Name: genome-wide analysis of dna methylation in gastrointestinal cancer
Uploaded: 2020-09-18T13:00:00.000+00:00
Duration: 7 min 50 s
Description: Watch this Scientific Journal Video about Genome-Wide Analysis of DNA Methylation in Gastrointestinal Cancer at JoVE.com

We are especially grateful to all members of the Department of Surgery, The Sidney Kimmel Comprehensive Cancer Center at the Johns Hopkins University School of Medicine for useful discussions and technical support. We also thank Kristen Rodgers for generous technical guidance on the procedures for bisulfite treatment and qMSP.

All procedures followed were in accordance with the ethical standards of the institutions&#8217; human research ethics committee. The study was approved by the Institutional Review Board at the Juntendo University Shizuoka Hospital, and written informed consent was waived because of the retrospective design.
1. Washing the slides
<ol>
	<li>Prepare 10 &#181;m of unstained formalin-fixed paraffin-embedded (FFPE) sections.</li>
	<li>Place slides in a glass slide holder: use about 3&#8211;5 largest cross-sectional slides unless the tissue is minimal, and more slides are needed.</li>
	<li>Fill the slide holder with xylene, and make sure that all tissue on the slide is submerged. Allow to sit for 15 min.</li>
	<li>After 15 min, pour out the xylene with the slides held with a pipette tip so that the slides do not fall out.</li>
	<li>Pour in more xylene to the same level as before. Allow to sit for another 15 min.</li>
	<li>After 15 min, pour out the xylene again.</li>
	<li>Fill the slide holder with 100% ethanol (EtOH), and make sure that all tissue on the slide is fully submerged. Allow to sit for 3 min.</li>
	<li>Pour off the EtOH while carefully holding the slides. Refill to the same level with more EtOH. Allow to sit for 2 min.</li>
	<li>Pour off the EtOH again and remove the slide. Carefully place them face up on a clean paper towel to dry. Allow to sit for 10 min.</li>
</ol>
2. Scraping the slides
<ol>
	<li>Prepare digestion/lysis buffer with 650 mL of diethylpyrocarbonate (DEPC)-treated water, 100 mL of Ethylenediaminetetraacetic acid (EDTA), 50 ml of Tris hydrochloride (Tris-HCL) 2 M pH 8.8, and 200 mL of 10% sodium dodecyl sulfate (SDS) (see Table of Materials).</li>
	<li>Fill a 1.5 mL single-use polypropylene tube with 80 &#181;L of digestion/lysis buffer (see Table of Materials).</li>
	<li>Put a clean pipette tip into the buffer.</li>
	<li>Identify cancer tissues of the tumor area most suitable for macrodissection according to the appropriate H&#38;E stained section. 
	NOTE: The tumor area for macrodissection should be identified by preferably two qualified pathologists.</li>
	<li>Macrodissect cancer tissue based on the marked H&#38;E stained section.
	<ol>
		<li>Take a clean razor blade and gently scrape the cancer tissue off the slide, trying to keep it in one piece so that it is easier to work with.</li>
		<li>Take the pipette tip out of single-use polypropylene tube containing buffer and use it to transfer the scrapped tissue into the buffer vial (see Table of Materials). 
		NOTE: The wet tip should attract the tissue, which makes the transfer easier.</li>
	</ol>
	</li>
	<li>Repeat step 2.5 with the rest of the slides.</li>
	<li>After all the tissue is in single-use polypropylene tube, use the tip to make sure the tissue is fully submerged and is not stuck to the wall of the tube.</li>
	<li>Add 20 &#181;L of a subtilisin-related serine protease to the vial and gently flick to mix (see Table of Materials).</li>
	<li>Place the vial in a 55 &#176;C heat block for at least 4 h or overnight. Make sure to slightly vortex after 2 h.</li>
</ol>
3. Bisulfite treatment
<ol>
	<li>Use 45 &#181;L of the digested tissue solution as the sample.</li>
	<li>Perform the bisulfite treatment using reagents in a bisulfite conversion kit according to the manufacturer&#8217;s instructions (see Table of Materials).
	<ol>
		<li>Add 5 &#181;L of dilution buffer to the DNA sample and incubate at 37 &#176;C for 15 min (see Table of Materials).</li>
		<li>While the samples are incubating, prepare the bisulfite conversion reagent by adding 750 &#181;L of dH2O and 210 &#181;L of dilution buffer to one tube of CT conversion reagent (see Table of Materials). Mix the tubes by vortexing for 10 min.</li>
		<li>After the 15 min incubation, add 100 &#181;L of the prepared CT conversion reagent to each sample and mix by inversion.</li>
		<li>Incubate the samples in the dark at 50 &#176;C for 12 to 16 h.</li>
		<li>After the incubation, remove the samples and place on ice for 10 min.</li>
		<li>Add 400 &#181;L of binding buffer and mix each sample by pipetting up and down (see Table of Materials). Load each sample into a spin column and place the column into a 2 mL collection tube (see Table of Materials).</li>
		<li>Centrifuge each sample at full speed (10,000 x g) for 1 min and discard the flow-through.</li>
		<li>Add 200 &#181;L of wash buffer to each column and spin at full speed for 1 min, discarding the flow-through (see Table of Materials).</li>
		<li>Add 200 &#181;L of desulfonation buffer to each column and allow the column to stand at room temperature for 15 min (see Table of Materials). After the incubation, spin the columns at full speed for 1 min and discard the flow-through.</li>
		<li>Add 200 &#181;L of wash buffer to each column and spin at full speed for 1 min (see Table of Materials).</li>
		<li>Repeat this step one more time.</li>
		<li>Add 46 &#181;L of dH2O to each column and place each column in a new sterile 1.5 mL single-use polypropylene tube (see Table of Materials). Spin each tube for 2 min to elute the DNA.</li>
	</ol>
	</li>
	<li>Remove each spin column from the single-use polypropylene tube and discard (see Table of Materials). The DNA is now ready for the analysis.</li>
</ol>
4. Array platform for evaluation of DNA methylation at a CpG locus in the human genome
<ol>
	<li>Assess the quality of DNA (quality check: QC) using FFPE QC assay on a real-time PCR amplification and detection instrument, with subsequent data analysis performed according to the manufacturers&#8217; instructions (see Table of Materials). 
	NOTE: Samples with &#8710;Cq values less than the recommended 5.0 are further processed14.</li>
	<li>Analyze samples with an array platform for complex evaluation of DNA methylation to assess the methylation status of &#62;450,000 CpG sites in the genome (see Table of Materials). Product information sheet, data sheet, and product files for the array platform are available15. 
	NOTE: The assay is performed according to the manufacturer&#8217;s instructions for FFPE material14.</li>
	<li>Calculate high and low locus methylation in a data analysis tool for an array platform as a &#946;-value, with a range from 0 to 1, respectively (see Table of Materials). Use commercially available software (see Table of Materials) to calculate a &#946;-value.</li>
	<li>Import data generated on the array platform for complex evaluation of DNA methylation platform into the R software environment (R v.2.15.1) and process them using a tool for analyzing methylation arrays16,17. 
	NOTE: On the heatmap, which is generated using a data analysis tool, columns are ordered by unsupervised clustering, whereas the order of rows is based on the decreasing significance of the t statistic for differential methylation from top to bottom.</li>
	<li>Divide the heatmap into high and low methylation groups using the first differentiator in the unsupervised clustering. 
	NOTE: For validation with quantitative methylation-specific PCR (qMSP), choose genes based on a larger &#946;-value in relation to CpG islands in the promoter region, and due to their suitability for primer and probe design for qMSP.</li>
</ol>
5. Quantitative Methylation-Specific PCR (qMSP)
<ol>
	<li>Use the bisulfite-modified DNA from step 3.3 as a template for fluorescence-based real-time PCR in qMSP to evaluate methylation of the promoter region in each gene analysis.</li>
	<li>Perform qMSP using a 96-well Real-Time PCR instrument (see Table of Materials).
	<ol>
		<li>Check for the promoter methylation status of the target gene on the bisulfite-modified DNA using 200 nM forward primer, 200 nM reverse primer, and 80 nM probes. Prepare the master mix with 16.6 mM (NH4)2SO4, 67 mM Tris pH 8.8, 10 mM &#946;-mercaptoethanol, 10 nM fluorescein, 0.166 mM of each deoxynucleotide triphosphate, and 0.04 U/&#956;l of DNA polymerase (see Table of Materials). The final reaction volume in each well for all assays is 25 &#956;L.</li>
		<li>Perform cycling of qMSP as follows: 95 &#176;C for 5 mins, followed by 55 cycles of 95 &#176;C for 15 s, 60 &#176;C for 1 min, and 72 &#176;C for 1 min. 
		NOTE: Target gene should be chosen based on the criteria of having larger beta values, being related to CpG islands in the promoter region, and being suitable for primer and probe design for qMSP.</li>
	</ol>
	</li>
	<li>Use Human genomic treated with CpG Methylase (M.SssI) as a positive methylation control (see Table of Materials). 
	NOTE: The final quantification of methylation is defined as the relative methylation value (RMV), and calculated as 2&#8211;&#916;&#916;Ct for each methylation detection replicate compared to the mean Ct for &#946;-Actin (ACTB)18. Primer and probe sequences are shown in Table 1. A Ct of 100 is used for undetected replicates, which gives a value of 2&#8211;&#916;&#916;Ct close to zero. The following formula is used: mean 2&#8211;&#916;&#916;Ct (RMV) = (2&#8211;&#916;&#916;Ct_replicate_1 + 2&#8211;&#916;&#916;Ct_replicate_2 + 2&#8211;&#916;&#916;Ct_replicate_3)/318.</li>
</ol>

