SEARCH

SEARCH BY CITATION

Keywords:

  • DNA methylation;
  • epigenetic;
  • fingerprint;
  • Helicobacter pylori;
  • molecular epidemiology

Abstract

  1. Top of page
  2. Abstract
  3. Material and methods
  4. Results
  5. Discussion
  6. References
  7. Supporting Information

Aberrant DNA methylation is deeply involved in human cancers, but its inducers and targets are still mostly unclear. Helicobacter pylori infection was recently shown to induce aberrant methylation in gastric mucosae, and produce a predisposed field for cancerization. Here, we analyzed the presence of target genes in methylation induction by H. pylori and the mechanism for the gene specificity. Noncancerous gastric mucosae were collected from 4 groups of individuals (with and without a gastric cancer, and with and without current H. pylori infection; N = 11 for each group), and methylation of promoter CpG islands of 48 genes that can be methylated in gastric cancer cell lines was analyzed by methylation-specific PCR. In total, 26 genes were consistently methylated in individuals with current or past infection by H. pylori, whereas 7 genes were not methylated at all. In addition, 14 genes were randomly or intermediately methylated in individuals with gastric cancers and the remaining 1 gene was methylated in all the cases. The methylation-susceptible genes had significantly lower mRNA expression levels than the methylation-resistant genes. H. pylori infection did not induce mRNA and protein expression of DNA methyltransferases; DNMT1, DNMT3A or DNMT3B. Gene specificity was present in the induction of aberrant DNA methylation by H. pylori infection, and low mRNA expression, which could precede methylation, was one of the mechanisms for the gene specificity. These findings open up the possibility that a methylation fingerprint can be used as a novel marker for past exposure to a specific carcinogenic factor. © 2008 Wiley-Liss, Inc.

Aberrant DNA methylation is deeply involved in human cancer development and progression.1 In some cancer types, such as gastric cancers, tumor-suppressor genes are more frequently inactivated by aberrant DNA methylation than by mutations.2 Nevertheless, only limited information is available for inducers of aberrant DNA methylation, which include aging, viral infection and ulcerative colitis.3, 4 Also, almost no information is available for gene specificity in methylation induction by a specific factor. Using cancer tissues, it is very difficult to clarify an association between a specific inducer and methylation of a gene. Aberrant methylation of a gene can be present in cancer tissues because its methylation conferred a growth advantage although it was a rare and random event, or because its methylation was carried over from a precursor tissue to a cancer tissue since it was frequently induced in the precursor tissue. In contrast, using a noncancerous tissue, one can assess an effect of a methylation inducer by the fraction of cells with methylation in the polyclonal tissue.

Gastric mucosa infected by Helicobacter pylori is a useful model to examine the possible presence of gene specificity in methylation induction by a specific factor since H. pylori infection was recently shown to induce aberrant DNA methylation potently in gastric mucosae.5 Moreover, the fraction of DNA molecules with aberrant methylation (methylation level) in gastric mucosae of individuals without current H. pylori infection was correlated with gastric cancer risk,5, 6 indicating that methylation in noncancerous tissues is related to gastric carcinogenesis. So far, 6 CpG islands in gene promoter regions methylated in gastric cancers7 were analyzed, and all were methylated in gastric mucosae with current and past infection with H. pylori. However, it is unknown whether these 6 genes are preferentially methylated by H. pylori infection or H. pylori infection induces methylation of random genes.

In this study to analyze the presence of gene specificity for methylation induction, firstly we examined the methylation status of 48 promoter CpG islands in the noncancerous gastric mucosae of 4 groups of individuals (with and without a gastric cancer, and with and without current H. pylori infection). The 48 genes were selected as genes that can be methylation-silenced in gastric cancer cell lines8 because the vast majority of CpG islands in gene promoter regions are not methylated at all in noncancerous tissues, and we had to newly select genes that have better chances to be methylated in noncancerous tissues. Secondly, we analyzed an association between susceptibility to methylation induction and mRNA expression levels in normal tissue without and with H. pylori infection.

