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<front>
<journal-meta>
<journal-id journal-id-type="publisher-id">Front. Oncol.</journal-id>
<journal-title>Frontiers in Oncology</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Oncol.</abbrev-journal-title>
<issn pub-type="epub">2234-943X</issn>
<publisher>
<publisher-name>Frontiers Media S.A.</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.3389/fonc.2021.697409</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Oncology</subject>
<subj-group>
<subject>Original Research</subject>
</subj-group>
</subj-group>
</article-categories>
<title-group>
<article-title>Crosstalk Between DNA Methylation and Gene Mutations in Colorectal Cancer</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname>Dobre</surname>
<given-names>Maria</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<uri xlink:href="https://loop.frontiersin.org/people/1176163"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Salvi</surname>
<given-names>Alessandro</given-names>
</name>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
<uri xlink:href="https://loop.frontiersin.org/people/1385639"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Pelisenco</surname>
<given-names>Iulia Andreea</given-names>
</name>
<xref ref-type="aff" rid="aff3">
<sup>3</sup>
</xref>
<uri xlink:href="https://loop.frontiersin.org/people/1385733"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Vasilescu</surname>
<given-names>Florina</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>De Petro</surname>
<given-names>Giuseppina</given-names>
</name>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
<uri xlink:href="https://loop.frontiersin.org/people/782534"/>
</contrib>
<contrib contrib-type="author" corresp="yes">
<name>
<surname>Herlea</surname>
<given-names>Vlad</given-names>
</name>
<xref ref-type="aff" rid="aff4">
<sup>4</sup>
</xref>
<xref ref-type="author-notes" rid="fn001">
<sup>*</sup>
</xref>
<uri xlink:href="https://loop.frontiersin.org/people/1385727"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Milanesi</surname>
<given-names>Elena</given-names>
</name>
<xref ref-type="aff" rid="aff5">
<sup>5</sup>
</xref>
<uri xlink:href="https://loop.frontiersin.org/people/843470"/>
</contrib>
</contrib-group>
<aff id="aff1">
<sup>1</sup>
<institution>Laboratory of Histopathology and Immunohistochemistry, Victor Babes National Institute of Pathology</institution>, <addr-line>Bucharest</addr-line>, <country>Romania</country>
</aff>
<aff id="aff2">
<sup>2</sup>
<institution>Division of Biology and Genetics, Department of Molecular and Translational Medicine, University of Brescia</institution>, <addr-line>Brescia</addr-line>, <country>Italy</country>
</aff>
<aff id="aff3">
<sup>3</sup>
<institution>Faculty of Biology, University of Bucharest</institution>, <addr-line>Bucharest</addr-line>, <country>Romania</country>
</aff>
<aff id="aff4">
<sup>4</sup>
<institution>Department of Pathology, Fundeni Clinical Institute</institution>, <addr-line>Bucharest</addr-line>, <country>Romania</country>
</aff>
<aff id="aff5">
<sup>5</sup>
<institution>Laboratory of Radiobiology, Victor Babes National Institute of Pathology</institution>, <addr-line>Bucharest</addr-line>, <country>Romania</country>
</aff>
<author-notes>
<fn fn-type="edited-by">
<p>Edited by: Octav Ginghina, Carol Davila University of Medicine and Pharmacy, Romania</p>
</fn>
<fn fn-type="edited-by">
<p>Reviewed by: Paul Willemsen, Hospital Network Antwerp (ZNA), Belgium; James N. Luo, Brigham and Women&#x2019;s Hospital and Harvard Medical School, United States</p>
</fn>
<fn fn-type="corresp" id="fn001">
<p>*Correspondence: Vlad Herlea, <email xlink:href="mailto:herlea2002@yahoo.com">herlea2002@yahoo.com</email>
</p>
</fn>
<fn fn-type="other" id="fn002">
<p>This article was submitted to Surgical Oncology, a section of the journal Frontiers in Oncology</p>
</fn>
</author-notes>
<pub-date pub-type="epub">
<day>01</day>
<month>07</month>
<year>2021</year>
</pub-date>
<pub-date pub-type="collection">
<year>2021</year>
</pub-date>
<volume>11</volume>
<elocation-id>697409</elocation-id>
<history>
<date date-type="received">
<day>19</day>
<month>04</month>
<year>2021</year>
</date>
<date date-type="accepted">
<day>14</day>
<month>06</month>
<year>2021</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#xa9; 2021 Dobre, Salvi, Pelisenco, Vasilescu, De Petro, Herlea and Milanesi</copyright-statement>
<copyright-year>2021</copyright-year>
<copyright-holder>Dobre, Salvi, Pelisenco, Vasilescu, De Petro, Herlea and Milanesi</copyright-holder>
<license xlink:href="http://creativecommons.org/licenses/by/4.0/">
<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) and the copyright owner(s) 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>Colorectal cancer (CRC) is often characterized by mutations and aberrant DNA methylation within the promoters of tumor suppressor genes and proto-oncogenes. The most frequent somatic mutations occur within <italic>KRAS</italic> and <italic>BRAF</italic> genes. Mutations of the <italic>KRAS</italic> gene have been detected in approximately 40% of patients, while mutations in <italic>BRAF</italic> have been detected less frequently at a rate of 10%. In this study, the DNA methylation levels of 22 candidate genes were evaluated in three types of tissue: mucosal tumoral tissue from 18 CRC patients, normal adjacent tissues from 10 CRC patients who underwent surgical resection, and tissue from a control group of six individuals with normal colonoscopies. A differential methylation profile of nine genes (<italic>RUNX3</italic>, <italic>SFRP1</italic>, <italic>WIF1</italic>, <italic>PCDH10</italic>, <italic>DKK2</italic>, <italic>DKK3</italic>, <italic>TMEFF2</italic>, <italic>OPCML</italic>, and <italic>SFRP2</italic>) presenting high methylation levels in tumoral compared to normal tissues was identified. <italic>KRAS</italic> mutations (codons 12 or 13) were detected in eight CRC cases, and <italic>BRAF</italic> mutations (codon 600) in four cases. One of the CRC patients presented concomitant mutations in <italic>KRAS</italic> codon 12 and <italic>BRAF</italic>, whereas seven patients did not present these mutations (WT). When comparing the methylation profile according to mutation status, we found that six genes (<italic>SFRP2</italic>, <italic>DKK2</italic>, <italic>PCDH10</italic>, <italic>TMEFF2</italic>, <italic>SFRP1</italic>, <italic>HS3ST2</italic>) showed a methylation level higher in <italic>BRAF</italic> positive cases than <italic>BRAF</italic> negative cases. The molecular sub-classification of CRC according to mutations and epigenetic modifications may help to identify epigenetic biomarkers useful in designing personalized strategies to improve patient outcomes.</p>
