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Abstract

  1. Top of page
  2. Abstract
  3. Materials and Methods
  4. Results
  5. Discussion
  6. Acknowledgments
  7. References

The presence of high levels of aberrant DNA methylation in gastric mucosae correlates with risk of gastric cancer. Some gastric cancers are known to have methylation of multiple CpG islands (CGI), which is referred to as the CGI methylator phenotype (CIMP). In the present study, we aimed to clarify the possible association between the CIMP in cancers and high methylation levels in their background mucosae by accurate quantitative methylation analysis of 14 carefully selected promoter CGI. Methylation levels were measured in 66 cancers and their background mucosae, along with 19 normal mucosae of healthy volunteers. Methylation in cancers was classified as absent (methylation level = 0%) or positive. The number of methylated CGI in a cancer showed a continuous distribution, and cancers were classified as CIMP high (21 cases), CIMP low (30 cases), or CIMP negative (15 cases). CIMP-high gastric cancer patients had significantly better survival rates than CIMP-negative patients. Of the Epstein–Barr virus-positive gastric cancers studied, eight out of nine presented as CIMP high. Methylation in background mucosae showed a unimodal distribution, and was assessed by their degree. The gastric mucosae of cancer patients showed higher levels than normal gastric mucosae of healthy volunteers. Finally, the CIMP-high, CIMP-low, and CIMP-negative statuses in cancers were not associated with methylation levels of individual genes and their means in the background mucosae. These showed that the CIMP statuses in gastric cancers had no association with methylation levels in the background gastric mucosae. (Cancer Sci 2007; 98: 1853–1861)

Gastric cancer is one of the major causes of cancer death in Asia and some European countries.(1) Regarding the molecular mechanisms of gastric cancers, inactivation of p53, CDH1, CDKN2A, and hMLH1 is well known, and the latter three genes are inactivated more frequently by aberrant DNA methylation of their promoter CpG islands (CGI) than by mutations.(2) As an etiological factor for gastric cancers, Helicobacter pylori infection is known to elevate gastric cancer risk 2.2–21-fold.(3–5) We have recently shown that H. pylori infection potently induces aberrant DNA methylation in gastric mucosae,(6) and that DNA methylation levels in gastric mucosae correlate with risk of gastric cancer in individuals without current H. pylori infection.(6,7) These findings explain why aberrant DNA methylation is frequently associated with gastric cancers.

Methylation of multiple CGI in a cancer was first observed in colorectal cancers.(8) The number of methylated CGI showed a bimodal distribution,(8,9) and the phenotype was designated as the CGI methylator phenotype (CIMP). A recent study using accurate and non-biased quantitative methylation analysis showed that a group of colorectal cancers with CIMP was associated with BRAF mutations.(10) Analysis of methylation levels in the matched non-cancerous background colonic mucosa showed that high methylation levels of specific genes were associated with the presence of CIMP in colorectal cancers.(11) In contrast and surprisingly, although colonic mucosae of patients with ulcerative colitis show accumulation of aberrant methylation,(12,13) CIMP-positive cancers were less frequent in ulcerative colitis-associated colorectal cancers than in sporadic colorectal cancers.(14)

Unlike colorectal cancers, the number of methylated CGI does not show a bimodal distribution in gastric cancers,(15–19) and these cancers have been classified as CIMP high, CIMP low, and CIMP negative for convenience. However, some studies observed that CIMP-high groups were associated with better prognosis,(17,19,20) or with infection with Epstein–Barr virus (EBV).(19–22) These results indicated that CIMP-positive gastric cancers might consist of several different entities. Although gastric cancers arising from gastric mucosae with high methylation levels are likely to have methylation of multiple CGI, the association has not been demonstrated.

Technical limitations may help to explain the ambiguity of CIMP status in gastric cancers. CIMP in gastric cancers has been analyzed only by qualitative methods, such as conventional methylation-specific polymerase chain reaction (PCR), or by combined bisulfite-restriction analysis, which is limited in the number and location of CpG sites that can be analyzed. In addition, a methylation profile of a cancer is dependent on the CGI used for the analysis.(8,16,23) It is known that CGI in different locations relative to a gene show different susceptibility to DNA methylation,(24) therefore CGI with a uniform location relative to a gene should be used. To avoid selection bias of cells with methylation of a CGI, CGI whose methylation does not confer positive or negative selection should be used.