Material and methods

  1. Top of page
  2. Abstract
  3. Material and methods
  4. Results
  5. Discussion
  6. References
  7. Supporting Information

Tissue samples and DNA/RNA extraction

For methylation analysis, (noncancerous) gastric mucosa samples were collected from 4 groups of individuals (with and without a gastric cancer, and with and without current H. pylori infection; N = 11 for each group, average age = 60.8 ± 13.8 years). For analysis of mRNA expression that determines gene specificity of methylation induction, we need to analyze the mRNA expression level in gastric mucosae free of methylation, which, once induced, will cause decreased gene transcription to avoid confusion between cause and consequence. Therefore, samples were collected from 11 healthy volunteers, who were considered to have less chance for methylation induction by H. pylori than elderly individuals (7 males and 4 females; 6 with H. pylori infection and 5 without; average age = 34.8 ± 3.1 years). Biopsy specimens were taken from one standard site of the stomach (antral regions in the lesser curvature) using sterilized biopsy forceps (Olympus, Tokyo, Japan). H. pylori infection status was analyzed by culture test (Eiken, Tokyo, Japan) and rapid urease test (Otsuka, Tokushima, Japan). All the materials were obtained with written informed consents, and the procedures were approved by the institutional review board. High molecular weight DNA was extracted by the standard phenol/chloroform method and total RNA was isolated using ISOGEN (Nippon Gene, Tokyo, Japan) and an RNeasy Mini kit (Qiagen, Valencia, CA).

Cell lines and 5-aza-dC treatment

Gastric cancer cell lines, AGS and KATO-III, were obtained from the Japanese Collection of Research Bioresources (Tokyo, Japan) and the American Type Culture Collection (Manassas, VA). For treatment with a demethylating agent, 5-aza-2′-deoxycitidine (5-aza-dC, Sigma, St. Louis, MO), cells were seeded on day 0, media containing 0.3 μM 5-aza-dC was freshly added on days 1 and 3, and cells were harvested on day 5. Genomic DNA and total RNA were isolated in the same way as the primary samples.

Bisulfite treatment and methylation-specific PCR

Bisulfite treatment was performed as previously described.9 Briefly, DNA samples (1 μg each) digested by BamHI were denatured in 0.3 N NaOH at 37°C for 15 min. The samples underwent 15 cycles of 30-sec denaturation at 95°C and 15-min incubation at 50°C in 3.1 N sodium bisulfite (pH 5.0) and 0.5 mM hydroquinone. The samples were desalted with the Wizard DNA Clean-Up system (Promega, Madison, WI), and desulfonated in 0.3 N NaOH. DNA was ethanol precipitated and dissolved in 40 μL of TE buffer.

Methylation-specific PCR (MSP) was performed with a primer set specific to the methylated or unmethylated sequence (M or U set), respectively,8 using 2 μL of the sodium bisulfite-treated DNA. A region upstream of a putative transcriptional start site (200 bp or less) was analyzed, and CpG maps of all the genes are shown in the Supporting Information Figure 1. DNA methylated with SssI methylase was used to determine a specific condition of PCR for the M set, and DNA amplified by a GenomiPhi DNA amplification kit (GE Healthcare Bio-Sciences) was used for the U set. A number of PCR cycles that would yield a minimal visible band was determined using these fully methylated DNA (for M primers) and fully unmethylated DNA (for U primers), and a further 4 cycles were added for actual analysis of test samples. Methylation levels were classified as none (−), low (+), high (++) according to the intensity of the band for methylated DNA molecules compared with that for unmethylated DNA, respectively.

Quantitative reverse transcription PCR

cDNA was synthesized from 1 μg of total RNA using a Superscript II kit (Life Technologies, Rockville, MD) with a random primer. Real-time PCR was performed using an iCycler Thermal Cycler (Bio-Rad Laboratories, Hercules, CA) with SYBR Green I (BioWhittaker Molecular Applications, Rockland, ME). The number of molecules of a specific gene in a sample was measured by comparing its amplification with that of standard samples, which contained 101–107 copies of the gene. The standard samples were produced by PCR amplification and purification using Zymo-Spin I™ Columns (Zymo Research, Orange, CA). The amount of the standard samples was measured by OD 260 nm and also by quantification of band intensities after electrophoresis. The mRNA quantity of each gene was normalized to that of β-actin. The primers and PCR conditions are shown in the Supporting Information Table 1. The difference of mRNA expression levels between 2 groups of genes was analyzed by the Welch t-test method (both sided).