</abstract>
<kwd-group>
<kwd>colorectal cancer</kwd>
<kwd>DNA methylation</kwd>
<kwd>
<italic>KRAS</italic>
</kwd>
<kwd>
<italic>BRAF</italic>
</kwd>
<kwd>mutations</kwd>
</kwd-group>
<contract-sponsor id="cn001">Ministry of Education and Research, Romania<named-content content-type="fundref-id">10.13039/501100006730</named-content>
</contract-sponsor>
<counts>
<fig-count count="4"/>
<table-count count="3"/>
<equation-count count="0"/>
<ref-count count="46"/>
<page-count count="8"/>
<word-count count="3611"/>
</counts>
</article-meta>
</front>
<body>
<sec id="s1" sec-type="intro">
<title>Introduction</title>
<p>Colorectal cancer (CRC) is the third most common type of cancer and, even with advances in CRC screening and therapeutic strategies, it still remains the second deadliest malignancy for both sexes. CRC incidence has continued to increase in countries with medium to high human development indexes and in younger populations (<xref ref-type="bibr" rid="B1">1</xref>). This type of cancer is a highly heterogeneous disease that can be subtyped according to anatomical location or pathological and molecular signatures. The development of CRC is influenced by both environmental risk factors (such as obesity, a sedentary lifestyle, an unhealthy diet, alcohol consumption, and smoking) (<xref ref-type="bibr" rid="B2">2</xref>) and genetic risk factors, with less than 10% of patients presenting inherited mutations that increase the risk of CRC onset (<xref ref-type="bibr" rid="B3">3</xref>) and 25% of patients with &#x201c;familial&#x201d; CRC (<xref ref-type="bibr" rid="B4">4</xref>). About 70% of CRC cases are sporadic; these cases are most common in patients over the age of 50 and seem to depend mainly on dietary and environmental factors.</p>
<p>In general, the development of sporadic CRCs involves the &#x201c;normal-adenoma-dysplasia-carcinoma&#x201d; sequence as described by Vogelstein and Fearon (<xref ref-type="bibr" rid="B5">5</xref>). This sequence includes genetic and epigenetic changes in the colonic epithelium that transform normal glandular epithelium into invasive adenocarcinomas. The progression from adenoma to carcinoma is a multistep process that involves three molecular pathways: the Chromosomal Instability (CIN) pathway, the Microsatellite Instability (MSI) pathway, and the CpG Island Methylator Phenotype (CIMP) pathway. CIN is characterized by loss or gain of chromosomal segments, chromosomal translocations, or gene amplifications, which result in gene copy number variations. In addition, mutations in specific oncogenes, including <italic>KRAS</italic> and <italic>BRAF</italic>, and in tumor suppressor genes, such as <italic>APC</italic> and <italic>TP53</italic>, can be detected (<xref ref-type="bibr" rid="B6">6</xref>). MSI occurs in 15&#x2013;20% of sporadic CRC cases and comprises recurrent alterations in the microsatellite zone without structural and numerical changes in the genome. CIMP, reported in 20&#x2013;30% of sporadic CRCs (<xref ref-type="bibr" rid="B7">7</xref>), is characterized by hyper-methylation of CpG islands located in promoters that regulate the activity of several tumor suppressor genes and other CRC related genes. It is well known that specific mutations can modify DNA methylation (<xref ref-type="bibr" rid="B8">8</xref>) and that DNA methylation changes can cause an increase in mutation rate (<xref ref-type="bibr" rid="B9">9</xref>). In CRC, 10&#x2013;40% lower levels of absolute methylation than normal colon tissue within the whole genome have been determined (<xref ref-type="bibr" rid="B10">10</xref>). However, CpG islands in the promoters of CRC related genes show a hyper-methylated profile, resulting in repression of transcription of tumor suppressor genes (<xref ref-type="bibr" rid="B11">11</xref>). Therefore, the first aim of this study was to identify methylation differences in the promoters of 22 candidate genes in CRC tissues compared with control tissues (from non-CRC individuals) and with paired normal adjacent tissues. The second aim was to examine the relationship between promoter DNA methylation and the presence of mutations in <italic>KRAS</italic> and <italic>BRAF</italic> genes.</p>
</sec>
<sec id="s2" sec-type="materials|methods">
<title>Materials and Methods</title>
<sec id="s2_1">
<title>Sample Collection</title>
<p>The DNA for the study was obtained from 18 CRC sporadic tumor tissues and 10 corresponding paired normal adjacent tissues (NAT) from CRC patients who underwent surgical resection. The NATs were collected approximatively 10&#xa0;cm away from the tumors. For nine patients out of 10, the NAT was collected from the same anatomic segment. For one patient with tumor localization in the recto-sigmoid junction, the NAT was collected from the sigmoid. For five of the 18 CRC patients, blood samples were collected in EDTA tubes. The tissue specimens and the blood samples were collected at Fundeni Clinical Institute in Bucharest (Romania) and were stored at &#x2212;80&#xb0;C until DNA isolation. Six specimens from normal colonic mucosa as well as five blood samples from independent controls, from previous preclinical studies were available from the biobank of the Victor Babes Institute (<xref ref-type="table" rid="T1">
<bold>Table&#xa0;1</bold>
</xref>). The present study was approved by the local ethics committee (registration number 291, 8&#xa0;March 2016), carried out in accordance with the Declaration of Helsinki, and the individuals gave their written informed consent. Genomic DNA was isolated using the QIAmp DNA Mini Kit (Qiagen) according to the manufacturer&#x2019;s instruction and quantified using a NanoDrop 2000 spectrophotometer (Thermo Scientific). All samples were examined by a pathologist, and clinical information of the patient&#x2019;s cohort are reported in <xref ref-type="table" rid="T2">