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The authors have nothing to disclose.

There are three critical steps in obtaining accurate results from DNA methylation genome-wide analysis. The first is macrodissection of a tumor area by preferably two qualified pathologists based on representative H&#38;E stained sections. Inaccurate macrodissection can cause contamination with adjacent non-cancerous tissues, which engenders unreliable results; thus, careful macrodissection is required. The second is assessment of the DNA quality (quality check: QC). Samples which fail the QC (&#8710;Cq &#62; 5.0) may give poor quality data. Therefore, samples with &#8710;Cq &#62; 5.0 should be removed and others used. The third step is calculation of the &#946;-value, which is determined by a data analysis tool for the array platform software as the methylated signal / the total (methylated + unmethylated) signal17. The &#946;-value ranges from 0&#8211;1 (or 0%&#8211;100%), which is simple to interpret biologically17. The main problem with this value is its poor statistical properties, since its high heteroscedasticity implies that variance across samples at methylation range extremes (&#946; = 0 or &#946; = 1) is highly reduced17. In addition, due to poor sample quality, &#946;-values may not show reproducible biphasic peaks22, and samples without such peaks should be excluded from further study. In addition, target gene should be chosen based on the criteria of having larger beta values, being related to CpG islands in the promoter region, and being suitable for primer and probe design for qMSP.
Evaluation of DNA methylation at a CpG locus in the human genome is performed using microarray-based technology with a &#64257;xed number of probes to survey speci&#64257;c genomic loci. It is the most widely used method in epigenome-wide association studies (EWAS) due to its low cost, small amount of DNA required, and markedly shorter sample processing time, which allows high-throughput processing of many clinical samples23. However, an array platform for complex evaluation of DNA methylation at an individual CpG locus is limited by the number and speci&#64257;city of probes for epigenetically altered loci, which prevents exploration of some genomic regions. WGBS is generally viewed as the gold standard method due to its wider spectrum of genomic coverage10,11. However, this method has a substantial cost and a relatively long processing time for analysis of a large number of samples10,11. Thus, it is not always feasible. In comparison, the array platform for complex evaluation of DNA methylation at an individual CpG locus in the human genome is reasonable for use in terms of cost and genomic coverage. Recently, the latest upgraded bead chips have gotten ready to use24. These assays can help us analyze nearly doubled measured CpG sites, which can achieve ideal genome-wide association study (GWAS) for large sample populations.
In summary, DNA methylation genome-wide analysis with the array platform for complex evaluation of DNA methylation at an individual CpG locus in the human genome can provide important information on epigenetic biomarkers in gastrointestinal cancer. Compared with WGBS, this method is cost effective and reduces sample-processing time. Therefore, this method for detection of DNA methylation at a CpG locus is likely to be widely used in epigenetic biomarker research.