Western blot analysis

Each 100 μg whole-cell lysate sample was subjected to SDS-PAGE (10% acrylamide gel) and blotted to PVDF membrane. DNMT1 and DNMT3A were detected using rabbit polyclonal antibody against human DNMT1 (NEB, Beverly, MA), human DNMT3A (Cell Signaling Technology, Danvers, MA), respectively at 1/1,000 dilution. DNMT3B was detected using goat polyclonal antibody against human DNMT3B (Santa Cruz Biotechnology, Santa Cruz, CA) at 1/500 dilution. Horse radish peroxidase-conjugated secondary antibody (antirabbit; Cell Signaling Technology, antigoat; Santa Cruz Biotechnology) was used at 1/5,000 dilution.

Results

  1. Top of page
  2. Abstract
  3. Material and methods
  4. Results
  5. Discussion
  6. References
  7. Supporting Information

Confirmation of gene silencing due to promoter CpG islands

The 48 genes consisted of 32 randomly and 16 arbitrarily selected genes from 421 genes that had been identified as methylation-silenced genes in a gastric cancer cell line using microarray analysis of cells treated with 5-aza-dC, and MSP analysis.8 First, we analyzed mRNA expression of 7 of the 48 genes before and after 5-aza-dC treatment using real-time RT-PCR (Fig. 1). It was confirmed that no or little mRNA expression was present in cell lines without unmethylated DNA molecules and that mRNA expression was upregulated by the 5-aza-dC treatment.

thumbnail image

Figure 1. Gene silencing due to methylation of the regions analyzed. mRNA expression and methylation were analyzed by real-time RT-PCR and MSP, respectively, in gastric cancer cell lines (AGS and KATO-III) before and after 5-aza-dC treatment. The fold increases after 5-aza-dC treatment is shown for each cell line. No or little mRNA expression in a cell line(s) without unmethylated DNA molecules and upregulation by the 5-aza-dC treatment was confirmed for the 7 genes randomly selected from the 48 genes.

Download figure to PowerPoint

Gene specificity in methylation induction by H. pylori infection in gastric mucosae

We then analyzed the methylation status of the promoter CpG islands of the 48 genes in the (noncancerous) gastric mucosae of 4 groups of individuals; those with and without H. pylori infection and with and without a gastric cancer. Since MSP can produce inconsistent results if inappropriately performed, we carefully selected a PCR cycle for each primer set so that false positive and negative results were not produced. We scored the methylation status as negative, weakly positive or positive by comparing the band density with that of a fully methylated control (representative results in Fig. 2a).

thumbnail image

Figure 2. Methylation profile of the 48 genes in noncancerous gastric mucosae. (a) Representative results of MSP. Samples 1–11, gastric mucosae of healthy individuals without H. pylori infection; 12–22, those with H. pylori infection; 23–33, noncancerous gastric mucosae of gastric cancer cases without H. pylori infection; and 34–44, those with H. pylori infection. Methylation levels were classified as none (−), low (+), high (++) according to the intensity of the band for methylated DNA molecules compared with that of fully methylated control DNA. (b) Overview of the results of all the 48 genes. The genes were aligned in the order of increasing numbers of individuals with methylation. Closed, hatched, and open boxes represent the methylation levels of high (++), low (+), and none (−), respectively. Rows 1–7, the 7 genes completely resistant to methylation induction in any cases; rows 8–21, genes methylated randomly or more frequently in individuals with cancers; and rows 22–47, genes susceptible to methylation induction by H. pylori infection. CpG island configuration (number of CpG sites, G+C content, and CpG score) in 300 bp upstream regions from transcription start sites is also shown. The presence of methylation-resistant and methylation-susceptible genes was clearly revealed. No clear difference in the CpG island configuration was observed between the 2 groups.

