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 &#946;-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 &#62;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>&#946;-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 ...

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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

Cell-Free DNA Integrity Analysis in Urine Samples

Although the presence of circulating cell-free DNA in plasma or serum has been widely shown to be a suitable source of biomarkers for many types of cancer, few studies have focused on the potential use of urine cell-free (UCF) DNA. Starting from the hypotheses that normal apoptotic cells produce highly fragmented DNA and that cancer cells release longer DNA, the potential role of UCF DNA integrity was evaluated as an early diagnostic marker capable of distinguishing between patients with prostate or bladder cancer and healthy individuals.
A UCF DNA integrity analysis is proposed on the basis of four quantitative real-time PCRs of four sequences longer than 250 bp: c-MYC, BCAS1, HER2, and AR. Sequences that frequently have an increased DNA copy number in bladder and prostate cancers were chosen for the analysis, but the method is flexible, and these genes could be substituted with other genes of interest. The potential utility of UCF DNA as a source of biomarkers has already been demonstrated for urologic malignancies, thus paving the way for further studies on UCF DNA characterization. The UCF DNA integrity test has the advantage of being non-invasive, rapid, and easy to perform, with only a few milliliters of urine needed to carry out the analysis.

A method for analyzing DNA integrity in the cell-free supernatant fraction of urine samples is proposed. The method is suitable for early detection of urological malignancies and has proven accurate for the early diagnosis of bladder cancer.

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Isolation of Circulating Tumor Cells in an Orthotopic Mouse Model of Colorectal Cancer

Despite the advantages of easy applicability and cost-effectiveness, subcutaneous mouse models have severe limitations and do not accurately simulate tumor biology and tumor cell dissemination. Orthotopic mouse models have been introduced to overcome these limitations; however, such models are technically demanding, especially in hollow organs such as the large bowel. In order to produce uniform tumors which reliably grow and metastasize, standardized techniques of tumor cell preparation and injection are critical. 
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We describe the establishment of orthotopic colorectal tumors via injection of tumor cells or organoids into the cecum of mice and the subsequent isolation of circulating tumor cells (CTCs) from this model.

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A Genetically Engineered Mouse Model of Sporadic Colorectal Cancer

Despite the advantages of easy applicability and cost-effectiveness, colorectal cancer mouse models based on tumor cell injection have severe limitations and do not accurately simulate tumor biology and tumor cell dissemination. Genetically engineered mouse models have been introduced to overcome these limitations; however, such models are technically demanding, especially in large organs such as the colon in which only a single tumor is desired.
As a result, an immunocompetent, genetically engineered mouse model of colorectal cancer was developed which develops highly uniform tumors and can be used for tumor biology studies as well as therapeutic trials. Tumor development is initiated by surgical, segmental infection of the distal colon with adeno-cre virus in compound conditionally mutant mice. The tumors can be easily detected and monitored via colonoscopy. We here describe the surgical technique of segmental adeno-cre infection of the colon, the surveillance of the tumor via high-resolution colonoscopy and present the resulting colorectal tumors.

A protocol for the establishment of a genetically engineered mouse model of colorectal cancer by segmental adeno-cre infection and its surveillance via high-resolution colonoscopy is presented.

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Genome-wide RNAi Screening to Identify Host Factors That Modulate Oncolytic Virus Therapy

High-throughput genome-wide RNAi (RNA interference) screening technology has been widely used for discovering host factors that impact&#160;virus replication. Here we present the application of this technology to uncovering host targets that specifically modulate the replication of Maraba virus, an oncolytic rhabdovirus, and vaccinia virus with the goal of enhancing therapy. While the protocol has been tested for use with oncolytic Maraba virus and oncolytic vaccinia virus, this approach is applicable to other oncolytic viruses and can also be utilized for identifying host targets that modulate virus replication in mammalian cells in general. This protocol describes the development and validation of an assay for high-throughput RNAi screening in mammalian cells, the key considerations and preparation steps important for conducting a primary high-throughput RNAi screen, and a step-by-step guide for conducting a primary high-throughput RNAi screen; in addition, it broadly outlines the methods for conducting secondary screen validation and tertiary validation studies. The benefit of high-throughput RNAi screening is that it allows one to catalogue, in an extensive and unbiased fashion, host factors that modulate any aspect of virus replication for which one can develop an in vitro assay such as infectivity, burst size, and cytotoxicity. It has the power to uncover biotherapeutic targets unforeseen based on current knowledge.