Epigenetics refers to heritable changes in gene function without alteration of the sequence of DNA1. Such changes may be due to DNA methylation, in which methyl groups on a DNA base may alter the gene expression through changes in chromatin packing. Cancer development and progression may occur if this effect results in altered expression of tumor suppressor genes2. Aging and chronic inflammation are both causes of cancer and the main reasons for changes in DNA methylation in humans3,4,5. Consequently, this allows the utilization of DNA methylation as a biomarker in cancer diagnosis, and as a target for treatment and prevention. For early detection and cancer prognosis, DNA methylation are being measured in tumor, blood, urine, and stool samples6, while demethylating agents are now being used to treat leukemias such as myelodysplastic syndrome7.
Genome-wide DNA methylation analysis using an array platform for complex evaluation of DNA methylation at an individual CpG locus in the human genome can be utilized to examine the methylation status of more than 450,000 CpG sites in genomic DNA8,9, which permits exploration of cancer epigenetics (see Table of Materials). Whole genome bisul&#64257;te sequencing (WGBS) technologies have changed our approaches in the field of epigenetics10,11. However, there are some disadvantages to the technologies in terms of a substantial cost and processing time for epigenetic analysis of a large number of samples10,11. Therefore, the array platform is more feasible for complex evaluation of DNA methylation in the human genome. The availability of approaches for genome-wide methylation analyses has improved in the past few years and allows us to expand our knowledge of how DNA methylation contributes to cancer development and progression12. Recent progresses in microarray-platform approaches provide us the rationale for genome-wide methylation analysis to identify a novel epigenetic signature in gastrointestinal cancers13. The goal of this protocol is to provide a detailed description of how this method is used for biomarker identification in GI malignancies.

<table><tbody><tr><td>(NH4)2SO4</td><td>Sigma-Aldrich</td><td>14148</td><td>Step 5.2.</td></tr><tr><td>10% Sodium dodecyl sulfate (SDS)</td><td>Quality Biological</td><td>351-032-721 EA</td><td>Step 2.1.</td></tr><tr><td>100 % Ethanol</td><td>Sigma-Aldrich</td><td>24194</td><td>Step 1.7.</td></tr><tr><td>ABI StepOnePlus Real-Time PCR System</td><td>Applied BioSystems</td><td>4376600</td><td>Step 5.2. 96-well Real-Time PCR instrument</td></tr><tr><td>CT conversion reagent</td><td>Zymo Research</td><td>D5001-1</td><td>Step 3.2.3.</td></tr><tr><td>Deoxynucleotide triphosphate (dNTP)</td><td>Invitrogen</td><td>10297-018</td><td>Step 5.2.</td></tr><tr><td>DEPC-treated water</td><td>Quality Biological</td><td>351-068-131</td><td>Step 2.1.</td></tr><tr><td>Ethylenediaminetetraacetic acid (EDTA)</td><td>Corning</td><td>46-034-CL</td><td>Step 2.1.</td></tr><tr><td>EZ DNA Methylation Kit</td><td>Zymo Research</td><td>D5002</td><td>Step 3.2.</td></tr><tr><td>Fluorescein</td><td>Bio-Rad</td><td>#1708780</td><td>Step 5.2.</td></tr><tr><td>GenomeStudio</td><td>omicX</td><td>OMICS_00854</td><td>Step 4.3. Data analysis tool for an array platform as a &amp;beta;-value, with a range from 0 to 1</td></tr><tr><td>Human genomic DNA</td><td>New England Bio Labs</td><td>N4002S</td><td>Step 5.3.</td></tr><tr><td>Infinium HD FFPE QC Kit</td><td>Illumina</td><td>WG-321-1001</td><td>Step 4.1. FFPE QC assay on a real-time PCR amplification</td></tr><tr><td>Infinium HumanMethylation450 assay</td><td>Illumina</td><td>WG-314</td><td>Step 4.2. Array platform for complex evaluation of DNA methylation to assess the methylation status of &gt;450,000 CpG sites in the genome</td></tr><tr><td>LightCycler 480</td><td>Roche</td><td>5015278001</td><td>Step 4.1.</td></tr><tr><td>M-Binding Buffer</td><td>Zymo Research</td><td>D5002-3</td><td>Step 3.2.6.</td></tr><tr><td>M-Desulphonation Buffer</td><td>Zymo Research</td><td>D5002-5</td><td>Step 3.2.9.</td></tr><tr><td>M-Dilution Buffer</td><td>Zymo Research</td><td>D5002-2</td><td>Step 3.2.1.</td></tr><tr><td>Minfi package</td><td>Bioconductor</td><td>N/A</td><td>Step 4.4.</td></tr><tr><td>M-Wash Buffer</td><td>Zymo Research</td><td>D5002-4</td><td>Step 3.2.10.</td></tr><tr><td>Platinum Taq polymerase</td><td>ThermoFisher Scientific</td><td>10966-034</td><td>Step 5.2.</td></tr><tr><td>Proteinase K</td><td>New England Biolabs</td><td>P8107S</td><td>Step 2.8.</td></tr><tr><td>Single-use polypropylene (Eppendorf) tube</td><td>Eppendorf</td><td>24533495</td><td>Step 2.5.2.</td></tr><tr><td>Tris hydrochloride (Tris-HCL) 2 M pH 8.8</td><td>Quality Biological</td><td>351-092-101</td><td>Step 2.1.</td></tr><tr><td>Xylene</td><td>Sigma-Aldrich</td><td>214736</td><td>Step 1.3.</td></tr><tr><td>Zymo Spin 1 Column</td><td>Zymo Research</td><td>C1003</td><td>Step 3.2.6.</td></tr><tr><td>&amp;beta;-Mercaptoethanol</td><td>Sigma-Aldrich</td><td>M3148</td><td>Step 5.2.</td></tr></tbody></table>