Download figure to PowerPoint

When all the genes were aligned in the order of number of samples with methylation (Fig. 2b), the 48 genes were divided into 3 groups: (i) 7 genes that were completely unmethylated in any of the 4 groups (genes 1–7 in Fig. 2b; methylation-resistant genes), (ii) 14 genes that were methylated randomly or more frequently in individuals with cancers (genes 9–21; intermediate genes); and (iii) 26 genes that were consistently methylated in the individuals with H. pylori infection or with a gastric cancer (genes 22–47; methylation-susceptible genes). The remaining 1 gene, PLAGL1, was methylated in all the individuals. This demonstrated that some genes are resistant to methylation induction by H. pylori infection while others are susceptible, namely the presence of gene specificity in methylation induction.

Lack of association between CpG island configuration and methylation susceptibility

The 48 genes analyzed here all had CpG islands in their promoter regions. However, based on recent reports,10 there was a possibility that, even among CpG islands, their configurations (number of CpG sites, G+C content, and CpG score) might influence the susceptibility of individual genes to methylation induction by H. pylori. Therefore, we examined their configurations in 300 bp upstream regions from transcription start sites (Fig. 2b), which corresponded to the nucleosome-free region and whose methylation is critical for gene silencing.11, 12

The number of CpG sites in the region was 29.2 ± 10.4 (mean ± standard deviation) and 25.4 ± 9.3 for the susceptible and resistant genes, respectively (p = 0.38). The G + C content was 68.4 ± 7.4 and 66.4 ± 7.9% for the susceptible and resistant genes, respectively (p = 0.52). The CpG score was 0.82 ± 0.18 and 0.75 ± 0.21 for the susceptible and resistant genes, respectively (p = 0.40). In short, no significant difference was present between the 2 groups.

Involvement of low mRNA expression levels in gene specificity in methylation induction

To investigate an association between the gene specificity in methylation induction and mRNA expression levels in gastric mucosae, we analyzed mRNA expression levels of all of the 7 methylation-resistant and 15 methylation-susceptible genes, which were randomly selected from the 26 methylation-susceptible genes. To compare mRNA expression levels among different genes, the numbers of cDNA molecules were measured by quantitative RT-PCR after accurate measurement of the weights (converted into the numbers of DNA molecules) of standard DNA samples of all the genes. mRNA expression levels were analyzed in the gastric mucosae of young healthy individuals with and without H. pylori infection, who were considered to have no or little methylation of the genes analyzed.

The average mRNA expression level of methylation-resistant genes was much higher than that of methylation-susceptible genes among individuals without H. pylori infection (4.3 × 10−2vs. 7.3 × 10−3; p = 0.0008) and also among individuals with H. pylori infection (2.9 × 10−2vs. 5.1 × 10−3; p = 0.0012) (Fig. 3). Three of the 7 resistant genes and 5 of the 15 susceptible genes showed a significant decrease of mRNA expression levels by H. pylori infection, but no genes showed significantly increased mRNA expression.

thumbnail image

Figure 3. The mRNA expression levels of genes resistant and susceptible to methylation induction. mRNA expression levels of 22 genes (7 resistant and 15 susceptible genes) in the noncancerous gastric mucosae of young healthy individuals with (closed columns) and without (open columns) H. pylori infection was analyzed by real-time RT-PCR. Error bar: standard deviation. The average mRNA expression level of methylation-resistant genes was much higher than that of methylation-susceptible genes among individuals without H. pylori infection (4.3 × 10−2vs. 7.3 × 10−3; p = 0.0008) and also among individuals with H. pylori infection (2.9 × 10−2vs. 5.1 × 10−3; p = 0.0012). The genes whose names are boxed showed a significant decrease in their mRNA expression levels by H. pylori infection (p < 0.05). Considering that all these 48 genes are those that can be methylated in gastric cancer cell lines, downregulation of mRNA expression could be involved in methylation induction.

