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. 2010 Jan 29;136(8):1267–1273. doi: 10.1007/s00432-010-0777-z

Semi-quantitative detection of GADD45-gamma methylation levels in gastric, colorectal and pancreatic cancers using methylation-sensitive high-resolution melting analysis

Wei Zhang 1, Tianrun Li 2, Yong Shao 3,4, Chao Zhang 4, Qi Wu 4, Hong Yang 5, Jie Zhang 3, Ming Guan 6, Bo Yu 3,4,✉, Jun Wan 4,7,✉
PMCID: PMC11828201  PMID: 20111973

Abstract

Purpose

GADD45 is a family of proteins involved in DNA damage response and cell growth arrest. GADD45G was identified as an interleukin-2-induced immediate-early gene, and methylation of GADD45G was studied in various tumor cell lines and a few primary tumor samples. High-resolution melting (HRM) analysis has been used as a novel tool for analysis of promoter methylation.

Methods

In our study, we used HRM analysis to detect the methylation levels of GADD45G gene in 100 gastric cancers, 100 colorectal cancers, 70 pancreatic cancers and equal number of adjacent normal tissues.

Results

The frequency of GADD45G methylation in all three types of cancers was significantly higher than that in normal tissues. Consistent with previous reports, expression levels of GADD45G were inversely correlated with methylation levels. But we did not find significant association between GADD45G methylation status and TNM staging in all three types of cancers.

Conclusions

In summary, application of HRM analysis to large amount of clinical samples proves to be a fast and high-throughput way to investigate the epigenetic status of GADD45G. And this is the first study to evaluate the prevalence of GADD45G methylation based on large amount of tumor samples, showing that epigenetic regulation of GADD45G was associated with carcinogenesis.

Keywords: GADD45G, Methylation, HRM, Gastric cancer, Colorectal cancer, Pancreatic cancer

Introduction

GADD45 is a family of proteins induced by DNA damage and extrinsic stressors (Takekawa and Saito 1998). GADD45A was initially identified as a gene rapidly induced by DNA-damaging agents, such as methylmethane sulfonate, UV radiation, hydroxyurea, and ionizing radiation (Fornace et al. 1989; Zhan et al. 1994). GADD45A is also a central player in the maintenance of genomic stability, and loss of protein function can lead to centrosome amplification, chromosomal instability, and increased aneuploidy (Hollander and Fornace 2002; Hollander et al. 1999). GADD45B (MyD118) was first cloned as one of many myeloid differentiation primary response (MyD) genes, induced in the absence of protein synthesis following treatment of M1 myeloblastic leukemia cells with differentiation inducers (Abdollahi et al. 1991; Liebermann and Hoffman 1998; Vairapandi et al. 1996). GADD45G (GRP17/CR6) was identified as an interleukin-2-induced immediate-early gene (Takekawa and Saito 1998; Vairapandi et al. 2002; Zhang et al. 1999). GADD45G exerts at least part of its influence by inhibiting the Cdk1/cyclinB1 complex and blocking the S and G2-M cell cycle transition (Vairapandi et al. 2002). Reintroduction of GADD45G to tumor cell lines previously lacking GADD45G expression showed significant reduction in colony formation and culture growth (Ying et al. 2005).

Promoter hyper-methylation is one of the hallmarks of carcinogenesis associated with transcriptional silencing of genes encoding for diverse cellular pathways, and is considered to be an important epigenetic mechanism implicated in the regulation of normal gene expression. Such changes often affect 5′ regulatory CpG genomic regions and can be associated with aberrant expression of certain genes in cancer (Esteller 2007). GADD45 genes are epigenetically inactivated in various types of cancer and tumor cell lines. GADD45A was reported to be methylated in multiple tumors (Al-Romaih et al. 2008; Hollander et al. 1999). GADD45B is down-regulated in hepatocellular carcinoma through methylation (Qiu et al. 2004), and GADD45G is also down-regulated in anaplastic thyroid cancer and pituitary adenoma (Chung et al. 2003; Zhang et al. 2002). Gene methylation status of GADD45G was well studied in various tumor cell lines, but was only rarely reported in primary tumor samples (Bahar et al. 2004; Ying et al. 2005). The apparent discordance between cell lines and primary patient samples requires further evaluation to better estimate the prevalence of GADD45G methylation.