<bold>Table&#xa0;2</bold>
</xref>.</p>
<table-wrap id="T1" position="float">
<label>Table&#xa0;1</label>
<caption>
<p>Socio-demographic data of the individuals involved in the study.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="top" align="left">Group</th>
<th valign="top" align="center">Age &#xb1; SD</th>
<th valign="top" align="center">Sex (% Female)</th>
<th valign="top" align="center">p-value Age</th>
<th valign="top" align="center">p-value, Sex</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left">
<bold>T (N = 18)</bold>
</td>
<td valign="top" align="center">65.72 &#xb1; 11.31</td>
<td valign="top" align="center">55.55</td>
<td valign="top" align="center">T <italic>vs</italic> C, p = 0.381</td>
<td valign="top" align="center">T <italic>vs</italic> C, p = 0.633, &#x3c7;<sup>2</sup> = 0.229</td>
</tr>
<tr>
<td valign="top" align="left">
<bold>NAT (N = 10)</bold>
</td>
<td valign="top" align="center">62.40 &#xb1; 11.16</td>
<td valign="top" align="center">72.72</td>
<td valign="top" align="center">T <italic>vs</italic> PT, p = 0.461</td>
<td valign="top" align="center">T <italic>vs</italic> PT, p = 0.453, &#x3c7;<sup>2</sup> = 0.562</td>
</tr>
<tr>
<td valign="top" align="left">
<bold>C (N = 6)</bold>
</td>
<td valign="top" align="center">60.66 &#xb1; 14.06</td>
<td valign="top" align="center">66.66</td>
<td valign="top" align="center">PT <italic>vs</italic> C, p = 0.789</td>
<td valign="top" align="center">PT <italic>vs</italic> C, p = 0.793, &#x3c7;<sup>2</sup> = 0.069</td>
</tr>
</tbody>
</table>
</table-wrap>
<table-wrap id="T2" position="float">
<label>Table 2</label>
<caption>
<p>Available clinical data and mutation status of CRC patients involved in the study.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="top" align="left">Mutational status (KRAS/BRAF)</th>
<th valign="top" align="center">Number</th>
<th valign="top" align="center">Mutation type</th>
<th valign="top" align="center">Location</th>
<th valign="top" align="center">Hepatic metastasis</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" rowspan="3" align="left">
<bold>WT</bold>
</td>
<td valign="top" rowspan="3" align="center">7</td>
<td valign="top" align="center">//</td>
<td valign="top" align="left">Colon (n = 3)</td>
<td valign="top" rowspan="3" align="center">1</td>
</tr>
<tr>
<td valign="top" align="center">
</td>
<td valign="top" align="left">Rect/RSJ (n = 3)</td>
</tr>
<tr>
<td valign="top" align="center">
</td>
<td valign="top" align="left">Sigmoid (n = 1)</td>
</tr>
<tr>
<td valign="top" align="left">
<bold>KRAS12+</bold>
</td>
<td valign="top" align="center">4</td>
<td valign="top" align="left">KRAS12_G12D (n = 2)</td>
<td valign="top" align="left">Colon (n = 3)</td>
<td valign="top" rowspan="2" align="center">1<sup>*</sup>
</td>
</tr>
<tr>
<td valign="top" align="left">
</td>
<td valign="top" align="center">
</td>
<td valign="top" align="left">KRAS12_G12V<sup>*</sup> (n = 2)</td>
<td valign="top" align="left">Sigmoid (n = 1)</td>
</tr>
<tr>
<td valign="top" rowspan="3" align="left">
<bold>KRAS13+</bold>
</td>
<td valign="top" rowspan="3" align="center">3</td>
<td valign="top" rowspan="3" align="left">KRAS13_G13D (n = 3)</td>
<td valign="top" align="left">Colon (n = 1)</td>
<td valign="top" rowspan="3" align="center">0</td>
</tr>
<tr>
<td valign="top" align="left">Rect/RSJ (n = 1)</td>
</tr>
<tr>
<td valign="top" align="left">Sigmoid (n = 1)</td>
</tr>
<tr>
<td valign="top" rowspan="2" align="left">
<bold>BRAF+</bold>
</td>
<td valign="top" rowspan="2" align="center">3</td>
<td valign="top" rowspan="2" align="left">BRAF_V600E (n = 3)</td>
<td valign="top" align="left">Colon (n = 2)</td>
<td valign="top" rowspan="2" align="center">1</td>
</tr>
<tr>
<td valign="top" align="left">Rect/RSJ (n = 1)</td>
</tr>
<tr>
<td valign="top" align="left">
<bold>KRAS12+BRAF+</bold>
</td>
<td valign="top" align="center">1</td>
<td valign="top" align="left">KRAS12_G12D+ BRAF_V600E (n = 1)</td>
<td valign="top" align="left">Sigmoid (n = 1)</td>
<td valign="top" align="center">0</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>*The patient with hepatic metastasis presented KRAS12_G12V mutation.</p>
</fn>
</table-wrap-foot>
</table-wrap>
</sec>
<sec id="s2_2">
<title>Detection of <italic>KRAS</italic> and <italic>BRAF</italic> Mutations Detection&#xa0;and DNA Methylation Analysis</title>
<p>
<italic>KRAS</italic> gene mutations (in codons 12, 13, 61) were detected by pyrosequencing as previously described (<xref ref-type="bibr" rid="B12">12</xref>). Mutations in codons 600 and 601 of the <italic>BRAF</italic> gene were assessed using the BRAF 600/601 StripAssay (ViennaLab Diagnostic GmbH, Vienna, Austria) by PCR followed by reverse hybridization according to the manufacturer&#x2019;s protocol. The DNA methylation levels of the promoters of the 22 genes involved in colorectal cancer (<italic>APC</italic>, <italic>CDH1</italic>, <italic>CDKN2A</italic>, <italic>DKK2</italic>, <italic>DKK3</italic>, <italic>HIC1</italic>, <italic>HNF1B</italic>, <italic>HS3ST2</italic>, <italic>MGMT</italic>, <italic>MLH1</italic>, <italic>OPCML</italic>, <italic>PCDH10</italic>, <italic>RASSF1</italic>, <italic>RUNX3</italic>, <italic>SFRP1</italic>, <italic>SFRP2</italic>, <italic>SFRP5</italic>, <italic>SPARC</italic>, <italic>TMEFF2</italic>, <italic>UCHL1</italic>, <italic>WIF1</italic>, and <italic>WT1</italic>) were analyzed using the EpiTect Methyl II PCR Array (Qiagen, Hilden, Germany) according to manufacturer&#x2019;s protocol. This array system is based on the detection of the input DNA that remains after digestion with a methylation-sensitive and/or methylation-dependent restriction enzyme using the EpiTect Methyl II DNA Restriction kit (Qiagen, Hilden, Germany). The relative amount of un-methylated (UM) and methylated (M) DNA was quantified by qPCR using the comparative cycle threshold method.</p>
</sec>
<sec id="s2_3">