genome-wide analysis of dna methylation in gastrointestinal cancer

DNA methylation is an important epigenetic change that is biologically meaningful and a frequent focus of cancer research. Genome-wide DNA methylation is a useful measure to provide an accurate analysis of the methylation status of gastrointestinal (GI) malignancies. Given the multiple potential translational uses of DNA methylation analysis, practicing clinicians and others new to DNA methylation studies need to be able to understand step by step how these genome-wide analyses are performed. The goal of this protocol is to provide a detailed description of how this method is used for the biomarker identification in GI malignancies. Importantly, we describe three critical steps that are needed to obtain accurate results during genome-wide analysis. Clearly and concisely written, these three methods are often lacking and not noticeable to those new to epigenetic studies. We used 48 samples of a GI malignancy (gastric cancer) to highlight practically how genome-wide DNA methylation analysis can be performed for GI malignancies.

The characteristics of 48 patients with gastric cancer in the training cohort are as follows (Table 2): the median age of patients was 74 years (52&#8211;89 years), and the cohort included 38 males (79.2%), and 10 females (20.8%). There were 35 patients (72.9%) with primary gastric cancer and in 13 patients (27.1%) with remnant gastric cancer (primary gastric cancer: first occurrence of a non-metastatic malignancy in the stomach; remnant gastric cancer: cancer in the remnant stomach that developed more than 5 years after distal gastrectomy, regardless of the reason for the original resection19). There were 23 patients (47.9%) with lymph node metastasis and 25 patients (52.1%) without. First, all 48 samples (the training cohort) were loaded for identification of outliers (Figure 1A). Two samples gave peaks that were greater than two standard deviations displaced from the others, and these samples were removed (Figure 1B). Therefore, 46 samples were clustered by DNA promoter hypermethylation. The resultant heatmap was divided into two groups based on high and low methylation (Figure 2). This heatmap allows visualization of the top 50 probes within 1,500 bp of the transcriptional start site (TSS) in the differential methylation analysis. The high and low methylation groups differed in clinicopathological factors related to an aggressive malignant phenotype. That is, the type of cancer (primary gastric cancer: PGC) (p = 0.01, odds ratio = 9.09 (1.67&#8211;50.00)) and presence of lymph node metastasis (positive) (p = 0.03, odds ratio = 6.82 (1.16&#8211;40.08)) emerged as significant independent predictive factors when the clinicopathological factors were used as covariates in multivariate analysis (Table 3). Finally, we identified the EPB41L3 gene20,21 (primer and probe sequences shown in Table 1) to be strongly associated with codifying the training cohort into high and low methylation groups in the microarray analysis. Using qMSP, the results of the microarray analysis for EPB41L3 in the test cohort (126 samples) were validated. The characteristics of the patients in the test cohort are shown in Table 4. RMVs of EPB41L3 in PGC tissues were significantly higher than those in remnant gastric cancer (RGC) in univariate analysis (p = 0.01) (Figure 3A). Similarly, RMVs in samples with lymph node metastasis were significantly higher than those without lymph node metastasis (p = 0.03) (Figure 3B). In this way, DNA methylation genome-wide analysis can help us to identify specific genes to characterize certain clinical status in patients with GI malignancies.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/61355/61355fig01.jpg" /> 
Figure 1: Beta values in 48 samples (training cohort).&#160;All 48 samples (training cohort) were loaded and outliers were examined (A). Two samples had peaks that were outliers, and these were removed (B). <a href="https://www.jove.com/files/ftp_upload/61355/61355fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/61355/61355fig02.jpg" /> 
Figure 2: The resultant heatmap.&#160;The remaining 46 samples were clustered by DNA promoter hypermethylation. The heatmap was divided into high and low methylation groups. This heatmap allows visualization of the top 50 probes within 1,500 bp of the transcriptional start site (TSS) in the differential methylation analysis. <a href="https://www.jove.com/files/ftp_upload/61355/61355fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/61355/61355fig03.jpg" /> 
Figure 3: Relative methylation values (RMVs) for EPB41L3 in primary gastric cancer (PGC) vs. remnant gastric cancer (RGC), and in cases with and without lymph node metastasis.&#160;The results of microarray analysis for EPB41L3 in the test cohort (126 samples) were validated using qMSP. (A) In univariate analysis, RMVs of EPB41L3 in PGC tissues were significantly higher than those in RGC (p = 0.01). (B) Similarly, RMVs in samples with lymph node metastasis were significantly higher than in those without lymph node metastasis (p = 0.03). <a href="https://www.jove.com/files/ftp_upload/61355/61355fig03large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Gene</td>
			<td>Forward 5&#39; - 3&#39;</td>
			<td>Reverse 5&#39; - 3&#39;</td>
			<td>Probe</td>
		</tr>
		<tr>
			<td>B-ACTIN</td>
			<td>TAG GGA GTA TAT AGG TTG GGG AAG TT</td>
			<td>AAC ACA CAA TAA CAA ACA CAA ATT CAC</td>
			<td>\56-FAM\ TGT GGG GTG \ZEN\ GTG ATG GAG GAG GTT TAG \3IABkFQ\</td>
		</tr>
		<tr>
			<td>EPB41L3</td>
			<td>GGG ATA GTG GGG TTG ACG C</td>
			<td>ATA AAA ATC CCG ACG AAC GA</td>
			<td>AAA TTC GAA AAA CCG CGC GAC GCC GAA ACC A</td>
		</tr>
	</tbody>
</table>
Table 1: Primer and probe sequences.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Clinicopathological factors</td>
			<td>Variables</td>
			<td></td>
		</tr>
		<tr>
			<td>Age</td>
			<td></td>
			<td>74 (52 - 89) *</td>
		</tr>
		<tr>
			<td>Gender</td>
			<td>Male / Female</td>
			<td>38 (79.2%) / 10 (20.8%)</td>
		</tr>
		<tr>
			<td>Type</td>
			<td>PGC / RGC</td>
			<td>35 (72.9%) / 13 (27.1%)</td>
		</tr>
		<tr>
			<td>Lymph node metastasis</td>
			<td>(+) / (-)</td>
			<td>23 (47.9%) / 25 (52.1%)</td>
		</tr>
		<tr>
			<td colspan="3">PGC: Primary gastric cancer, RGC: Remnant gastric cancer</td>
		</tr>
		<tr>
			<td colspan="3">* Median (minimum-maximum)</td>
		</tr>
	</tbody>
</table>
Table 2: The characteristics in 48 patients with gastric cancer in the training cohort.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td></td>
			<td>P-value</td>
			<td>Odds ratio</td>
			<td>95% Confidence interval</td>
		</tr>
		<tr>
			<td>Type (PGC)</td>
			<td>0.01</td>
			<td>9.09</td>
			<td>1.67 &#8211; 50.00</td>
		</tr>
		<tr>
			<td>Lymph node metastasis (+)</td>
			<td>0.03</td>
			<td>6.82</td>
			<td>1.16 &#8211; 40.08</td>
		</tr>
		<tr>
			<td colspan="4">PGC: Primary gastric cancer</td>
		</tr>
	</tbody>
</table>
Table 3: Predictive factors for the high methylation group (Cluster B).
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Clinicopathological factors</td>
			<td>Variables</td>
			<td></td>
		</tr>
		<tr>
			<td>Age</td>
			<td></td>
			<td>71 (33 - 86) *</td>
		</tr>
		<tr>
			<td>Gender</td>
			<td>Male / Female</td>
			<td>96 (76.2%) / 30 (23.8%)</td>
		</tr>
		<tr>
			<td>Type</td>
			<td>PGC / RGC</td>
			<td>87 (69.0%) / 39 (31.0%)</td>
		</tr>
		<tr>
			<td>Lymph node metastasis</td>
			<td>(+) / (-)</td>
			<td>50 (39.7%) / 76 (60.3%)</td>
		</tr>
		<tr>
			<td colspan="3">PGC: Primary gastric cancer, RGC: Remnant gastric cancer</td>
		</tr>
		<tr>
			<td colspan="3">* Median (minimum-maximum)</td>
		</tr>
	</tbody>
</table>
Table 4: The characteristics in 126 patients with gastric cancer in the test cohort.