The aim of the present study was to clarify the presence of CIMP in human gastric cancers by an accurate quantitative methylation analysis of selected CGI, and to analyze the effect of methylation in the background non-cancerous gastric mucosae on the CIMP in cancers. We analyzed promoter CGI of one putative tumor-suppressor gene (LOX),(25) and 11 genes that can be methylated not only in gastric cancers but also in non-cancerous gastric mucosae, and are unlikely to cause selection bias.(26) We also analyzed promoter CGI of two tumor-suppressor genes (CDKN2A and hMLH1).

Materials and Methods

  1. Top of page
  2. Abstract
  3. Materials and Methods
  4. Results
  5. Discussion
  6. Acknowledgments
  7. References

Patients and tissue samples.  Sixty-six gastric cancer tissue specimens and background non-cancerous gastric mucosae were obtained from 66 patients (48 men and 18 women; average age 61.9 years, ranging from 35 to 81 years) who underwent gastrectomy due to gastric cancers. Normal gastric mucosae were also obtained from 19 H. pylori-negative healthy volunteers (5 men and 14 women; average age 59.1 years, ranging from 29 to 91 years) who underwent endoscopy for gastric cancer screening. Informed consent was obtained from all of the patients and healthy volunteers before collection of the samples. Cancers and background non-cancerous mucosae were frozen in liquid nitrogen immediately after biopsy, and stored at –80°C until extraction of genomic DNA. High-molecular weight DNA was extracted using the phenol–chloroform method. All of the cancers were diagnosed histologically according to the Japanese classification of gastric carcinoma,(27) and classified according to the Lauren classification system.(28)

Epstein–Barr virus-positive gastric cancers were determined by the presence of EBER1 in gastric cancer tissues by in situ hybridization using formalin-fixed and paraffin-embedded specimens.(29) The presence of H. pylori infection in gastric cancer patients was analyzed by detecting genomic DNA of H. pylori in gastric mucosae using the PCR method. The H. pylori-specific primers were: forward primer, 5′-AAC CCC CTT TCT TAG TTG CT-3′; and reverse primer, 5′-CAT GGC TGA TTT GCG ATT AC-3′. The presence of H. pylori infection in healthy volunteers was analyzed using a serum anti-H. pylori IgG antibody test (SBS, Kanagawa, Japan).

Sodium bisulfite modification, quantitative methylation-specific PCR, and bisulfite sequencing.  Bisulfite modification was carried out using 500 ng BamHI-digested genomic DNA as described previously,(30) and the modified DNA was suspended in 40 µL TE buffer.

For real-time methylation-specific PCR, an aliquot of 2 µL was amplified by PCR using a primer set specific to methylated or unmethylated sequences. Fully unmethylated DNA was prepared by amplifying human genomic DNA without H. pylori infection using the GenomiPhi amplification system (Amersham Biosciences, Uppsala, Sweden),(31) and fully methylated DNA was prepared by methylating genomic DNA with SssI methylase (New England Biolabs, Beverly, MA, USA). Using this control DNA, an annealing temperature specific for a primer set was determined. Real-time PCR was carried out using SYBR Green I (BioWhittaker Molecular Applications, Rockland, ME, USA) and an iCycler Thermal Cycler (Bio-Rad Laboratories, Hercules, CA, USA). Standard DNA was prepared by cloning PCR products into the pGEM-T Easy vector (Promega, Madison, WI, USA) or by purifying their PCR products using the Wizard SV Gel and PCR clean-up system (Promega). The number of molecules in a sample was determined by comparing its amplification with that of standard DNA that contained known numbers of molecules (10–106 molecules). Based on the numbers of methylated molecules and unmethylated molecules for a genomic region in a sample, methylation levels were calculated as the fraction of methylated molecules in the total number of DNA molecules (no. methylated molecules + no. unmethylated molecules). The primer sequences and PCR conditions are shown in Table 1.