<title>Statistical Analysis</title>
<p>The statistical analysis was conducted using the Statistical Package for Social Science (SPSS version 17.0). Categorical variables were tested using the chi-square test and continuous variables were tested using a t-test or Mann-Whitney U test. The Mann-Whitney U test was used to identify differences in promoter methylation levels between CRC patients (T) and controls (C), normal adjacent tissue (NAT) and controls (C), as well as to compare samples from patients with different mutation status for <italic>KRAS</italic> and <italic>BRAF</italic>. Receiver operating characteristic (ROC) curves were created and the area under the curve (AUC) was calculated to determine the role of methylation in differentiating T from C. Logistic regression was used to build a diagnostic model that could explore whether varying combinations of differentially methylated genes could better differentiate T from C. A gene promoter was considered to be methylated if the methylation level was over 20% according to the instructions of the manufacturer. A further analysis of the 10 tumoral samples with their matched NAT samples was performed using the Wilcoxon Signed Rank test. All reported significant p values were two-sided, with <italic>p</italic> &lt; 0.05.</p>
</sec>
</sec>
<sec id="s3" sec-type="results">
<title>Results</title>
<p>In this study, colonic mucosa sample from 18 patients with CRC and six controls were investigated to evaluate the levels of DNA methylation of the promoters of 22 target genes. The tumors were located in the ascending and transverse colon (n = 9), in the sigmoid (n = 4), and in the rectum or the rectosigmoid junction (RSJ) (n = 5). At the moment of sample collection, three cases presented hepatic metastasis. Seven out of the 18 analyzed tumoral tissues presented mutations in the <italic>KRAS</italic> gene: four occurred in codon 12 and three in codon 13. Three patients had mutations in the <italic>BRAF</italic> gene, and one patient showed a double mutation, i.e., mutations in both <italic>BRAF</italic> and <italic>KRAS</italic> genes. <italic>KRAS</italic> and <italic>BRAF</italic> mutations were not found in the 10 normal adjacent tissues or in the seven tumoral tissues (WT) (<xref ref-type="table" rid="T2">
<bold>Table&#xa0;2</bold>
</xref>).</p>
<p>Statistical analysis of the DNA methylation levels of the promoters was performed for 16 genes out of 22, because in six genes, the detected DNA methylation level in the tumoral tissue was below 20% (<xref ref-type="fig" rid="f1">
<bold>Figure&#xa0;1</bold>
</xref> and <xref ref-type="table" rid="T3">
<bold>Table&#xa0;3</bold>
</xref>).</p>
<fig id="f1" position="float">
<label>Figure&#xa0;1</label>
<caption>
<p>DNA methylation levels in 18 Tumoral (T), 10 Normal Adjacent Tissue l (NAT), and 6 Control (C) of each of the 22 evaluated genes. The line represent the threshold of 20% of CpG dinucleotides modified by a methyl group, above which the promoter is defined as methylated.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fonc-11-697409-g001.tif"/>
</fig>
<table-wrap id="T3" position="float">
<label>Table&#xa0;3</label>
<caption>
<p>Average of the methylation percentage (&#xb1; SEM) in tumoral and control tissues along with the statistical results between the two groups and the ROC curve analysis.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="top" rowspan="2" align="left">Gene</th>
<th valign="top" colspan="2" align="center">TUMORAL N = 18</th>
<th valign="top" colspan="2" align="center">CONTROL N = 6</th>
<th valign="top" align="center">
<italic>% Methylation T vs C</italic>
</th>
<th valign="top" colspan="4" align="center">
<italic>ROC</italic>
</th>
</tr>
<tr>
<th valign="top" align="center">Mean</th>
<th valign="top" align="center">SEM</th>
<th valign="top" align="center">Mean</th>
<th valign="top" align="center">SEM</th>
<th valign="top" align="center">
<italic>p-value (Mann-Whitney U test)</italic>
</th>
<th valign="top" align="center">AUC</th>
<th valign="top" align="center">Cut-off%</th>
<th valign="top" align="center">Sensitivity%/ Specificity%</th>
<th valign="top" align="center">
<italic>p-value</italic>
</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left">
<bold>RUNX3</bold>
</td>
<td valign="top" align="center">78.87</td>
<td valign="top" align="center">4.05</td>
<td valign="top" align="center">48.31</td>
<td valign="top" align="center">4.83</td>
<td valign="top" align="center">
<bold>
<italic>&lt;0.001</italic>
</bold>
</td>
<td valign="top" align="center">
<bold>0.917</bold>
</td>
<td valign="top" align="center">
<italic>55.57</italic>
</td>
<td valign="top" align="center">
<italic>94.4&#x2013;83.3</italic>
</td>
<td valign="top" align="center">
<bold>
<italic>0.003</italic>
</bold>
</td>
</tr>
<tr>
<td valign="top" align="left">
<bold>SFRP1</bold>
</td>
<td valign="top" align="center">54.61</td>
<td valign="top" align="center">7.44</td>
<td valign="top" align="center">3.18</td>
<td valign="top" align="center">0.98</td>
<td valign="top" align="center">
<bold>
<italic>&lt;0.001</italic>
</bold>
</td>
<td valign="top" align="center">
<bold>0.944</bold>
</td>
<td valign="top" align="center">
<italic>10.51</italic>
</td>
<td valign="top" align="center">
<italic>94.4&#x2013;100</italic>
</td>
<td valign="top" align="center">
<bold>
<italic>0.001</italic>
</bold>
</td>
</tr>
<tr>
<td valign="top" align="left">
<bold>SPARC</bold>
</td>
<td valign="top" align="center">52.01</td>
<td valign="top" align="center">6.14</td>
<td valign="top" align="center">33.66</td>
<td valign="top" align="center">4.78</td>
<td valign="top" align="center">ns</td>
<td valign="top" align="center">
<italic>&#xa0;</italic>
</td>
<td valign="top" align="center">
<italic>&#xa0;</italic>
</td>
<td valign="top" align="center">
<italic>&#xa0;</italic>
</td>
<td valign="top" align="center">
<bold>
<italic>&#xa0;</italic>
</bold>
</td>
</tr>
<tr>
<td valign="top" align="left">
<bold>WIF1</bold>
</td>
<td valign="top" align="center">51.95</td>
<td valign="top" align="center">7.13</td>
<td valign="top" align="center">6.66</td>
<td valign="top" align="center">1.69</td>
<td valign="top" align="center">
<bold>
<italic>0.003</italic>