Watch this Scientific Journal Video about Genome-Wide Analysis of DNA Methylation in Gastrointestinal Cancer at JoVE.com

Genome-Wide Analysis of DNA Methylation in Gastrointestinal Cancer

Herein, we describe a procedure for genome-wide analysis of DNA methylation in gastrointestinal cancers. The procedure is of relevance to studies that investigate relationships between methylation patterns of genes and factors contributing to carcinogenesis in gastrointestinal cancers.

DNA methylation is an important epigenetic change that is biologically meaningful and a frequent focus of cancer research. Genome-wide DNA methylation is ...

genome-wide-analysis-of-dna-methylation-in-gastrointestinal-cancer

Research

JoVE Journal

Cancer Research

5.4K Views.  Johns Hopkins University School of Medicine. Herein, we describe a procedure for genome-wide analysis of DNA methylation in gastrointestinal cancers. The procedure is of relevance to studies that investigate relationships between methylation patterns of genes and factors contributing to carcinogenesis in gastrointestinal cancers.

Video: Genome-Wide Analysis of DNA Methylation in Gastrointestinal Cancer

Methyl-binding DNA capture Sequencing for Patient Tissues

Methylation is one of the essential epigenetic modifications to the DNA, which is responsible for the precise regulation of genes required for stable development and differentiation of different tissue types. Dysregulation of this process is often the hallmark of various diseases like cancer. Here, we outline one of the recent sequencing techniques, Methyl-Binding DNA Capture sequencing (MBDCap-seq), used to quantify methylation in various normal and disease tissues for large patient cohorts. We describe a detailed protocol of this affinity enrichment approach along with a bioinformatics pipeline to achieve optimal quantification. This technique has been used to sequence hundreds of patients across various cancer types as a part of the 1,000 methylome project (Cancer Methylome System).

Here we present a protocol to investigate genome wide DNA methylation in large scale clinical patient screening studies using the Methyl-Binding DNA Capture sequencing (MBDCap-seq or MBD-seq) technology and the subsequent bioinformatics analysis pipeline.

Methylation is one of the essential epigenetic modifications to the DNA, which is responsible for the precise regulation of genes required for stable ...