Download figure to PowerPoint

Expression levels of DNA methyltransferase

To gain an insight into how H. pylori infection induces aberrant methylation, we analyzed mRNA expression levels of maintenance DNA methyltransferase, DNMT1, and de novo methyltransferases, DNMT3A and DNMT3B, in the gastric mucosae with and without H. pylori infection. However, no significant increase in their mRNA expression levels was observed (Fig. 4a). Further, at the protein level, expression levels of DNMT1, DNMT3A and DNMT3B were below the detection limit even in the gastric mucosae with H. pylori (Fig. 4b), indicating no increase was induced by H. pylori infection.

thumbnail image

Figure 4. The mRNA and protein expression levels of three DNA methyltransferases (DNMT1, DNMT3A and DNMT3B) in noncancerous gastric mucosae of young healthy individuals with and without H. pylori infection. (a) mRNA expression levels of DMNTs. Closed columns, individuals with H. pylori infection; open columns, those without. No significant increase was observed in the mRNA expression levels of these DNMTs. (b) Western blot analysis of DNMTs. For DNMT1 and DNMT3A, a stomach cancer cell line, KATO-III was used as a positive control (lane 12), and 5-aza-dC (1 μM)-treated KATO-III was used as a negative control (lane 11). ACTIN was used as a loading control. For DNMT3B, a commercially available positive control of DNMT3B (Santa Cruz, lane 13) was used. DNMT protein levels were below the detection limit in the noncancerous gastric mucosae of individuals without (lanes 1–5) and with (lanes 6–10) H. pylori infection, and no detectable increase was observed.

Download figure to PowerPoint

Discussion

  1. Top of page
  2. Abstract
  3. Material and methods
  4. Results
  5. Discussion
  6. References
  7. Supporting Information

The presence of gene specificity for aberrant DNA methylation induction by a specific carcinogenic factor was demonstrated for the first time in this study. Also, genes susceptible to methylation had significantly lower mRNA expression levels than resistant genes. For clarification of the relationship between a methylation-inducing factor and gene specificity, use of noncancerous gastric tissue, which is polyclonal, was important because gene silencing due to promoter methylation can result in over- or under-presence of methylation in cancer tissues. Methylation in noncancerous tissues is also reported in the colonic mucosae of patients with ulcerative colitis13, 14 and liver tissues of patients with hepatocellular carcinomas,15 but limited numbers of genes have been analyzed so far.

Methylation of specific genes can persist for a lifetime, and there is a possibility that the methylation profile can be used as a methylation fingerprint of H. pylori infection in the past, as specific p53 and APC mutations are used to assess past exposure to specific carcinogens.16, 17 Use of DNA methylation has an advantage over mutations because methylation can be present in a significant fraction of cells in noncancerous tissues, and can be detected sensitively and reproducibly. The noncancerous gastric mucosae of cases with a gastric cancer without current H. pylori infection, most of which are considered to have had past exposure to H. pylori,18 showed the same methylation profile as individuals with current H. pylori infection. This finding indicated that the methylation profile induced by H. pylori infection can persist even after discontinuation of H. pylori infection. Although eradication of H. pylori was reported to decrease incidences of individuals with methylation,19, 20 the decrease is only partial, not to zero, and highly variable among individuals (manuscript in preparation).

To establish a methylation profile as a fingerprint of H. pylori infection, the profile must be specific. Unfortunately, few gastric cancers can be considered as those induced solely by another carcinogenic factor, such as Epstein-Barr virus infection21 or high salt intake,22 and the specificity cannot be examined easily. However, since low mRNA expression levels are involved in gene specificity, there is a possibility that different carcinogenic factors induce different methylation profiles through induction of reduced mRNA expression of different genes. Once the specificity of a methylation profile is established, a methylation fingerprint will be very useful for clinicopathological analysis and epidemiology. Among the clinically used tests for H. pylori infection, the culture and rapid urease tests can detect only current H. pylori infection.23, 24 The serum antibody test remains positive in only half the patients as early as 1 year after successful eradication of H. pylori.25, 26

The role of low mRNA expression in methylation induction has been reported.4 De Smet et al. showed that weak transcriptional capacity leads to promoter remethylation by analysis of demethylation and mRNA expression of MAGE-A1 in various cell lines.27 Song et al. showed that decreased promoter activity leads to hypermethylation of a promoter CpG island of an exogenously introduced gene by disrupting its promoter activity.28 We and others previously observed that most genes methylated in cancer tissues had no or little expression in cancer precursor cells.29–32 This study showed that, in normal cells and in vivo, low mRNA expression is important for methylation induction. Also, it was suggested that downregulation by H. pylori infection precedes methylation since 8 of the 22 genes with expression analyses were downregulated by H. pylori infection, but none were upregulated. The 22 genes were selected from those that can be methylated in gastric cancer cell lines and even the resistant genes are considered to be relatively susceptible among the entire genes.