</bold>
</td>
<td valign="top" align="center">
<bold>0.889</bold>
</td>
<td valign="top" align="center">
<italic>15.69</italic>
</td>
<td valign="top" align="center">
<italic>88.9&#x2013;100</italic>
</td>
<td valign="top" align="center">
<bold>
<italic>0.005</italic>
</bold>
</td>
</tr>
<tr>
<td valign="top" align="left">
<bold>PCDH10</bold>
</td>
<td valign="top" align="center">50.31</td>
<td valign="top" align="center">7.07</td>
<td valign="top" align="center">4.95</td>
<td valign="top" align="center">1.01</td>
<td valign="top" align="center">
<bold>
<italic>0.001</italic>
</bold>
</td>
<td valign="top" align="center">
<bold>0.935</bold>
</td>
<td valign="top" align="center">
<italic>12.03</italic>
</td>
<td valign="top" align="center">
<italic>88.9&#x2013;100</italic>
</td>
<td valign="top" align="center">
<bold>
<italic>0.002</italic>
</bold>
</td>
</tr>
<tr>
<td valign="top" align="left">
<bold>HS3ST2</bold>
</td>
<td valign="top" align="center">50.03</td>
<td valign="top" align="center">8.39</td>
<td valign="top" align="center">11.04</td>
<td valign="top" align="center">8.7</td>
<td valign="top" align="center">ns</td>
<td valign="top" align="center">
<bold>&#xa0;</bold>
</td>
<td valign="top" align="center">
<italic>&#xa0;</italic>
</td>
<td valign="top" align="center">
<italic>&#xa0;</italic>
</td>
<td valign="top" align="center">
<bold>
<italic>&#xa0;</italic>
</bold>
</td>
</tr>
<tr>
<td valign="top" align="left">
<bold>DKK2</bold>
</td>
<td valign="top" align="center">47.96</td>
<td valign="top" align="center">6.68</td>
<td valign="top" align="center">7.38</td>
<td valign="top" align="center">1.58</td>
<td valign="top" align="center">
<bold>
<italic>&lt;0.001</italic>
</bold>
</td>
<td valign="top" align="center">
<bold>0.981</bold>
</td>
<td valign="top" align="center">
<italic>13.81</italic>
</td>
<td valign="top" align="center">
<italic>94.4&#x2013;100</italic>
</td>
<td valign="top" align="center">
<bold>
<italic>0.001</italic>
</bold>
</td>
</tr>
<tr>
<td valign="top" align="left">
<bold>DKK3</bold>
</td>
<td valign="top" align="center">47.22</td>
<td valign="top" align="center">8.65</td>
<td valign="top" align="center">2.35</td>
<td valign="top" align="center">0.55</td>
<td valign="top" align="center">
<bold>
<italic>0.027</italic>
</bold>
</td>
<td valign="top" align="center">
<bold>0.806</bold>
</td>
<td valign="top" align="center">
<italic>3.92</italic>
</td>
<td valign="top" align="center">
<italic>77.8&#x2013;83.3</italic>
</td>
<td valign="top" align="center">
<bold>
<italic>0.028</italic>
</bold>
</td>
</tr>
<tr>
<td valign="top" align="left">
<bold>TMEFF2</bold>
</td>
<td valign="top" align="center">46.4</td>
<td valign="top" align="center">7.89</td>
<td valign="top" align="center">1.87</td>
<td valign="top" align="center">0.4</td>
<td valign="top" align="center">
<bold>
<italic>0.002</italic>
</bold>
</td>
<td valign="top" align="center">
<bold>0.907</bold>
</td>
<td valign="top" align="center">
<italic>2.99</italic>
</td>
<td valign="top" align="center">
<italic>88.9&#x2013;83.3</italic>
</td>
<td valign="top" align="center">
<bold>
<italic>0.003</italic>
</bold>
</td>
</tr>
<tr>
<td valign="top" align="left">
<bold>OPCML</bold>
</td>
<td valign="top" align="center">43.28</td>
<td valign="top" align="center">5.37</td>
<td valign="top" align="center">9.38</td>
<td valign="top" align="center">2.69</td>
<td valign="top" align="center">
<bold>
<italic>0.003</italic>
</bold>
</td>
<td valign="top" align="center">
<bold>0.889</bold>
</td>
<td valign="top" align="center">
<italic>15.20</italic>
</td>
<td valign="top" align="center">
<italic>83.3&#x2013;83.3</italic>
</td>
<td valign="top" align="center">
<bold>
<italic>0.005</italic>
</bold>
</td>
</tr>
<tr>
<td valign="top" align="left">
<bold>WT1</bold>
</td>
<td valign="top" align="center">42.9</td>
<td valign="top" align="center">9.09</td>
<td valign="top" align="center">0.9</td>
<td valign="top" align="center">0.23</td>
<td valign="top" align="center">
<italic>ns</italic>
</td>
<td valign="top" align="center">&#xa0;</td>
<td valign="top" align="center">
<italic>&#xa0;</italic>
</td>
<td valign="top" align="center">
<italic>&#xa0;</italic>
</td>
<td valign="top" align="center">
<bold>
<italic>&#xa0;</italic>
</bold>
</td>
</tr>
<tr>
<td valign="top" align="left">
<bold>SFRP2</bold>
</td>
<td valign="top" align="center">41.84</td>
<td valign="top" align="center">7.51</td>
<td valign="top" align="center">2.18</td>
<td valign="top" align="center">0.66</td>
<td valign="top" align="center">
<bold>
<italic>0.015</italic>
</bold>
</td>
<td valign="top" align="center">
<bold>0.833</bold>
</td>
<td valign="top" align="center">
<italic>10.84</italic>
</td>
<td valign="top" align="center">
<italic>83.3&#x2013;100</italic>
</td>
<td valign="top" align="center">
<bold>
<italic>0.016</italic>
</bold>
</td>
</tr>
<tr>
<td valign="top" align="left">
<bold>UCHL1</bold>
</td>
<td valign="top" align="center">36.95</td>
<td valign="top" align="center">7.6</td>
<td valign="top" align="center">5.28</td>
<td valign="top" align="center">2.65</td>
<td valign="top" align="center">ns</td>
<td valign="top" align="center">
<italic>&#xa0;</italic>
</td>
<td valign="top" align="center">
<italic>&#xa0;</italic>
</td>
<td valign="top" align="center">
<italic>&#xa0;</italic>
</td>
<td valign="top" align="center">
<bold>
<italic>&#xa0;</italic>
</bold>
</td>
</tr>
<tr>
<td valign="top" align="left">
<bold>HIC1</bold>
</td>
<td valign="top" align="center">29.87</td>
<td valign="top" align="center">8.12</td>
<td valign="top" align="center">4.34</td>
<td valign="top" align="center">0.58</td>
<td valign="top" align="center">ns</td>
<td valign="top" align="center">
<italic>&#xa0;</italic>
</td>
<td valign="top" align="center">
<italic>&#xa0;</italic>
</td>
<td valign="top" align="center">
<italic>&#xa0;</italic>
</td>
<td valign="top" align="center">
<bold>
<italic>&#xa0;</italic>
</bold>
</td>
</tr>
<tr>
<td valign="top" align="left">
<bold>SFRP5</bold>
</td>
<td valign="top" align="center">26.87</td>
<td valign="top" align="center">7.57</td>
<td valign="top" align="center">2.53</td>
<td valign="top" align="center">0.93</td>
<td valign="top" align="center">