High-resolution melting (HRM) analysis is a novel tool for analysis of promoter methylation (Wojdacz et al. 2008). The new approach is based on the “melting” properties of DNA in solution (Virmani et al. 2002), and was originally developed for SNP genotyping (Wittwer et al. 2003). The principle of this method is that bisulfite-treated DNA templates with different contents of methyl-cytosine can be resolved by melting analysis due to differences in melting temperatures (Paz et al. 2003). HRM relies on the precise monitoring of the change of fluorescence as a DNA duplex melts. This technique requires the use of standard PCR reagents and double stranded DNA-binding dyes that can be used at saturating concentrations without inhibiting PCR amplification (Wittwer et al. 2003). The melting analysis does not allow detailed information about the methylation of single cytosines within the sequence of interest, but can distinguish fully and partially methylated samples. The semi-quantitative measurement of methylation is important because low levels of methylation may not be biologically important (Cameron et al. 1999; Hsieh 1994). Also, quantification of promoter methylation may enable early detection of cancer and early metastatic spread (Taback et al. 2006).

In our study, we used HRM analysis to detect the methylation levels of GADD45G gene in 100 gastric cancers, 100 colorectal cancers, 70 pancreatic cancers and equal number of adjacent normal tissues. Methylation levels in all three types of cancers were significantly higher than that in normal tissues. This is the first study to estimate the prevalence of GADD45G methylation based on large amount of tumor samples, showing that epigenetic regulation of GADD45G was associated with carcinogenesis.

Materials and methods

Controls and patient samples

CpGenome Universal Methylated and unmethylated DNA (Chemicon, Millipore Billerica, MA, USA) were used as 100 and 0% methylated control DNA, respectively. Methylation standards were constructed by diluting 100% methylated bisulfite-modified control DNA in a pool of unmethylated bisulfite-modified DNA at ratios of 50, 10, and 1%. These standards were included in each experimental run.

Surgically resected tumor tissues and adjacent normal tissues were collected from 100 primary gastric cancer patients, 100 primary colorectal cancer patients and 70 primary pancreatic cancer patients. Staging was assessed after pathological examination of formalin fixed specimens based on the 2002 TNM classification (6th edition of the staging criteria of the UICC and AJCC). The study was approved by the ethical committee of the Shenzhen Hospital, Peking University. The individuals gave their written informed consent. The investigations were conducted according to the Declaration of Helsinki principles.

Extraction of genomic DNA and sodium bisulfite modification

Genomic DNA was isolated from the tissues using the Genomic DNA Extraction Kit (Innogent, Shenzhen, China) according to the manufacturer’s instruction. One microgram of genomic DNA was subjected to bisulfite conversion with the EZ DNA methylation kit (Zymo Research, USA). The eluted DNA (40 μl volume) was used for the HRM analysis.

HRM analysis

PCR amplification and HRM were performed on the ABI7500 (Applied Biosystems) as adapted from the published protocol (Wojdacz et al. 2008). The primers were designed as outlined (Wojdacz et al. 2008). The sequences of the primers for GADD45G are as follows: forward-CGTCGTGTTGAGTTTTGGT and reverse-TAACCGCGAACTTCTTCCA (115 bp). PCR was performed in a 20 μl volume containing: 1× buffer, 2 U Hotstart Taq DNA polymerase (Takara), 250 nM of each primer, 2.5 mM SYTO-9, and 10 ng bisulfite treated DNA template, with 3 mM final MgCl2. Each reaction was performed in triplicate. The cycling conditions were as follows: 1 cycle of 95°C for 10 min, 60 cycles of 95°C for 10 s, 62°C for 10 s, and 72°C for 10 s; followed by an HRM step of 95°C for 1 min, 40°C for 1 min, 65°C for 15 s, and continuous acquisition to 95°C at 1 acquisition per 0.3°C. A standard curve with known methylation ratios was included in each assay and was used to deduce the methylation ratio of each tumor and normal sample. HRM data were analyzed using the High Resolution Melting Software (Applied Biosystems). Output plots are in the form of normalized melting curves and difference plots. Statistical analysis was performed using chi-square test or Fisher exact test. Odds ratios (ORs) and 95% confidence intervals (95% CIs) were calculated according to Woolf’s method, using the SPSS 10.0 software for Windows. P values less than 0.05 were considered statistically significant.

Quantitative RT-PCR

Total RNA was isolated from whole blood by using AxyPrepTM Blood Total RNA MiniPrep Kit (Axygen) according to the manufacturer’s instruction. First strand cDNA was synthesized with RevertAidTM First Stand cDNA Synthesis Kit (Fermentas). Quantitative PCR was performed through BioRad Chromo4 real-time PCR system. The relative abundance of GADD45G mRNA level was calculated by using the comparative C(T) method (Livak and Schmittgen 2001; Schmittgen and Livak 2008) with GAPDH as the internal control. Logarithmic transformation (Log2 [1 + X]) was used to transform primary data to normal distribution. Data from three independent experiments were analyzed by Students’ t test and p < 0.05 was considered statistically significant. The primers for GADD45G are: forward: 5′GTCTACGAGTCAGCCAAAGTC and reverse: 5′AAAGCCTGGATCAGCGTAAAAT. The primers for GAPDH are: forward: 5′CAGCCTCAAGATCATCAGCA and reverse: 5′TGTGGTCATGAGTCCTTCCA.