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Simple and Rapid Method to Obtain High-quality Tumor DNA from Clinical-pathological Specimens Using Touch Imprint Cytology

It is critical to determine the mutational status in cancer before administration and treatment of specific molecular targeted drugs for cancer patients. In the clinical setting, formalin-fixed paraffin-embedded (FFPE) tissues are widely used for genetic testing. However, FFPE DNA is generally damaged and fragmented during the fixation process with formalin. Therefore, FFPE DNA is sometimes not adequate for genetic testing because of low quality and quantity of DNA. Here we present a method of touch imprint cytology (TIC) to obtain genomic DNA from cancer cells, which can be observed under a microscope. Cell morphology and cancer cell numbers can be evaluated using TIC specimens. Furthermore, the extraction of genomic DNA from TIC samples can be completed within two days. The total amount and quality of TIC DNA obtained using this method was higher than that of FFPE DNA. This rapid and simple method allows researchers to obtain high-quality DNA for genetic testing (e.g., next generation sequencing analysis, digital PCR, and quantitative real time PCR) and to shorten the turnaround time for reporting results.

Obtaining high-quality genomic DNA from tumor tissues is an essential first step for analyzing genetic alterations using next generation sequencing. In this article, we present a simple and rapid method to enrich tumor cells and obtain intact DNA from touch imprint cytology specimens.

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An Integrated Platform for Genome-wide Mapping of Chromatin States Using High-throughput ChIP-sequencing in Tumor Tissues

Histone modifications constitute a major component of the epigenome and play important regulatory roles in determining the transcriptional status of associated loci. In addition, the presence of specific modifications has been used to determine the position and identity non-coding functional elements such as enhancers. In recent years, chromatin immunoprecipitation followed by next generation sequencing (ChIP-seq) has become a powerful tool in determining the genome-wide profiles of individual histone modifications. However, it has become increasingly clear that the combinatorial patterns of chromatin modifications, referred to as Chromatin States, determine the identity and nature of the associated genomic locus. Therefore, workflows consisting of robust high-throughput (HT) methodologies for profiling a number of histone modification marks, as well as computational analyses pipelines capable of handling myriads of ChIP-Seq profiling datasets, are needed for comprehensive determination of epigenomic states in large number of samples. The HT-ChIP-Seq workflow presented here consists of two modules: 1) an experimental protocol for profiling several histone modifications from small amounts of tumor samples and cell lines in a 96-well format; and 2) a computational data analysis pipeline that combines existing tools to compute both individual mark occupancy and combinatorial chromatin state patterns. Together, these two modules facilitate easy processing of hundreds of ChIP-Seq samples in a fast and efficient manner. The workflow presented here is used to derive chromatin state patterns from 6 histone mark profiles in melanoma tumors and cell lines. Overall, we present a comprehensive ChIP-seq workflow that can be applied to dozens of human tumor samples and cancer cell lines to determine epigenomic aberrations in various malignancies.