<italic>ns</italic>
</td>
<td valign="top" align="center">
<italic>&#xa0;</italic>
</td>
<td valign="top" align="center">
<italic>&#xa0;</italic>
</td>
<td valign="top" align="center">
<italic>&#xa0;</italic>
</td>
<td valign="top" align="center">
<bold>
<italic>&#xa0;</italic>
</bold>
</td>
</tr>
<tr>
<td valign="top" align="left">
<bold>RASSF1</bold>
</td>
<td valign="top" align="center">20.28</td>
<td valign="top" align="center">6.6</td>
<td valign="top" align="center">0.87</td>
<td valign="top" align="center">0.22</td>
<td valign="top" align="center">ns</td>
<td valign="top" align="center">
<italic>&#xa0;</italic>
</td>
<td valign="top" align="center">
<italic>&#xa0;</italic>
</td>
<td valign="top" align="center">
<italic>&#xa0;</italic>
</td>
<td valign="top" align="center">
<bold>
<italic>&#xa0;</italic>
</bold>
</td>
</tr>
<tr>
<td valign="top" align="left">
<bold>CDKN2A</bold>
</td>
<td valign="top" align="center">19.03</td>
<td valign="top" align="center">7.09</td>
<td valign="top" align="center">1.84</td>
<td valign="top" align="center">0.38</td>
<td valign="top" rowspan="6" colspan="5" align="center">Methylation &lt;20%</td>
</tr>
<tr>
<td valign="top" align="left">
<bold>MLH1</bold>
</td>
<td valign="top" align="center">14.48</td>
<td valign="top" align="center">6.88</td>
<td valign="top" align="center">2.05</td>
<td valign="top" align="center">0.95</td>
</tr>
<tr>
<td valign="top" align="left">
<bold>APC</bold>
</td>
<td valign="top" align="center">9.2</td>
<td valign="top" align="center">3.36</td>
<td valign="top" align="center">2.28</td>
<td valign="top" align="center">0.58</td>
</tr>
<tr>
<td valign="top" align="left">
<bold>MGMT</bold>
</td>
<td valign="top" align="center">7.81</td>
<td valign="top" align="center">3.38</td>
<td valign="top" align="center">3.5</td>
<td valign="top" align="center">1.54</td>
</tr>
<tr>
<td valign="top" align="left">
<bold>CDH1</bold>
</td>
<td valign="top" align="center">1.08</td>
<td valign="top" align="center">0.1</td>
<td valign="top" align="center">7.35</td>
<td valign="top" align="center">2.38</td>
</tr>
<tr>
<td valign="top" align="left">
<bold>HNF1B</bold>
</td>
<td valign="top" align="center">0.9</td>
<td valign="top" align="center">0.11</td>
<td valign="top" align="center">1.23</td>
<td valign="top" align="center">0.43</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>The bold values refer to significant results.</p>
<p>NS, not significant.</p>
</fn>
</table-wrap-foot>
</table-wrap>
<p>A significant differential methylation profile of nine genes (<italic>RUNX3</italic>, <italic>SFRP1</italic>, <italic>WIF1</italic>, <italic>PCDH10</italic>, <italic>DKK2</italic>, <italic>DKK3</italic>, <italic>TMEFF2</italic>, <italic>OPCML</italic>, and <italic>SFRP2</italic>) was detected in tumoral tissues when compared to control tissues (<xref ref-type="table" rid="T3">
<bold>Table&#xa0;3</bold>
</xref>). No statistical difference was observed when comparing the methylation levels of NAT to C (<italic>p</italic> &gt; 0.05).To assess the potential diagnostic value in discriminating tumoral from normal tissues from controls, ROC analysis was performed. All the significant genes presented an AUC above 0.80 and a specificity and sensitivity between 77.8 and 100% (<xref ref-type="table" rid="T3">
<bold>Table&#xa0;3</bold>
</xref>). When combining three selected potential biomarkers (<italic>SFRP1</italic>, <italic>PCDH10</italic>, and <italic>DKK2</italic>) the value of the AUC was 0.972 and <italic>p</italic> = 0.001 (<xref ref-type="fig" rid="f2">
<bold>Figure&#xa0;2</bold>
</xref>).</p>
<fig id="f2" position="float">
<label>Figure&#xa0;2</label>
<caption>
<p>ROC curves for three potential biomarkers combined (SFRP1, PCDH10, and DKK2). AUC = 0.972, p = 0.001.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fonc-11-697409-g002.tif"/>
</fig>
<p>We also performed a paired analysis for the 10 tumoral samples with their matched NATs. The results showed that <italic>RUNX3</italic> and <italic>TMEFF2</italic> promoters were significantly hyper-methylated in tumoral tissue (<italic>p</italic> = 0.017 and <italic>p</italic> = 0.037, respectively) (<xref ref-type="fig" rid="f3">
<bold>Figure&#xa0;3</bold>
</xref>). <italic>SFRP1</italic>, <italic>WIF1</italic>, <italic>PCDH10</italic>, <italic>DKK2</italic>, <italic>DKK3</italic>, <italic>OPCML</italic>, and <italic>SFRP2</italic> maintained the same trend of hyper-methylation in T <italic>vs.</italic> C cases, without reaching statistical significance in the comparison with NAT.</p>
<fig id="f3" position="float">
<label>Figure&#xa0;3</label>
<caption>
<p>Significant promoter genes hyper-methylated in the paired analysis (Wilcoxon Signed Rank test): T <italic>vs</italic> NAT.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fonc-11-697409-g003.tif"/>
</fig>
<p>When comparing methylation profiles according to <italic>BRAF</italic> mutation status, we found that <italic>SFRP2</italic>, <italic>DKK2</italic>, <italic>PCDH10</italic>, <italic>TMEF2</italic>, <italic>SFRP1</italic>, and <italic>HS3ST2</italic> showed methylation levels that were significantly higher in the <italic>BRAF</italic> positive cases (n = 4, including the case with <italic>BRAF</italic>+ and <italic>KRAS12</italic>+) than in <italic>BRAF</italic> negative cases (WT and <italic>KRAS+</italic>; n = 14) (<xref ref-type="fig" rid="f4">
<bold>Figure&#xa0;4A</bold>
</xref>). No changes were identified comparing <italic>BRAF</italic>+ <italic>vs</italic> WT (n = 7).</p>
<fig id="f4" position="float">
<label>Figure&#xa0;4</label>
<caption>
<p>
<bold>(A)</bold> The graph shows the genes whose promoters methylation percentage were significantly higher in BRAF+ cases (n = 4) <italic>vs</italic> BRAF&#x2212; cases (n = 14). The bars indicate the mean &#xb1; SEM. <bold>(B)</bold> Heat map of DNA promoter methylation according to mutation status. Methylation levels vary according to the color scale, ranging from light green (low) to red (high).</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fonc-11-697409-g004.tif"/>
</fig>