Table 1. Primers for real-time methylation-specific polymerase chain reaction
GeneMethylation statusPrimer sequencesLength (bp)Annealing temperature (°C)
Forward (5′[RIGHTWARDS ARROW]3′)Reverse (5′[RIGHTWARDS ARROW]3′)
  • Primer set designed on the bottom strand; M, specific to methylated DNA; U, specific to unmethylated DNA.

CDKN2AMTTGGTAGTTAGGAAGGTTGTATCGCTCCCTACTCCCAACCGCG12666
UGGTAGTTAGGAAGGTTGTATTGTTCCCTACTCCCAACCACA12460
hMLH1MCGTTAAGTATTTTTTTCGTTTTGCTCCGCTCTTCCTATTAATTCG13659
UAGTGTTAAGTATTTTTTTTGTTTTGTCTATCCACTCTTCCTATTAATTCA14156
LOXMATAAATAGTTGAGGGGCGGTCCGACAATCCCGAAAAACG12061
UATAAATAGTTGAGGGGTGGTTACAACAATCCCAAAAAACA12158
FLNcMGAGAGAGAGTTAGAGAGCGGTCGAGCGACCACGAAACTCGCTACGCTACG12170
UGAGAGAGAGTTAGAGAGTGGTTGAGTAACCACAAAACTCACTACACTACA12163
HRASLSMGTGTATTTATGATGGGTGTATTCACCAAACGCTATACTAAACG 8859
UGTATTTATGATGGGTGTATTTCACCAAACACTATACTAAACA 8757
HAND1MATAGTTTAGGGCGTTGGTCCTACTCTACGAACTTAAAAAAACG10057
UAATAGTTTAGGGTGTTGGTTCTACTCTACAAACTTAAAAAAACA10155
THBDMCGTTCGTTTTTATTCGGCGTCGCCAAACCCCATCTCATCG11860
UATGTGTTTGTTTTTATTTGGTGTTCAAACCCCATCTCATCAAA11956
F2RMTTAGGAGGGTCGAGACGGTCGCTCCTCTAAACACCGTTAATTCG11361
UTTTTAGGAGGGTTGAGATGGTTGTTTCCTCTAAACACCATTAATTCACA11661
NT5EMAGTCGATAGTCGCGTTAGGGTCGAACAACTAAAACCGAAACTCG16955
UTAGTTGATAGTTGTGTTAGGGTTAACTAAAACCAAAACTCAATACC16657
GREM1MCGTCGGTATTTAAACGGGAGACGAAACTCGACGCGAAATCAACG12159
UTGTTGGTATTTAAATGGGAGATCAAAACTCAACACAAAATCAACA12257
ZNF177MGTAGGAGTATTTGCGATGTTTCAAAATAACGAAACGACGAACG12863
UGTTTTTAAGTTTTTAGGGTGAATTTAAACAACAAACACCCACTTCCA 9756
CLDN3MAGGTTTTGGAGAGCGCGGTTTCACCCTAAACTAAAACCGATACG 8659
UGGTGGTAGGGGTGGAGTTGTCCTACCCCAACATTATAAACCACA12564
PAX6MCGGGATTTATCGGCGGAGTCAACCTCGCGCCAACCG10463
UGTAATATTTTGTGTGAGAGTGAGTTCCTCCTACACCTAAACCAAAACA11561
CTSLMGATTTTATTTTGCGTCGTTTCACGCTACGATTAACTATACCG16359
UGTTTGATTTTATTTTGTGTTGTTTTACTACACTACAATTAACTATACCA17059

For bisulfite sequencing of LOX, 1 µL of the sodium bisulfite-treated DNA was amplified using 5′-AAG TTA GTG TGT TTT AGG ATG TGT GT-3′ and 5′-CTT CCC TTT CCC CTT TCT CAA T-3′,(25) which were common to the methylated and unmethylated DNA sequences. PCR products were cloned into a pGEM-T Easy vector, and 10 clones or more were cycle sequenced for each sample.