Even among the genes with similarly low mRNA expression levels, some genes were resistant and others were susceptible to methylation induction by H. pylori. As additional factors that determine the gene specificity of methylation induction, histone modification deregulation could be important. For example, a repressive histone modification, methylation at Lys27 of histone H3 (H3K27) induced by Polycomb group proteins, is associated with genes methylated in cancers.33, 34 Active chromatin marks, associated with active mRNA expression, could be important to protect DNA from methylation. At the same time, without H. pylori infection, even the susceptible genes were not methylated, indicating that abnormality in epigenetic regulation was induced by H. pylori infection. The final step of aberrant methylation must be mediated by DNA methyltransferases, and actually overexpression of de novo methyltransferases enhance methylation of specific genes in a mouse model.35 Also, some inflammatory cytokines, such as IL-6, are reported to induce DNA methyltransferases.36 However, contrary to initial expectations, H. pylori infection did not induce either mRNA or protein expression of DNMT1, DNMT3A and DNMT3B in gastric mucosae. Abnormalities in epigenetic regulation induced by H. pylori infection also need to be investigated.

In summary, methylation of specific genes was induced by H. pylori infection in noncancerous gastric membranes, and preceding low mRNA expression was suggested to be involved in the specificity. Use of the specific profile as a methylation fingerprint of past exposure to a specific carcinogenic factor was suggested.