<p>Regarding <italic>KRAS</italic> mutation status, we found that the methylation level of <italic>SFRP2</italic> was lower in <italic>KRAS</italic>12+/13+ (n = 8) <italic>versus KRAS</italic>12&#x2212;/13&#x2212; (n = 10) (<italic>p</italic> = 0.027). Even when the case with double mutation (<italic>BRAF</italic>+ and <italic>KRAS</italic>12+) was excluded, the comparison remained significant (<italic>p</italic> = 0.043). Moreover, <italic>OPCML</italic> was less methylated in the comparison of <italic>KRAS</italic>12+ (n = 4) to <italic>KRAS</italic>12&#x2212; (n = 14), (<italic>p</italic> = 0.035). No significant difference in the percentage levels of methylation was found in the following comparisons: <italic>KRAS</italic>12+ (n = 4) with WT (n = 7), <italic>KRAS</italic>13+ (n = 3) with WT (n = 7), <italic>KRAS</italic>12+/13+ (n = 8) with WT (n = 7). The methylation results of the tumoral samples grouped by mutational status are presented in the heat map of <xref ref-type="fig" rid="f4">
<bold>Figure&#xa0;4B</bold>
</xref>.</p>
<p>Furthermore, we quantified the methylation levels of the promoters in peripheral blood mononuclear cells (PBMC) from five controls and five CRC patients whose methylation profiles were performed in the tumoral tissue. None of the five patients presented BRAF/KRAS mutations in their blood. Except for <italic>RUNX3</italic>, which was highly methylated both in patients and controls, the other 21 gene promoters did not show a significant general level of methylation (data not shown).</p>
</sec>
<sec id="s4" sec-type="discussion">
<title>Discussion</title>
<p>In this study, we profiled DNA methylation of the promoters of 22 candidate genes in tumoral tissue, normal adjacent tissue from CRC patients, and normal mucosa from controls. In all tumoral samples, we also assessed the mutation status of <italic>KRAS</italic> and <italic>BRAF</italic> to investigate a possible association between hyper-methylation of these candidate genes and <italic>KRAS</italic> and <italic>BRAF</italic> mutations. Our study revealed the hyper-methylation of nine genes out of 22 in tumoral tissue from CRC patients compared to normal mucosa and identified a specific methylation profile for patients with the <italic>BRAF</italic> V600E mutation.</p>
<p>In the case-control comparison, we found hyper-methylation of genes belonging to the Wnt signaling pathway (<italic>SFRP1</italic>, <italic>SFRP2</italic>, <italic>DKK1</italic>, <italic>DKK2</italic>, and <italic>WIF1</italic>), the TGF-Beta;/SMAD pathway (<italic>RUNX3</italic>), two tumor suppressor genes (<italic>PCDH10</italic> and <italic>OPCML</italic>), and a gene that acts both as an oncogene and a tumor suppressor, depending on the cellular context (<italic>TMEFF2</italic>). Many CRCs feature methylation of the extracellular inhibitors of Wnt signaling, such as <italic>SFRP1</italic>, <italic>SFRP2</italic> (<xref ref-type="bibr" rid="B13">13</xref>&#x2013;<xref ref-type="bibr" rid="B15">15</xref>), and genes belonging to the dickkopf (Dkk) family. Although genes from the Dkk family largely have an inhibitory effect on this pathway, there is evidence that <italic>DKK2</italic> can also activate Wnt signaling (<xref ref-type="bibr" rid="B16">16</xref>). In particular, <italic>DKK2</italic> and <italic>DKK3</italic> were hyper-methylated in tumoral tissue from CRC <italic>versus</italic> control tissues or paired adjacent healthy tissues (<xref ref-type="bibr" rid="B13">13</xref>, <xref ref-type="bibr" rid="B17">17</xref>, <xref ref-type="bibr" rid="B18">18</xref>). Furthermore, <italic>DKK3</italic> expression levels were negatively correlated with the rate of promoter methylation (<xref ref-type="bibr" rid="B19">19</xref>). Moreover, it has been suggested that <italic>DKK2</italic> and <italic>DKK3</italic> co hyper-methylation might be considered as an independent prognostic predictor (<xref ref-type="bibr" rid="B20">20</xref>). Aberrant methylation of <italic>WIF1</italic> was found in CRC (<xref ref-type="bibr" rid="B13">13</xref>, <xref ref-type="bibr" rid="B14">14</xref>, <xref ref-type="bibr" rid="B20">20</xref>, <xref ref-type="bibr" rid="B21">21</xref>) with a statistically significant association with increasing tumor stage and tumor differentiation (<xref ref-type="bibr" rid="B22">22</xref>).</p>
<p>A high percentage of colorectal cancers contain mutations that disrupt signaling in the pathways of the TGF-&#x3b2; family that regulate the proliferation, differentiation, adhesion, and migration of cells (<xref ref-type="bibr" rid="B23">23</xref>). One of the transcriptional effectors involved in TGF-&#x3b2;/SMAD signaling is <italic>RUNX3</italic>. Its promoter has been found to be methylated in approximately 30% of CRCs (<xref ref-type="bibr" rid="B15">15</xref>) with high levels both in CRC tissues (<xref ref-type="bibr" rid="B17">17</xref>, <xref ref-type="bibr" rid="B24">24</xref>) and in serum, where methylation levels increase with the advancement of pathological stage (<xref ref-type="bibr" rid="B25">25</xref>). <italic>PCDH10</italic> belongs to the protocadherin gene family and acts as tumor suppressor gene inhibiting cell proliferation and cell invasion in colorectal cancer development. The rate of <italic>PCDH10</italic> methylation in CRC tissue was significantly higher compared with normal mucosa in different studies (<xref ref-type="bibr" rid="B26">26</xref>, <xref ref-type="bibr" rid="B27">27</xref>) as well as in other types of cancer such as lymphomas (<xref ref-type="bibr" rid="B28">28</xref>, <xref ref-type="bibr" rid="B29">29</xref>), breast cancer (<xref ref-type="bibr" rid="B30">30</xref>) and medulloblastoma (<xref ref-type="bibr" rid="B31">31</xref>). Another tumor suppressor gene found hyper-methylated in our study was <italic>OPCML</italic> (Opioid Binding Protein/Cell Adhesion Molecule Like). It is involved in cell growth, invasion, and metastasis. This gene exhibits high promoter methylation and reduced expression levels in different cancers, including ovarian, bladder, nasopharyngeal, and cervical cancers, as well as hepatocellular carcinomas, and colorectal cancer (<xref ref-type="bibr" rid="B32">32</xref>, <xref ref-type="bibr" rid="B33">33</xref>). Finally, our comparison between CRC tissue and controls revealed hyper-methylation of <italic>TMEFF2</italic>. In this case, our results are also in line with the literature as it shows aberrant methylation of this gene in CRC (<xref ref-type="bibr" rid="B34">34</xref>), suggesting its role as prognostic marker (<xref ref-type="bibr" rid="B35">35</xref>).</p>