Calculation of deviation values and statistical analysis.  For each gene, the deviation value of a case was calculated as:

  • methylation level of the case (%) –  mean methylation level (%)/SD × 10 + 50.

The ‘average of the deviation values’ of a case was calculated as the average of the deviation values of the 12 genes in the case.

Pairwise differences in age were analyzed using Student's t-test with Bonferroni's correction, and an overall difference in age was analyzed using the Kruskal–Wallis test. Pairwise and overall differences in sex, histology, lymph-node metastasis, H. pylori-infection status, and EBV-infection status were analyzed using Fisher's exact test. Correlations between the number of methylated genes in cancers and methylation levels (mean deviation values) were analyzed using Pearson's correlation coefficient. Patient survival was calculated from the date of surgery until the date of death or the last follow up. Survival curves were analyzed using the Kaplan–Meier method and differences in the survival rates were evaluated using the log-rank test. All tests were two-sided, and a P-value of less than 0.05 was considered statistically significant. All of the analyses were carried out using SPSS (SPSS, Chicago, IL, USA).

Results

  1. Top of page
  2. Abstract
  3. Materials and Methods
  4. Results
  5. Discussion
  6. Acknowledgments
  7. References

Methylation quantification in cancers and background non-cancerous mucosae.  Methylation levels of the promoter CGI were analyzed quantitatively for the 14 promoter CGI in 66 gastric cancers and their background non-cancerous gastric mucosae (Fig. 1), and in 19 normal gastric mucosae of H. pylori-negative healthy volunteers. Methylation levels showed different patterns of distribution between cancers and their background non-cancerous mucosae. In cancers, 15–60 (23–90%) of the 66 samples had a methylation level of 0%, whereas the others had methylation levels ranging up to 85%. This indicated that cancer samples could be classified into those without methylation (methylation level = 0%) and those with methylation. In contrast, the methylation levels in the background non-cancerous mucosae followed a unimodal distribution, particularly for LOX, FLNc, HAND1, THBD, F2R, and ZNF177. Notably, methylation of the hMLH1 tumor-suppressor gene was not detected at all in the non-cancerous mucosae.

image

Figure 1. Methylation levels of the 14 promoter CpG islands (CGI) in 66 gastric cancers and their background non-cancerous gastric mucosae. Methylation levels were analyzed by the quantitative methylation-specific polymerase chain reaction method. In the cancers, the methylation levels were classified as absent (methylation level = 0%) or positive. In the background non-cancerous mucosae, methylation levels showed unimodal distributions, especially for LOX, FLNc, HAND1, THBD, F2R, and ZNF177. Very low levels of and no methylation were detected for CDKN2A (except for one case with 7.1% methylation) and hMLH1, respectively.

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These data supported the suggestion that methylation levels should be interpreted differently in cancers and in non-cancerous tissues, reflecting their monoclonal and polyclonal natures, respectively. When a cancer has methylation of a gene, all of the cancer cells have the methylation, and its methylation level should approximate the fraction of cancer cells in a sample. When a cancer does not have methylation of a gene, its methylation level should be 0%. However, a sample of background non-cancerous mucosae contains many glands, each of which is expected to have a different methylation status. Because different genes had different susceptibilities to methylation, deviation values were used to calculate mean values among different genes.

The clonality in cancers and non-cancerous mucosae was confirmed by bisulfite sequencing of a representative gene, LOX (Suppl. Fig. 1). A gastric cancer sample (case 2) had three patterns of DNA methylation among 12 methylated DNA molecules, whereas a non-cancerous gastric mucosa (case 11) had very variable patterns (eight patterns among eight methylated DNA molecules). This finding supported the monoclonal origin of the cancer cells and polyclonal origins of methylated DNA molecules in non-cancerous samples. A similar tendency was confirmed by bisulfite sequencing of three additional samples.