References

  1. Top of page
  2. Abstract
  3. Material and methods
  4. Results
  5. Discussion
  6. References
  7. Supporting Information
  • 1
    Jones PA,Baylin SB. The epigenomics of cancer. Cell 2007; 128: 68392.
  • 2
    Ushijima T,Sasako M. Focus on gastric cancer. Cancer Cell 2004; 5: 1215.
  • 3
    Issa JP,Ottaviano YL,Celano P,Hamilton SR,Davidson NE,Baylin SB. Methylation of the oestrogen receptor CpG island links ageing and neoplasia in human colon. Nat Genet 1994; 7: 53640.
  • 4
    Ushijima T,Okochi-Takada E. Aberrant methylations in cancer cells: where do they come from? Cancer Sci 2005; 96: 20611.
  • 5
    Maekita T,Nakazawa K,Mihara M,Nakajima T,Yanaoka K,Iguchi M,Arii K,Kaneda A,Tsukamoto T,Tatematsu M,Tamura G,Saito D, et al. High levels of aberrant DNA methylation in Helicobacter pylori-infected gastric mucosae and its possible association with gastric cancer risk. Clin Cancer Res 2006; 12: 98995.
  • 6
    Nakajima T,Maekita T,Oda I,Gotoda T,Yamamoto S,Umemura S,Ichinose M,Sugimura T,Ushijima T,Saito D. Higher methylation levels in gastric mucosae significantly correlate with higher risk of gastric cancers. Cancer Epidemiol Biomarkers Prev 2006; 15: 231721.
  • 7
    Kaneda A,Kaminishi M,Yanagihara K,Sugimura T,Ushijima T. Identification of silencing of nine genes in human gastric cancers. Cancer Res 2002; 62: 664550.
  • 8
    Yamashita S,Tsujino Y,Moriguchi K,Tatematsu M,Ushijima T. Chemical genomic screening for methylation-silenced genes in gastric cancer cell lines using 5-aza-2′-deoxycytidine treatment and oligonucleotide microarray. Cancer Sci 2006; 97: 6471.
  • 9
    Kaneda A,Kaminishi M,Sugimura T,Ushijima T. Decreased expression of the seven ARP2/3 complex genes in human gastric cancers. Cancer Lett 2004; 212: 203210.
  • 10
    Weber M,Hellmann I,Stadler MB,Ramos L,Paabo S,Rebhan M,Schubeler D. Distribution, silencing potential and evolutionary impact of promoter DNA methylation in the human genome. Nat Genet 2007; 39: 45766.
  • 11
    Li B,Carey M,Workman JL. The role of chromatin during transcription. Cell 2007; 128: 70719.
  • 12
    Lin JC,Jeong S,Liang G,Takai D,Fatemi M,Tsai YC,Egger G,Gal-Yam EN,Jones PA. Role of nucleosomal occupancy in the epigenetic silencing of the MLH1 CpG island. Cancer Cell 2007; 12: 43244.
  • 13
    Hsieh CJ,Klump B,Holzmann K,Borchard F,Gregor M,Porschen R. Hypermethylation of the p16INK4a promoter in colectomy specimens of patients with long-standing and extensive ulcerative colitis. Cancer Res 1998; 58: 394245.
  • 14
    Issa JP,Ahuja N,Toyota M,Bronner MP,Brentnall TA. Accelerated age-related CpG island methylation in ulcerative colitis. Cancer Res 2001; 61: 357377.
  • 15
    Kondo Y,Kanai Y,Sakamoto M,Mizokami M,Ueda R,Hirohashi S. Genetic instability and aberrant DNA methylation in chronic hepatitis and cirrhosis—a comprehensive study of loss of heterozygosity and microsatellite instability at 39 loci and DNA hypermethylation on 8 CpG islands in microdissected specimens from patients with hepatocellular carcinoma. Hepatology 2000; 32: 9709.
  • 16
    Hussain SP,Harris CC. Molecular epidemiology and carcinogenesis: endogenous and exogenous carcinogens. Mutat Res 2000; 462: 31122.
  • 17
    Toyota M,Ushijima T,Kakiuchi H,Canzian F,Watanabe M,Imai K,Sugimura T,Nagao M. Genetic alterations in rat colon tumors induced by heterocyclic amines. Cancer 1996; 77: 159397.
  • 18
    Uemura N,Okamoto S,Yamamoto S,Matsumura N,Yamaguchi S,Yamakido M,Taniyama K,Sasaki N,Schlemper RJ. Helicobacter pylori infection and the development of gastric cancer. N Engl J Med 2001; 345: 7849.
  • 19
    Leung WK,Man EP,Yu J,Go MY,To KF,Yamaoka Y,Cheng VY,Ng EK,Sung JJ. Effects of Helicobacter pylori eradication on methylation status of E-cadherin gene in noncancerous stomach. Clin Cancer Res 2006; 12: 321621.
  • 20
    Chan AO,Chu KM,Huang C,Lam KF,Leung SY,Sun YW,Ko S,Xia HH,Cho CH,Hui WM,Lam SK,Rashid A. Association between Helicobacter pylori infection and interleukin 1beta polymorphism predispose to CpG island methylation in gastric cancer. Gut 2007; 56: 5957.
  • 21
