<italic>RAS</italic> oncogenes more frequently than methylation(&#x02212;) cancer (<italic>P</italic> &#x0003D; 0.04, Fisher&#x00027;s exact test). In previous studies of papillary thyroid cancer, although methylation of <italic>RASSF1A</italic> and <italic>BRAF</italic> mutation were detected in a mutually exclusive manner (Xing et al., <xref ref-type="bibr" rid="B47">2004</xref>; Hoque et al., <xref ref-type="bibr" rid="B14">2005</xref>), methylation of <italic>RAR</italic>-&#x003B2;<italic>2</italic> or <italic>MLH1</italic> significantly correlated to <italic>BRAF</italic> mutation (Hoque et al., <xref ref-type="bibr" rid="B14">2005</xref>; Guan et al., <xref ref-type="bibr" rid="B10">2008</xref>). Correlation of aberrant methylation and oncogene mutation are also reported in colorectal cancer; high-methylation and intermediate-methylation epigenotypes strongly correlated to <italic>BRAF</italic> mutation and <italic>KRAS</italic> mutation, respectively, and low-methylation epigenotype strongly correlated to lack of oncogene mutation (Shen et al., <xref ref-type="bibr" rid="B39">2007</xref>; Yagi et al., <xref ref-type="bibr" rid="B51">2010</xref>; Hinoue et al., <xref ref-type="bibr" rid="B13">2012</xref>). The mechanism of these correlations is still unknown, but oncogene mutation may somehow induce aberrant methylation, or aberrant methylation may help escape from senescence by disrupting factors critical in <italic>RAF</italic>/<italic>RAS</italic>-induced senescence (Kaneda and Yagi, <xref ref-type="bibr" rid="B19">2011</xref>).</p>
<p>Preferentially methylated cancer also tended to have larger tumors and higher thyroglobulin levels, which might relate to cancer progression (Piccardo et al., <xref ref-type="bibr" rid="B36">2013</xref>). Although 90% of papillary thyroid cancers are considered to be at low risk with a mortality rate of 1&#x02013;2%, the mortality rate of the high risk group is 50&#x02013;75% (Hay et al., <xref ref-type="bibr" rid="B11">1993</xref>; Noguchi et al., <xref ref-type="bibr" rid="B34">1994</xref>; Shaha et al., <xref ref-type="bibr" rid="B38">1996</xref>; Dean and Hay, <xref ref-type="bibr" rid="B6">2000</xref>). The tumor-node-metastasis (TNM) classification is a tool for cancer prognosis; each variable used in TNM staging (age, tumor size, extent of primary tumor, and presence of nodal or distant metastases) shows significant association with observed end points of cancer recurrence or death. Cancer recurrence and mortality ratios are significantly lower in stage I (15.4% and 1.7%, respectively) compared with more advanced tumors (22% and 15.8% in stage II, 46.4% and 30% in stage III, and 66.7% and 60.9% in stage IV, respectively) (Loh et al., <xref ref-type="bibr" rid="B27">1997</xref>). Molecular diagnostic markers are still not used, although their development is anticipated (McLeod et al., <xref ref-type="bibr" rid="B29">2013</xref>). Although aberrant methylation was not significantly associated with lymph node metastasis, distant metastasis, or recurrence in analysis of the 34 cancer samples in this study, further study should be performed using larger set of clinical samples for comparison of aberrant methylation, gene mutation status, and prognosis.</p>
<p>As for detected genes, <italic>TLX3</italic> (<italic>HOX11L2</italic>) is a transcription factor highly expressed in T-cell leukemia (Baak et al., <xref ref-type="bibr" rid="B1">2008</xref>), and its aberrant methylation was observed in cisplatin-resistant bladder cancer (Tada et al., <xref ref-type="bibr" rid="B41">2011</xref>). <italic>SHOX2</italic> is a member of the homeobox gene family, and is reported to relate to a short-stature phenotype of Turner syndrome (Clement-Jones et al., <xref ref-type="bibr" rid="B4">2000</xref>). DNA methylation of <italic>SHOX2</italic> was suggested to be a biomarker for diagnosis of lung cancer based on bronchial aspirates (Schmidt et al., <xref ref-type="bibr" rid="B37">2010</xref>). <italic>HOXA7</italic> is also a transcription factor belonging to the homeobox gene family that regulates gene expression, morphogenesis, and differentiation (La Celle and Polakowska, <xref ref-type="bibr" rid="B25">2001</xref>). <italic>POU4F2</italic> is one of POU family genes with Pit-Oct-Unc domain, and is a transcription factor with a role in cell identity and regulation of nerve cell or retinal development (Weishaupt et al., <xref ref-type="bibr" rid="B46">2005</xref>). PHKG2 is the gamma subunit of phosphorylase kinase, containing the active site of the enzyme. Phosphorylase kinase-deficient liver glycogenosis can be caused by mutations of phosphorylase kinase subunits, <italic>PHKA2</italic>, <italic>PHKB</italic>, or <italic>PHKG2</italic>, but <italic>PHKG2</italic> mutation was reported to cause a severe phenotype of this disease (Burwinkel et al., <xref ref-type="bibr" rid="B3">2003</xref>). <italic>HIST1H3J</italic> encodes a member of histone H3 family, and is found in the small histone gene cluster on chromosome 6p22-p21.3 (NCBI gene data bank). If the role of histone modifications is known to affect the regulation of gene expression, less is known about the possible direct involvement of histones, an H3 variant in the present case, in thyroid tumorigenesis. Further investigation is necessary to clarify tumorigenic roles of these genes and their methylation, in papillary thyroid cancer and other types of cancer (Schmidt et al., <xref ref-type="bibr" rid="B37">2010</xref>; Tada et al., <xref ref-type="bibr" rid="B41">2011</xref>).</p>
<p>In summary, 25 new genes were found to be frequently methylated in papillary thyroid cancer. There might be subsets of papillary thyroid cancer hardly methylated and preferentially methylated, and aberrant methylation of these genes correlates <italic>a priori</italic> to <italic>BRAF</italic>/<italic>RAS</italic> oncogene mutation in papillary thyroid cancer.</p>
</sec>
<sec>
<title>Author contributions</title>
<p>Yasuko Kikuchi, Koichi Yagi, and Keisuke Matsusaka performed the experiments. Eiichi Tsuji and Toshihisa Ogawa prepared clinical samples and information. Junichi Kurebayashi established and supplied cell lines. Yasuko Kikuchi, Shingo Tsuji and Atsushi Kaneda analyzed and interpreted the data. Yasuko Kikuchi and Atsushi Kaneda wrote the manuscript. Toshihisa Ogawa, Hiroyuki Aburatani, and Atsushi Kaneda supervised the study.</p>
<sec>
<title>Conflict of interest statement</title>
<p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p></sec>
</sec>
</body>
<back>
<ack>
<p>We thank Dr. Mitsutake, University of Nagasaki, for kindly providing cancer cell line TPC-1, and Hiroko Meguro, Kyoko Fujinaka and Kaori Nakano for their technical help. This work was supported by Grants-in-Aid for Scientific Research from the Ministry of Education, Culture, Sports, Science and Technology of Japan, and by JST CREST program.</p>
</ack>
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