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Elastic Staining on Paraffin-embedded Slides of pT3N0M0 Gastric Cancer Tissue

The elastic lamina, which is usually located in the sub-mesothelial layer, adjacent to the peritoneum mesothelial cells, is amphiphilic on hematoxylin and eosin (H&amp;E) staining but can be visualized through elastic staining. One of the important benefits of elastic staining is that it makes it easy to determine whether tumor cells have invaded beyond the elastic lamina. This helps in examining the extent of peritoneal surface invasion, thereby distinguishing the pT3 and pT4 stages in gastrointestinal cancer. In this study, we present a protocol to identify elastic fibers in the formalin-fixed, paraffin-embedded pT3N0M0 gastric cancer tissue sections. We prepare 5-&#181;m paraffin sections fixed on slides, and then all the slides are deparaffinated and rehydrated during the staining procedure. Subsequently, all the sections are oxidized by potassium permanganate, bleached by oxalic acid, stained by elastic staining, and counterstained by Van Gieson's. Finally, we examine the effect of staining; elastic lamina is stained blue-black and collagen fibers are stained red, while the cancer cells are stained in varying shades of yellow. We also describe in detail the methods used for determining the positional relationship between cancer cells and elastic lamina. This method is simple, low-cost, and widely applicable in the identification of peritoneal surface invasion in gastrointestinal cancer because of its outstanding selectivity for elastic fibers and authentic results.

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Testing Targeted Therapies in Cancer using Structural DNA Alteration Analysis and Patient-Derived Xenografts

We present here an integrative approach for testing efficacy of targeted therapies that combines the next generation sequencing technolo-gies, therapeutic target analyses and drug response monitoring using patient derived xenografts (PDX). This strategy was validated using ovarian tumors as an example. The mate-pair next generation sequencing (MPseq) protocol was used to identify structural alterations and followed by analysis of potentially targetable alterations. Human tumors grown in immunocompromised mice were treated with drugs selected based on the genomic analyses. Results demonstrated a good correlation between the predicted and the observed responses in the PDX model. The presented approach can be used to test the efficacy of combination treatments and aid personalized treatment for patients with recurrent cancer, specifically in cases when standard therapy fails and there is a need to use drugs off label.

Here we present a protocol to test the efficacy of targeted therapies selected based on the genomic makeup of a tumor. The protocol describes identification and validation of structural DNA rearrangements, engraftment of patients&#8217; tumors into mice and testing responses to corresponding drugs.

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Evaluating Cell Death Using Cell-Free Supernatant of Probiotics in Three-Dimensional Spheroid Cultures of Colorectal Cancer Cells

This manuscript describes a protocol to evaluate cancer cell deaths in three dimensional (3D) spheroids of multicellular types of cancer cells using supernatants from Lactobacillus fermentum cell culture, considered as probiotics cultures. The use of 3D cultures to test Lactobacillus cell-free supernatant (LCFS) are a better option than testing in 2D monolayers, especially as L. fermentum can produce anti-cancer effects within the gut. L. fermentum supernatant was identified to possess increased anti-proliferative effects against several colorectal cancer (CRC) cells in 3D culture conditions. Interestingly, these effects were strongly related to the culture model, demonstrating the notable ability of L. fermentum to induce cancer cell death. Stable spheroids were generated from diverse CRCs (colorectal cancer cells) using the protocol presented below. This protocol of generating 3D spheroid is time saving and cost effective. This system was developed to easily investigate the anti-cancer effects of LCFS in multiple types of CRC spheroids. As expected, CRC spheroids treated with LCFS strongly induced cell death during the experiment and expressed specific apoptosis molecular markers as analyzed by qRT-PCR, western blotting, and FACS analysis. Therefore, this method is valuable for exploring cell viability and evaluating the efficacy of anti-cancer drugs.

Here methods are presented to understand anti-cancer effects of Lactobacillus cell-free supernatant (LCFS). Colorectal cancer cell lines show cell deaths when treated with LCFS in 3D cultures. The process of generating spheroids can be optimized depending on the scaffold and the analysis methods presented are useful for evaluating the involved signaling pathways.

This manuscript describes a protocol to evaluate cancer cell deaths in three dimensional (3D) spheroids of multicellular types of cancer cells using ...