Analysis of CIMP presence in gastric cancers.  Based on the findings in relation to methylation levels described above, a gene in a cancer sample was scored as methylated or unmethylated. To set a cut-off value based on the fraction of cancer cells in the samples, the methylation levels of the two tumor-suppressor genes CDKN2A and hMLH1 (which were most unlikely to be methylated in a subpopulation of cancer cells) were referred to (Suppl. Fig. 2). Most cancers did not have methylation of CDKN2A or hMLH1. Among the samples with positive methylation, the lowest methylation levels were 6.8% for CDKN2A and 7.0% for hMLH1, excluding the samples with extremely low methylation levels of CDKN2A. Therefore, we adopted a cut-off value of 6%.

image

Figure 2. Distribution of gastric cancers by number of methylated genes. Analyses (a) including and (b) excluding the two tumor-suppressor genes were carried out. In both analyses, the number of methylated genes did not show a bimodal distribution, suggesting that multiple entities underlie the CpG island methylator phenotype in gastric cancers.

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The number of genes methylated in a cancer was examined in two ways, including and excluding the two tumor-suppressor genes. In both cases a continuous distribution was demonstrated, with 15 cancers having no methylation (Fig. 2). The absence of a bimodal distribution was in contrast to the case of colorectal cancers,(8,10) but was in accordance with previous reports in gastric cancers.(15–19) To carry out unbiased association analysis with clinicopathological features, cancers were classified into tertiles, using a cut-off of methylation of five genes. Cancers with methylation of five genes or more, those with one to four genes, and those with no methylation were designated as CIMP high (cases 1–21), CIMP low (cases 22–51), and CIMP negative (cases 52–66), respectively.

Relationship between CIMP in cancers and methylation in background mucosae.  To examine the relationship between CIMP in gastric cancers and methylation levels in the background non-cancerous gastric mucosae, all of the samples were sorted according to the number of genes methylated in the cancers, and then according to the average of the deviation values of the 12 genes in the background non-cancerous mucosae (Fig. 3). The methylation levels in the non-cancerous mucosae of cancer patients (except for cases 2, 10, 21, 51, and 66) were higher than normal gastric mucosae of the H. pylori-negative healthy volunteers, which was in accordance with our previous findings.(6)

image

Figure 3. Relationship between CpG island (CGI) methylator phenotype (CIMP) status in gastric cancers and methylation levels in their background non-cancerous gastric mucosae. The cancer samples (right half) were scored as positive or negative (shown by the black and white boxes, respectively), and the background non-cancerous mucosae (left half) were analyzed by the deviation values of methylation levels (black, deviation value ≥ 65; dark gray, 55 ≤ deviation value < 65; light gray, 45 ≤ deviation value < 55; and white, deviation value < 45 or methylation level < 0.5%). The 66 samples were first sorted by number of methylated CGI, and then by mean deviation value of methylation in the non-cancerous mucosae. Cancers with methylation of five genes or more, those with one to four genes, and those with no methylation were designated as CIMP high (cases 1–21), CIMP low (cases 22–51), and CIMP negative (cases 52–66), respectively. No correlation was observed between CIMP in cancers and methylation levels in non-cancerous mucosae. Methylation levels in the non-cancerous mucosae were higher than those of normal mucosae of Helicobacter pylori-negative healthy volunteers. The presence of Epstein–Barr virus and H. pylori is indicated by a plus (+) sign.

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For more detailed analysis, the methylation levels in the background non-cancerous mucosae were compared among the CIMP-high, CIMP-low, and CIMP-negative cancers. However, no significant differences were observed for methylation levels of the 12 genes (Fig. 4a), or for the average of the deviation values of the 12 genes (49.9 ± 7.9, 50.3 ± 5.1, 49.5 ± 3.9, respectively; mean ± SD, P = 0.650). When the number of genes methylated in cancers and the mean deviation value in the background mucosae were analyzed, again no correlation was observed (Fig. 4b; r = –0.036, P = 0.774).

image

Figure 4. Detailed analysis of the correlation between CpG island (CGI) methylator phenotype (CIMP) status in gastric cancers and methylation levels in non-cancerous gastric mucosae. (a) Methylation levels of individual genes in non-cancerous mucosae in CIMP-negative (N), CIMP-low (L), and CIMP-high (H) gastric cancers. No significant differences were observed in the mean methylation levels among the three gastric cancer groups. Error bars: standard errors (SE). (b) Mean deviation values of the 12 genes according to the number of CGI methylated in gastric cancers. No correlation was observed (Pearson's correlation coefficient; r = –0.036, P = 0.774).