    Takada K. Epstein-Barr virus and gastric carcinoma. Mol Pathol 2000; 53: 25561.
  • 22
    Tsugane S,Sasazuki S,Kobayashi M,Sasaki S. Salt and salted food intake and subsequent risk of gastric cancer among middle-aged Japanese men and women. Br J Cancer 2004; 90: 12834.
  • 23
    Kawakami Y,Akahane T,Gotoh A,Okimura Y,Oana K,Katsuyama T. Successful development of air-dried microplates (HP-Plates) for susceptibility testing against Helicobacter pylori isolates. Microbiol Immunol 1997; 41: 70308.
  • 24
    Nishikawa K,Sugiyama T,Kato M,Ishizuka J,Kagaya H,Hokari K,Asaka M. A prospective evaluation of new rapid urease tests before and after eradication treatment of Helicobacter pylori, in comparison with histology, culture and 13C-urea breath test. Gastrointest Endosc 2000; 51: 1648.
  • 25
    Kosunen TU,Seppala K,Sarna S,Sipponen P. Diagnostic value of decreasing IgG, IgA, and IgM antibody titres after eradication of. Helicobacter pylori. Lancet 1992; 339: 8935.
  • 26
    Marchildon P,Balaban DH,Sue M,Charles C,Doobay R,Passaretti N,Peacock J,Marshall BJ,Peura DA. Usefulness of serological IgG antibody determinations for confirming eradication of Helicobacter pylori infection. Am J Gastroenterol 1999; 94: 210508.
    Direct Link:
  • 27
    De Smet C,Loriot A,Boon T. Promoter-dependent mechanism leading to selective hypomethylation within the 5′ region of gene MAGE-A1 in tumor cells. Mol Cell Biol 2004; 24: 478190.
  • 28
    Song JZ,Stirzaker C,Harrison J,Melki JR,Clark SJ. Hypermethylation trigger of the glutathione-S-transferase gene (GSTP1) in prostate cancer cells. Oncogene 2002; 21: 104861.
  • 29
    Hagihara A,Miyamoto K,Furuta J,Hiraoka N,Wakazono K,Seki S,Fukushima S,Tsao MS,Sugimura T,Ushijima T. Identification of 27 5′ CpG islands aberrantly methylated and 13 genes silenced in human pancreatic cancers. Oncogene 2004; 23: 870510.
  • 30
    Furuta J,Nobeyama Y,Umebayashi Y,Otsuka F,Kikuchi K,Ushijima T. Silencing of Peroxiredoxin 2 and aberrant methylation of 33 CpG islands in putative promoter regions in human malignant melanomas. Cancer Res 2006; 66: 608086.
  • 31
    Ushijima T. Detection and interpretation of altered methylation patterns in cancer cells. Nat Rev Cancer 2005; 5: 22331.
  • 32
    Keshet I,Schlesinger Y,Farkash S,Rand E,Hecht M,Segal E,Pikarski E,Young RA,Niveleau A,Cedar H,Simon I. Evidence for an instructive mechanism of de novo methylation in cancer cells. Nat Genet 2006; 38: 14953.
  • 33
    Ohm JE,McGarvey KM,Yu X,Cheng L,Schuebel KE,Cope L,Mohammad HP,Chen W,Daniel VC,Yu W,Berman DM,Jenuwein T, et al. A stem cell-like chromatin pattern may predispose tumor suppressor genes to DNA hypermethylation and heritable silencing. Nat Genet 2007; 39: 23742.
  • 34
    Widschwendter M,Fiegl H,Egle D,Mueller-Holzner E,Spizzo G,Marth C,Weisenberger DJ,Campan M,Young J,Jacobs I,Laird PW. Epigenetic stem cell signature in cancer. Nat Genet 2007; 39: 1578.
  • 35
    Linhart HG,Lin H,Yamada Y,Moran E,Steine EJ,Gokhale S,Lo G,Cantu E,Ehrich M,He T,Meissner A,Jaenisch R. Dnmt3b promotes tumorigenesis in vivo by gene-specific de novo methylation and transcriptional silencing. Genes Dev 2007; 21: 311022.
  • 36
    Hodge DR,Xiao W,Clausen PA,Heidecker G,Szyf M,Farrar WL. Interleukin-6 regulation of the human DNA methyltransferase (HDNMT) gene in human erythroleukemia cells. J Biol Chem 2001; 276: 3950811.

Supporting Information

  1. Top of page
  2. Abstract
  3. Material and methods
  4. Results
  5. Discussion
  6. References
  7. Supporting Information

Additional Supporting Information may be found in the online version of this article.

FilenameFormatSizeDescription
IJC_24018_sm_SuppFig1.tif7357KSupporting Information Figure 1. The maps of the genes analyzed. Vertical lines, individual GpC or CpG sites; Open boxes, non-coding and coding exons; Arrows; transcription start sites; and Arrowheads, positions of MSP primers. It can be seen that a region upstream of a putative transcriptional start site in CpG island was analyzed by MSP.
IJC_24018_sm_SuppTable.doc69KSupporting Information Table.

Please note: Wiley Blackwell is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.