Results

The sensitivity of HRM analysis for GADD45G

The sensitivity of the GADD45G HRM analysis was tested by using dilutions of fully methylated DNA into unmethylated DNA. The HRM standard melting curve was derived from five samples with the following ratios of methylated DNA: 0, 1, 10, 50 and 100% methylation. The inclusion of CpGs in the primer sequence makes it possible to direct the PCR bias towards the methylated templates by manipulating the annealing temperature of PCR amplification. At the annealing temperature of 62°C, methylation level as low as 1% can be easily detected (Fig. 1). The normalized melting profiles of the PCR product amplified from the same template were consistent between replicates and between different runs, and the shapes of normalized melting profiles were amplification independent as samples with different starting amount of template displayed very similar profiles (data not shown).

Fig. 1.

Fig. 1

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Normalized HRM standard curves and difference plot of GADD45G gene. Templates with different ratios (as indicated) of methylated DNA were amplified at the annealing temperature of 62°C, and subjected to HRM analysis. a Normalized melting curves, b difference plot

Methylation levels of GADD45G in tumor samples and normal tissues

Table 1 shows the methylation levels of GADD45G in 100 gastric, 100 colorectal and 70 pancreatic cancer samples and equal number of adjacent normal tissues. In all three types of cancers, methylation levels of GADD45G in cancer samples were significantly higher than that in normal tissues.

Table 1.

Methylation levels of GADD45G in cancer samples and normal tissues

Total no. 0% Methylation 0–1% Methylation 1–10% Methylation 10–50% Methylation 50–100% Methylation P
Gastric cancer
 Cancer samples 100 37 29 19 11 4 3.2E−07
 Normal tissues 100 71 25 4 0 0
Colorectal cancer
 Cancer samples 100 34 21 27 18 0 6.6E−12
 Normal tissues 100 56 43 1 0 0
Pancreatic cancer
 Cancer samples 70 33 15 17 5 0 0.0001
 Normal tissues 70 55 12 3 0 0
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Correlation between methylation and expression of GADD45G

In order to confirm that methylation of GADD45G was indeed correlate with the downregulation of its gene expression, we then used real-time PCR to detect the mRNA levels of GADD45G in the cancer samples. Since low levels of methylation may not be biologically important (Cameron et al. 1999; Hsieh 1994), the cancer samples were sub classified into to groups: <1% methylation and >1% methylation. The numbers of samples in the two groups are 66 and 34 (gastric cancer), 55 and 45 (colorectal cancer), and 48 and 22 (pancreatic cancer), respectively (Table 2). As expected, in all three types of cancers, expression levels of GADD45G were significantly lower in the groups of >1% methylation (Fig. 2), which is consistent with previous reports.

Table 2.

Methylation levels of GADD45G in different stages of cancers

No. of samples <1% Methylation >1% Methylation P
Gastric cancer
 Stage I cancers 15 10 (67%) 5 (33%) 0.834
 Stage II cancers 27 16 (59%) 11 (41%)
 Stage III cancers 37 25 (68%) 12 (32%)
 Stage IV cancers 21 15 (71%) 6 (29%)
 Total 100 66 (66%) 34 (34%)
Colorectal cancer
 Stage I cancers 11 7 (64%) 4 (36%) 0.818
 Stage II cancers 23 12 (52%) 11 (48%)
 Stage III cancers 32 16 (50%) 16 (50%)
 Stage IV cancers 34 20 (59%) 14 (41%)
 Total 100 55 (55%) 45 (45%)
Pancreatic cancer
 Stage I cancers 12 8 (67%) 4 (33%) 0.958
 Stage II cancers 21 15 (71%) 6 (29%)
 Stage III cancers 26 17 (65%) 9 (35%)
 Stage IV cancers 11 8 (73%) 3 (27%)
 Total 70 48 (69%) 22 (31%)
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Fig. 2.

Fig. 2

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GADD45G expression in different types of cancer samples. Total RNA from cancer samples were extracted and subjected to real-time PCR analysis. The relative abundance of GADD45G mRNA level was calculated by using the comparative C(T) method after logarithmic transformation. *p < 0.05

Correlation between methylation of GADD45G and staging of cancers

According to the sixth edition of the staging criteria of the UICC and AJCC, the cancer samples used in our study were assessed after pathological examination based on the 2002 TNM classification. The number of samples in each stage was summarized in Table 2. There was no significant association between GADD45G methylation status and TNM staging in all three types of cancers.