Genetics

Comprehensive DNA Methylation Analysis Using a Methyl-CpG-binding Domain Capture-based Method in Chronic Lymphocytic Leukemia Patients

The role of long noncoding RNAs (lncRNAs) in cancer is coming to the forefront due to growing interest in understanding their mechanistic functions during cancer development and progression. Despite this, the global epigenetic regulation of lncRNAs and repetitive sequences in cancer has not been well investigated, particularly in chronic lymphocytic leukemia (CLL). This study focuses on a unique approach: the immunoprecipitation-based capture of double-stranded, methylated DNA fragments using methyl-binding domain (MBD) proteins, followed by next-generation sequencing (MBD-seq). CLL patient samples belonging to two prognostic subgroups (5 IGVH mutated samples + 5 IGVH unmutated samples) were used in this study. Analysis revealed 5,800 hypermethylated and 12,570 hypomethylated CLL-specific differentially methylated genes (cllDMGs) compared to normal healthy controls. Importantly, these results identified several CLL-specific, differentially methylated lncRNAs, repetitive elements, and protein-coding genes with potential prognostic value. This work outlines a detailed protocol for an MBD-seq and bioinformatics pipeline developed for the comprehensive analysis of global methylation profiles in highly CpG-rich regions using CLL patient samples. Finally, a protein-coding gene and an lncRNA were validated using pyrosequencing, which is a highly quantitative method to analyze CpG methylation levels to further corroborate the findings from the MBD-seq protocol.

This work describes an optimized methyl-CpG-binding domain (MBD) sequencing protocol and a computational pipeline to identify differentially methylated CpG-rich regions in chronic lymphocytic leukemia (CLL) patients.

The role of long noncoding RNAs (lncRNAs) in cancer is coming to the forefront due to growing interest in understanding their mechanistic functions during ...

Immunostaining for DNA Modifications: Computational Analysis of Confocal Images

For several decades, 5-methylcytosine (5mC) has been thought to be the only DNA modification with a functional significance in metazoans. The discovery of enzymatic oxidation of 5mC to 5-hydroxymethylcytosine (5hmC), 5-formylcytosine (5fC) and 5-carboxylcytosine (5caC) as well as detection of N6-methyladenine (6mA) in the DNA of multicellular organisms provided additional degrees of complexity to the epigenetic research. According to a growing body of experimental evidence, these novel DNA modifications may play specific roles in different cellular and developmental processes. Importantly, as some of these marks (e. g. 5hmC, 5fC and 5caC) exhibit tissue- and developmental stage-specific occurrence in vertebrates, immunochemistry represents an important tool allowing assessment of spatial distribution of DNA modifications in different biological contexts. Here the methods for computational analysis of DNA modifications visualized by immunostaining followed by confocal microscopy are described. Specifically, the generation of 2.5 dimension (2.5D) signal intensity plots, signal intensity profiles, quantification of staining intensity in multiple cells and determination of signal colocalization coefficients are shown. Collectively, these techniques may be operational in evaluating the levels and localization of these DNA modifications in the nucleus, contributing to elucidating their biological roles in metazoans.

Newly discovered oxidized forms of 5-methylcytosine (oxi-mCs), 5-hydroxymethylcytosine (5hmC), 5-formylcytosine (5fC) and 5-carboxylcytosine (5caC) may represent distinct DNA modifications with unique functional roles. Here a semi-quantitative workflow for visualization of oxi-mCs' spatial distribution, signal intensity profiling and colocalization is described.

For several decades, 5-methylcytosine (5mC) has been thought to be the only DNA modification with a functional significance in metazoans. The discovery of ...

Methylated DNA Immunoprecipitation

The identification of DNA methylation patterns is a common procedure in the study of epigenetics, as methylation is known to have significant effects on gene expression, and is involved with normal development as well as disease 1-4. Thus, the ability to discriminate between methylated DNA and non-methylated DNA is essential for generating methylation profiles for such studies. Methylated DNA immunoprecipitation (MeDIP) is an efficient technique for the extraction of methylated DNA from a sample of interest 5-7. A sample of as little as 200 ng of DNA is sufficient for the antibody, or immunoprecipitation (IP), reaction. DNA is sonicated into fragments ranging in size from 300-1000 bp, and is divided into immunoprecipitated (IP) and input (IN) portions. IP DNA is subsequently heat denatured and then incubated with anti-5'mC, allowing the monoclonal antibody to bind methylated DNA. After this, magnetic beads containing a secondary antibody with affinity for the primary antibody are added, and incubated. These bead-linked antibodies will bind the monoclonal antibody used in the first step. DNA bound to the antibody complex (methylated DNA) is separated from the rest of the DNA by using a magnet to pull the complexes out of solution. Several washes using IP buffer are then performed to remove the unbound, non-methylated DNA. The methylated DNA/antibody complexes are then digested with Proteinase K to digest the antibodies leaving only the methylated DNA intact. The enriched DNA is purified by phenol:chloroform extraction to remove the protein matter and then precipitated and resuspended in water for later use. PCR techniques can be used to validate the efficiency of the MeDIP procedure by analyzing the amplification products of IP and IN DNA for regions known to lack and known to contain methylated sequences. The purified methylated DNA can then be used for locus-specific (PCR) or genome-wide (microarray and sequencing) methylation studies, and is particularly useful when applied in conjunction with other research tools such as gene expression profiling and array comparative genome hybridization (CGH) 8. Further investigation into DNA methylation will lead to the discovery of new epigenetic targets, which in turn, may be useful in developing new therapeutic or prognostic research tools for diseases such as cancer that are characterized by aberrantly methylated DNA 2, 4, 9-11.

This video demonstrates the protocol for methylated DNA immunoprecipitation (MeDIP). MeDIP is a two day procedure that selectively extracts methylated DNA fragments from a genomic DNA sample using antibodies with specificity for 5 -methylcytosine (anti-5 mC).

The identification of DNA methylation patterns is a common procedure in the study of epigenetics, as methylation is known to have significant effects on ...