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Clinicopathological features of CIMP in gastric cancers.  Associations between CIMP status (CIMP high, CIMP low, and CIMP negative) and clinicopathological characteristics were analyzed. There were no differences in sex, lymph-node metastasis, and H. pylori-infection status among the three groups (Table 2). The CIMP-negative group was younger than the CIMP-low group (P = 0.056). The CIMP-high group had a higher incidence of diffuse-type gastric cancers than the CIMP-low group (P = 0.048). EBV-associated gastric cancers mostly displayed CIMP-high status. By Kaplan–Meier survival analysis, the CIMP-high group showed significantly better survival than the CIMP-negative group (P = 0.026), and better survival than the CIMP-low group (P = 0.088) (Fig. 5).

Table 2. Clinicopathological features of the CpG island methylator phenotype (CIMP)-high, CIMP-low and CIMP-negative cancers
FactorCIMP high (n = 21)CIMP low (n = 30)CIMP negative (n = 15)CIMP-high versus CIMP-low P-valueCIMP-high versus CIMP-negative P-valueCIMP-low versus CIMP-negative P-valueOverall P-value
  1. Mean ± SD is shown for age, and number of cases is shown for all other parameters.

Age (years)61.1 ± 11.165.0 ± 9.057.0 ± 12.50.6120.7290.0560.092
Sex (male/female)14/723/711/40.5290.7291.0000.726
Histology (intestinal/diffuse)2/1911/194/110.0480.2140.7380.072
Lymph-node metastasis (positive/negative)18/325/512/31.0000.6771.0000.916
Helicobacter pylori infection (positive/negative)18/329/113/20.2931.0000.2540.341
Epstein–Barr virus infection (positive/negative)8/131/290/150.0020.0111.0000.000
image

Figure 5. Kaplan–Meier survival analysis of gastric cancer patients according to CpG island methylator phenotype (CIMP) status. The CIMP-high patients tended to show better survival than the CIMP-low patients (P = 0.088). The CIMP-high patients showed significantly better survival than the CIMP-negative patients (P = 0.026).

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Finally, the effect of EBV infection on the methylation levels of the background non-cancerous mucosae was analyzed using the average of the deviation values of the 12 genes. Its mean ± SD was 49.5 ± 4.2 and 50.7 ± 9.6 in the CIMP-high groups with and without EBV infection, respectively, and no significant difference was observed (P = 0.875). Some cases with EBV infection showed very low methylation levels in the background mucosae (cases 2 and 15; Fig. 3).

Discussion

  1. Top of page
  2. Abstract
  3. Materials and Methods
  4. Results
  5. Discussion
  6. Acknowledgments
  7. References

The present study is the first report of concurrent analysis of the methylation levels both in gastric cancers and in their background non-cancerous gastric mucosae. We used quantitative methylation-specific PCR, which is very accurate and can analyze any CpG sites,(6,32) to measure the methylation levels of 14 promoter CGI, consisting of 12 marker genes and two tumor-suppressor genes, in 66 matched cancers and non-cancerous mucosae. As expected from their monoclonal and polyclonal origins, methylation levels in cancers and non-cancerous mucosae showed entirely different patterns. Therefore, methylation in cancers was assessed as positive or negative, and that in non-cancerous mucosae was assessed by its degree using the methylation levels or the deviation values.

The number of methylated genes in a cancer did not show a bimodal distribution, suggesting that CIMP in gastric cancers consists of multiple entities or even does not exist. To classify gastric cancers without a bias, we chose to put them into tertiles. The first group was CIMP negative (15 cancers without methylation), and the remaining 51 cancers were classified as 30 CIMP-low and 21 CIMP-high cancers with a cut-off of five genes methylated. The fraction of CIMP-high gastric cancers (32%) in this classification was similar to those (24–41%) in previous studies.(15–20) Eight of the nine EBV-associated gastric cancers belonged to the CIMP-high cancers,(19–22) and CIMP-high cancer patients tended to have a better prognosis than CIMP-low and CIMP-negative patients, also as reported.(17,19,20) These findings suggested that, although CIMP consists of multiple entities, CIMP itself does exist in gastric cancers. Even when using a different cut-off value (four genes), the above findings did not change (data not shown).