<p>Mutations in <italic>BRAF</italic> and <italic>KRAS</italic> genes occur in about 10 and 40% of CRC cases respectively (<xref ref-type="bibr" rid="B36">36</xref>, <xref ref-type="bibr" rid="B37">37</xref>) and affect different biological pathways. Functionally, these genes are linked to dysregulated DNA methylation (<xref ref-type="bibr" rid="B38">38</xref>) and miRNA expression (<xref ref-type="bibr" rid="B39">39</xref>). We found a specific methylation profile of tumoral tissues with the <italic>BRAF</italic> V600E mutation that showed increased methylation levels of <italic>SFRP2</italic>, <italic>DKK2</italic>, <italic>PCDH10</italic>, <italic>TMEFF2</italic>, <italic>SFRP1</italic>, and <italic>HS3ST2</italic> compared with tissues without this mutation.</p>
<p>The association of the <italic>BRAF</italic> V600E mutation and <italic>SFRP2</italic> methylation levels has been already shown by Bagci and colleagues (<xref ref-type="bibr" rid="B40">40</xref>). In line with the results of this research group, the association between <italic>KRAS</italic> mutations in codon 12 and codon 13 and hyper-methylation of <italic>SFRP2</italic> did not show significant results. Regarding the other identified genes, a specific association between <italic>BRAF</italic> V600E mutation and methylation levels has not, to our knowledge, been reported in the literature.</p>
<p>Epigenetic aberrations are reversible and therefore represent promising targets for novel approaches for cancer therapies (<xref ref-type="bibr" rid="B41">41</xref>, <xref ref-type="bibr" rid="B42">42</xref>). The observation that <italic>BRAF</italic>-mutant CRCs displayed six hyper-methylated genes with tumor suppressive functions respect to <italic>BRAF</italic>-negative CRCs may shed light on their possible re-expression following treatment with demethylating agents. In CRC, epigenetic alterations may promote resistance to systemic drugs such as 5-fluorouracil (5-FU), oxaliplatin, and irinotecan (<xref ref-type="bibr" rid="B43">43</xref>). For this reason, it has been suggested that combined therapies with epigenetic agents may reverse drug resistance. It has been demonstrated that the use of a demethylating compound, such as 5-azacitidine (5-AC) improves sensitivity and reduces resistance in <italic>BRAF</italic>-mutant CRCs that are usually characterized by very aggressive behavior, poor prognosis, and resistance to therapies with 5-FU or irinotecans (<xref ref-type="bibr" rid="B44">44</xref>). Mao et al. demonstrated that treatment with demethylating agents might prime CRC for <italic>BRAF</italic> inhibitor treatment. In a xenograft model of CRC, these authors showed that pretreatment with 5-AC significantly increased the efficacy of subsequent treatments with a <italic>BRAF</italic> inhibitor (<xref ref-type="bibr" rid="B45">45</xref>).</p>
<p>Our results indicate a panel of genes that could be considered for the identification of an epigenetic molecular signature of BRAF-mutant CRCs and thus potential molecular targets for selective epigenetic treatments. Several epigenetic drugs are currently under study in clinical trials for the treatment of CRC (<xref ref-type="bibr" rid="B46">46</xref>); epigenetic target-based therapy might be a promising approach in the future to improve the curative treatments of CRC.</p>
<p>Clearly, there were some limitations to this study. Firstly, the sample size was relatively small; secondly, mRNA data, which is useful to perform a correlation between methylation and gene expression, was not available.</p>
</sec>
<sec id="s5">
<title>Data Availability Statement</title>
<p>The original contributions presented in the study are included in the article/<xref ref-type="supplementary-material" rid="SM1">
<bold>Supplementary Material</bold>
</xref>. Further inquiries can be directed to the corresponding author.</p>
</sec>
<sec id="s6">
<title>Ethics Statement</title>
<p>The studies involving human participants were reviewed and approved by the Ethics Committee of Victor Babes National Institute of Pathology. The patients/participants provided their written informed consent to participate in this study.</p>
</sec>
<sec id="s7">
<title>Author Contributions</title>
<p>MD designed and coordinated the study, performed the laboratory experiments, contributed to data processing and writing. VH and FV were responsible of the collection and diagnosis of the samples included in the study. EM was responsible of data processing, statistical analysis, and wrote the first draft of the manuscript. AS and GP gave their contribution in critical revision, data interpretation, and writing. IAP was involved in scientific writing. All authors discussed the results and commented on the manuscript. All authors contributed to the article and approved the submitted version.</p>
</sec>
<sec id="s8" sec-type="funding-information">
<title>Funding</title>
<p>This work was funded by the Ministry of Research, Innovation and Digitalization in Romania under grant no. PN 1N/2019_19.29.01.05.</p>
</sec>
<sec id="s9" sec-type="COI-statement">
<title>Conflict of Interest</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>
</body>
<back>
<ack>
<title>Acknowledgments</title>
<p>The authors thank the patients involved in the study for the generous collaboration.</p>
</ack>
<sec id="s10" sec-type="supplementary-material">
<title>Supplementary Material</title>
<p>The Supplementary Material for this article can be found online at: <ext-link ext-link-type="uri" xlink:href="https://www.frontiersin.org/articles/10.3389/fonc.2021.697409/full#supplementary-material">https://www.frontiersin.org/articles/10.3389/fonc.2021.697409/full#supplementary-material</ext-link>
</p>
<supplementary-material xlink:href="Table_1.xls" id="SM1" mimetype="application/vnd.ms-excel"/>
</sec>
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