Biology

LINE-1 Methylation Analysis in Mesenchymal Stem Cells Treated with Osteosarcoma-Derived Extracellular Vesicles

Methylation-specific probe amplification (MSPA) is a simple and robust technique that can be used to detect relative differences in methylation levels of DNA samples. It is resourceful, requires small amounts of DNA, and takes around 4&#8211;5 h of hands-on work. In the presented technique, DNA samples are first denatured then hybridized to probes that target DNA at either methylated or reference sites as a control. Hybridized DNA is separated into parallel reactions, one undergoing only ligation and the other undergoing ligation followed by HhaI-mediated digestion at unmethylated GCGC sequences. The resultant DNA fragments are amplified by PCR and separated by capillary electrophoresis. Methylated GCGC sites are not digested by HhaI and produce peak signals, while unmethylated GCGC sites are digested and no peak signals are generated. Comparing the control-normalized peaks of digested and undigested versions of each sample provides the methylation dosage ratio of a DNA sample. Here, MSPA is used to detect the effects of osteosarcoma-derived extracellular vesicles (EVs) on the methylation status of long interspersed nuclear element-1 (LINE-1) in mesenchymal stem cells. LINE-1s are repetitive DNA elements that typically undergo hypomethylation in cancer and, in this capacity, may serve as a biomarker. Ultracentrifugation is also used as a cost-effective method to separate extracellular vesicles from biological fluids (i.e., when preparing EV-depleted fetal bovine serum [FBS] and isolating EVs from osteosarcoma conditioned media [differential centrifugation]). For methylation analysis, custom LINE-1 probes are designed to target three methylation sites in the LINE-1 promoter sequence and seven control sites. This protocol demonstrates the use of MSPA for LINE-1 methylation analysis and describes the preparation of EV-depleted FBS by ultracentrifugation.

Described here is the use of a methylation-specific probe amplification method to analyze methylation levels of LINE-1 elements in mesenchymal stem cells treated with osteosarcoma-derived extracellular vesicles. Ultracentrifugation, a popular procedure for separating extracellular vesicles from fetal bovine serum, is also demonstrated.

Methylation-specific probe amplification (MSPA) is a simple and robust technique that can be used to detect relative differences in methylation levels of ...

Sample Preparation to Bioinformatics Analysis of DNA Methylation: Association Strategy for Obesity and Related Trait Studies

Obesity is directly connected to lifestyle and has been associated with DNA methylation changes that may cause alterations in the adipogenesis and lipid storage processes contributing to the development of the disease. We demonstrate a complete protocol from selection to epigenetic data analysis of patients with and without obesity. All steps from the protocol were tested and validated in a pilot study. 32 women participated in the study, in which 15 individuals were classified with obesity according to Body Mass Index (BMI) (45.1 &#177; 5.4 kg/m2); and 17 individuals were classified without obesity according to BMI (22.6 &#177; 1.8 kg/m2). In the group with obesity, 564 CpG sites related to fat mass were identified by linear regression analysis. The CpG sites were in the promoter regions. The differential analysis found 470 CpGs hypomethylated and 94 hypermethylated sites in individuals with obesity. The most hypomethylated enriched pathwayswere in the RUNX, WNT signaling, and response to hypoxia. The hypermethylated pathways were related to insulin secretion, glucagon signaling, and Ca2+. We conclude that the protocol effectively identified DNA methylation patterns and trait-related DNA methylation. These patterns could be associated with altered gene expression, affecting adipogenesis and lipid storage. Our results confirmed that an obesogenic lifestyle could promote epigenetic changes in human DNA.

The present study describes the workflow to manage DNA methylation data obtained by microarray technologies. The protocol demonstrates steps from sample preparation to data analysis. All procedures are described in detail, and the video shows the significant steps.

Obesity is directly connected to lifestyle and has been associated with DNA methylation changes that may cause alterations in the adipogenesis and lipid ...

TMEM200A: A Novel Oncogene Regulating EMT and PI3K/AKT in Gastric Cancer

Multiomics Analysis of TMEM200A as a Pan-Cancer Biomarker

The transmembrane protein, TMEM200A, is known to be associated with human cancers and immune infiltration. Here, we assessed the function of TMEM200A in common cancers by multiomics analysis and used in vitro cell cultures of gastric cells to verify the results. The expression of TMEM200A in several human cancer types was assessed using the RNA-seq data from the UCSC Xena database. Bioinformatic analysis revealed a potential role of TMEM200A as a diagnostic and prognostic biomarker. 
Cultures of normal gastric and cancer cell lines were grown and TMEM200A was knocked down. The expression levels of TMEM200A were measured by using quantitative real-time polymerase chain reaction and western blotting. In vitro loss-of-function studies were then used to determine the roles of TMEM200A in the malignant behavior and tumor formation of gastric cancer (GC) cells. Western blots were used to assess the effect of the knockdown on epithelial-mesenchymal transition (EMT) and PI3K/AKT signaling pathway in GC. Bioinformatic analysis showed that TMEM200A was expressed at high levels in GC. 
The proliferation of GC cells was inhibited by TMEM200A knockdown, which also decreased vimentin, N-cadherin, and Snai proteins, and inhibited AKT phosphorylation. The PI3K/AKT signaling pathway also appeared to be involved in TMEM200A-mediated regulation of GC development. The results presented here suggest that TMEM200A regulates the tumor microenvironment by affecting the EMT. TMEM200A may also affect EMT through PI3K/AKT signaling, thus influencing the tumor microenvironment. Therefore, in pan-cancers, especially GC, TMEM200A may be a potential biomarker and oncogene.

Author Spotlight: Unveiling Transmembrane Protein Family-Related Markers in Gastric Cancer and Implications for Targeted Therapies 

Here, a protocol is presented in which multiple bioinformatic tools are combined to study the biological functions of TMEM200A in cancer. In addition, we also experimentally validate the bioinformatics predictions.