Initially, we expected that gastric cancers arising from a background gastric mucosa with high methylation levels would display a CIMP-high status. However, contrary to our expectation, the CIMP status in cancers did not correlate with the methylation levels in non-cancerous mucosae. This apparent discrepancy can be explained in two ways. First, most of methylation present in gastric cancers could have been induced after a cancer cell was produced. This idea is supported by the fact that some CIMP-high gastric cancers had unique clinicopathological characteristics, and by our previous finding that some gastric cancer cell lines appeared to have an intrinsic abnormality that increased the rate of methylation events.(33,34) Second, methylation levels in the entire gastric mucosae might not reflect methylation levels in the precursor cells for gastric cancers. Although the precise origin of gastric cancer cells has not been clarified, the number of stem and progenitor cells in a gastric gland is known to be relatively small.(35)

We adopted a cut-off value of 6% to score cancer samples with positive methylation. This value was based on the methylation levels of CDKN2A and hMLH1, which were considered to reflect the fraction of cancer cell-derived DNA in the samples. Although some cancers showed methylation levels between 1 and 3%, most methylation-positive cancers had methylation levels higher than 6%. Methylation levels of 1–3% in cancers was considered to be present in a subpopulation of cancer cells, as histological analysis of gastric cancers did not support such a small fraction of cancer cells occurring in a tissue sample. The 6% methylation cut-off value used in the present study is comparable with previous reports using MethyLight technology, in which a cut-off value of 4% was used to best discriminate between normal and malignant tissues.(36,37)

Among the 12 marker genes used in the present study, five genes (LOX, HRASLS, FLNc, HAND1, and THBD) were identified by methylation-sensitive representational difference analysis as methylated in gastric cancers, and were used to analyze gastric cancers for their CIMP statuses in previous studies.(16,19) The remaining seven genes were identified by treating a gastric cancer cell line with a demethylating agent, 5-aza-2′ deoxycytidine, and screening using an oligonucleotide microarray.(26) Because these 12 genes were methylated not only in cancers but also in non-cancerous gastric mucosae, we used these genes as marker genes. Although LOX has tumor-suppressive activity in gastric cancers,(25) the remaining 11 genes were unlikely to have such activity and were considered suitable to analyze the effects of factors that induce aberrant DNA methylation in an unbiased manner.

We recently showed that H. pylori infection, a potent gastric carcinogenic factor, induces methylation of specific genes in non-cancerous gastric mucosae,(6) and methylation levels increase in the order of healthy volunteers, cases with a single gastric cancer, and cases with multiple gastric cancers.(7) The high methylation levels in the non-cancerous mucosae of cancer cases observed here were considered to reflect their current or past exposure to H. pylori, and to be associated with the methylation of tumor-suppressor genes. Because the methylation levels of tumor-suppressor genes are very low in non-cancerous gastric mucosae, as observed for CDKN2A and hMLH1 here, currently their accurate measurement is technically very difficult and the use of marker genes has value.

In conclusion, this is the first study that has revealed no correlation between CIMP status in gastric cancers and methylation levels in their background non-cancerous gastric mucosae.

Acknowledgments

  1. Top of page
  2. Abstract
  3. Materials and Methods
  4. Results
  5. Discussion
  6. Acknowledgments
  7. References

We thank Dr Takashi Sugimura for his advice and critical reading of the manuscript. S. E., T. N., and K. N. are recipients of Research Resident Fellowships from the Foundation for Promotion of Cancer Research. This study was supported by Grants-in-Aid for the Third-Term Comprehensive Cancer Control Strategy from the Ministry of Health, Labour and Welfare, and from the Ministry of Education, Science, Culture, and Sport, Japan.

References

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  2. Abstract
  3. Materials and Methods
  4. Results
  5. Discussion
  6. Acknowledgments
  7. References
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