Discussion

Several methods have been developed for the analysis of methylation, each with their characteristic strengths and weaknesses. The most widely used method is methylation-specific PCR (MSP) that uses primers specific for methylated, bisulphite-modified DNA (Herman et al. 1996). MSP is very sensitive but is not quantitative, thus can lead to the classification of gene methylation when only a small number of cells are positive. Genomic sequencing can be considered the gold standard (Clark et al. 1994; Frommer et al. 1992). It provides the most detailed information but is relatively insensitive, and its expensive cost makes it generally unsuitable for screening. Pyrosequencing was recently introduced with higher sensitive, but is dependent on the availability of the proprietary instrumentation (Colella et al. 2003). HRM analysis becomes a novel tool for analysis of promoter methylation (Wojdacz et al. 2008). The applications of sequencing and HRM in methylation studies utilize methylation-independent PCR (MIP) where the primers are designed to amplify the bisulphite-modified sequence regardless of its methylation status. However, MIP primers do not always lead to the proportional amplification of methylated and unmethylated sequences (Warnecke et al. 1997; Wojdacz and Hansen 2006). So, inclusion of some CpGs seems necessary in the primer sequence to avoid the underestimate of the degree of methylation (Wojdacz and Hansen 2006). At lower annealing temperatures, the primers bind both methylated and unmethylated templates and PCR bias will favor the amplification of unmethylated sequences. At higher annealing temperatures, primer binding will favor methylated sequences. Thus the optimal annealing temperature is important for the effective amplification of templates independent of methylation status. HRM has several advantages over the other methods. It is high-throughput and relatively cheap. More importantly, it can be used to estimate the proportion or extent of methylation when run with standards. This is especially useful when assessing clinical cancer samples for predictive markers such as GADD45 where discrimination between different levels of methylation may have diagnostic and prognostic value. In our study, we have shown that HRM is applicable for the very sensitive detection of GADD45G methylation in an unmethylated background. With HRM, we were able to detect the methylation level of GADD45G as low as 1%.

Methylation of GADD45G has been reported in a few tumor cell lines and primary tumor samples (Bahar et al. 2004; Ying et al. 2005). In one recent study, GADD45G was shown to be methylated in 11 of 13 non-hodgkin lymphoma cell lines, but only 1/6 primary follicular lymphoma samples were found to be methylated (Ying et al. 2005). Another group found that the majority of pituitary adenomas (22 of 33; 67%) did not express GADD45G, and Loss of expression was associated with GADD45G methylation (19 of 33; 58%) (Bahar et al. 2004). Only limited number of primary tumor samples was recruited in previous studies, and there was an apparent discordance between cell lines and primary patient samples. This makes further evaluation necessary to better estimate the prevalence of GADD45G. Our study for the first time investigated GADD45G methylation in large amount of primary cancer samples. The high prevalence of hypermethylation of GADD45G in the gastric, colorectal and pancreatic cancers (34, 45 and 31%, respectively) suggests that transcriptional silencing of GADD45G by methylation is common and may be involved in the pathogenesis of many types of cancers. On the other hand, methylation levels among different cancer types are not consistent. In our cases, colorectal cancers have the highest percentage of methylation. This indicates that contribution of GADD45G methylation to carcinogenesis relies on the unique property of different cancer types. Furthermore, a certain incidence of GADD45 methylation was also detected in adjacent normal tissues, although the methylation levels in these normal tissues remain lower than 10% (Table 1). Aberrant hypermethylation could be caused by various factors in normal tissues such as diets or aging (Kwabi-Addo et al. 2007) and this might result in the low methylation frequencies in normal tissues. Another possibility is the contamination of trace amount of tumor tissues during the surgical resection process, since our normal tissues are resected from adjacent area of the same patient. In the future, application of microdissection would be very helpful to distinguish tumors from normal tissues.

A significant correlation between GADD45G methylation and loss of expression of GADD45G was observed in all three types of cancers by real-time PCR. This is not unexpected and is consistent with many previous studies. We also analyzed the staging of the cancers in relation to the methylation of GADD45G. To our surprise, no association was found between them. This can be explained by the fact that GADD45G methylation is supposed to be the cause, rather than the outcome, of carcinogenesis. Thus, even in the stage I of cancers, GADD45G gene is already in methylated state, as observed in our study. This suggests that GADD45G methylation cannot be used as an auxiliary criterion for the staging of cancers, but rather, can be used as a marker for the diagnosis of early stage cancers.

Conflict of interest statement

We declare that we have no conflict of interest.

Footnotes

W. Zhang, T. Li, B. Yu, J. Wan contributed equally to this work.

Contributor Information

Bo Yu, Phone: +86-755-13823226811, Email: yubomd@hotmail.com.

Jun Wan, Phone: +86-13728770312, FAX: +86-755-83913095, Email: wanj@ust.hk.

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