The transmembrane protein, TMEM200A, is known to be associated with human cancers and immune infiltration. Here, we assessed the function of TMEM200A in ...

DNA Methylation: Bisulphite Modification and Analysis

Epigenetics describes the heritable changes in gene function that occur independently to the DNA sequence. The molecular basis of epigenetic gene regulation is complex, but essentially involves modifications to the DNA itself or the proteins with which DNA associates. The predominant epigenetic modification of DNA in mammalian genomes is methylation of cytosine nucleotides (5-MeC). DNA methylation provides instruction to gene expression machinery as to where and when the gene should be expressed. The primary target sequence for DNA methylation in mammals is 5'-CpG-3' dinucleotides (Figure 1). CpG dinucleotides are not uniformly distributed throughout the genome, but are concentrated in regions of repetitive genomic sequences and CpG "islands" commonly associated with gene promoters (Figure 1). DNA methylation patterns are established early in development, modulated during tissue specific differentiation and disrupted in many disease states including cancer. To understand the biological role of DNA methylation and its role in human disease, precise, efficient and reproducible methods are required to detect and quantify individual 5-MeCs.
This protocol for bisulphite conversion is the "gold standard" for DNA methylation analysis and facilitates identification and quantification of DNA methylation at single nucleotide resolution. The chemistry of cytosine deamination by sodium bisulphite involves three steps (Figure 2). (1) Sulphonation: The addition of bisulphite to the 5-6 double bond of cytosine (2) Hydrolic Deamination: hydrolytic deamination of the resulting cytosine-bisulphite derivative to give a uracil-bisulphite derivative (3) Alkali Desulphonation: Removal of the sulphonate group by an alkali treatment, to give uracil. Bisulphite preferentially deaminates cytosine to uracil in single stranded DNA, whereas 5-MeC, is refractory to bisulphite-mediated deamination. Upon PCR amplification, uracil is amplified as thymine while 5-MeC residues remain as cytosines, allowing methylated CpGs to be distinguished from unmethylated CpGs by presence of a cytosine "C" versus thymine "T" residue during sequencing.
DNA modification by bisulphite conversion is a well-established protocol that can be exploited for many methods of DNA methylation analysis. Since the detection of 5-MeC by bisulphite conversion was first demonstrated by Frommer et al.1 and Clark et al.2, methods based around bisulphite conversion of genomic DNA account for the majority of new data on DNA methylation. Different methods of post PCR analysis may be utilized, depending on the degree of specificity and resolution of methylation required. Cloning and sequencing is still the most readily available method that can give single nucleotide resolution for methylation across the DNA molecule.

The gold standard for DNA methylation analysis is genomic sequencing of bisulphite converted DNA. This method takes advantage of the increased sensitivity of cytosine compared with 5-methylcytosine (5-MeC) to bisulphite deamination under acidic conditions. Unmethylated cytosines can be distinguished from methylated cytosines after PCR amplification of the target genomic DNA.

Epigenetics describes the heritable changes in gene function that occur independently to the DNA sequence. The molecular basis of epigenetic gene ...

Enhanced Reduced Representation Bisulfite Sequencing for Assessment of DNA Methylation at Base Pair Resolution

DNA methylation pattern mapping is heavily studied in normal and diseased tissues. A variety of methods have been established to interrogate the cytosine methylation patterns in cells. Reduced representation of whole genome bisulfite sequencing was developed to detect quantitative base pair resolution cytosine methylation patterns at GC-rich genomic loci. This is accomplished by combining the use of a restriction enzyme followed by bisulfite conversion. Enhanced Reduced Representation Bisulfite Sequencing (ERRBS) increases the biologically relevant genomic loci covered and has been used to profile cytosine methylation in DNA from human, mouse and other organisms. ERRBS initiates with restriction enzyme digestion of DNA to generate low molecular weight fragments for use in library preparation. These fragments are subjected to standard library construction for next generation sequencing. Bisulfite conversion of unmethylated cytosines prior to the final amplification step allows for quantitative base resolution of cytosine methylation levels in covered genomic loci. The protocol can be completed within four days. Despite low complexity in the first three bases sequenced, ERRBS libraries yield high quality data when using a designated sequencing control lane. Mapping and bioinformatics analysis is then performed and yields data that can be easily integrated with a variety of genome-wide platforms. ERRBS can utilize small input material quantities making it feasible to process human clinical samples and applicable in a range of research applications. The video produced demonstrates critical steps of the ERRBS protocol.

Enhanced Reduced Representation Bisulfite Sequencing is a method for the preparation of sequencing libraries for DNA methylation analysis based on restriction enzyme digestion combined with cytosine bisulfite conversion. This protocol requires 50 ng of starting material and yields base pair resolution data at GC-rich genomic regions.

DNA methylation pattern mapping is heavily studied in normal and diseased tissues. A variety of methods have been established to interrogate the cytosine ...

DNA Methylation Analysis

Methylation at CpG dinucleotides is a chemical modification of DNA hypothesized to play important roles in regulating gene expression. In particular, the methylation of clusters of methylation sites, called &#8220;CpG islands&#8221;, near promoters and other gene regulatory elements may contribute to the stable silencing of genes, for example, during epigenetic processes such as genomic imprinting and X-chromosome inactivation. At the same time, aberrant CpG methylation has been shown to be associated with cancer.In this video, the biological functions and mechanisms of DNA methylation will be presented, along with various techniques used to identify methylation sites in the genome. We will then examine the steps of bisulfite analysis, one of the most commonly used methods for detecting DNA methylation, as well as several applications of this technique.

Methylation at CpG dinucleotides is a chemical modification of DNA hypothesized to play important roles in regulating gene expression. In particular, the ...