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Cancer Epidemiol Biomarkers Prev. Author manuscript; available in PMC Nov 18, 2009.
Published in final edited form as:
PMCID: PMC2778751

Aberrant Promoter Methylation of Multiple Genes during Pathogenesis of Bladder Cancer



The aims of our study were to elucidate the role of methylation of a large panel of genes during multistage pathogenesis of bladder cancer and to correlate our findings with patient age and other clinicopathologic features.

Experimental Design

We studied the methylation status of 21 genes by quantitative methylation-specific PCR in an evaluation set of 25 tumor and 5 normal samples. Based on methylation frequency in tumors and normals in gene evaluation set, we selected 7 candidate genes and tested an independent set of 93 tumors and 26 normals. The presence or absence of methylation was evaluated for an association with cancer using cross-tabulations and χ2 or Fisher’s exact tests as appropriate. All statistical tests were two-sided.


Most primary tumors (89 of 93, 96%) had methylation of one or more genes of independent set; 53 (57%) CCNA1, 29 (31%) MINT1, 36 (39%) CRBP, 53 (57%) CCND2, 66 (71%) PGP9.5, 60 (65%) CALCA, and 78 (84%) AIM1. Normal uroepithelium samples from 26 controls revealed no methylation of the CCNA1 and MINT1 genes, whereas methylation of CRBP, CCND2, PGP9.5, and CALCA was detected at low levels. All the 7 genes in independent set were tightly correlated with each other and 3 of these genes showed increased methylation frequencies in bladder cancer with increasing age. PGP9.5 and AIM1 methylation correlated with primary tumor invasion.


Our results indicate that the methylation profile of novel genes in bladder cancers correlates with clinicopathologic features of poor prognosis and is an age-related phenomenon.


In the United States, approximately 68,810 newly diagnosed bladder cancer cases and 14,100 deaths are expected in 2008 (1). More than 90% of bladder carcinomas have a transitional cell origin (2). Approximately 75% of these neoplasms are superficial at first presentation and are curatively treated by transurethral resection alone, although 30% to 85% will recur and 10% to 30% will progress to a muscle-invasive disease with a poorer prognosis (3). Like many other neoplastic diseases, the survival rate of bladder cancer patients is critically influenced by the progression of the tumor. Whereas the 5-year survival rate for patients with a localized tumor is 92%, it decreases to 45% and 6% for regional and distant disease, respectively (1). There is a need for improved clinical stratification methods that can identify patients with early-stage disease and identify those with high risk of recurrence. Conventional methods, including computed tomography, urine cytology, histopathology, or tumor-node-metastasis classification, have thus far failed to overcome limitations in early detection and risk assessment. Conversely, a variety of novel molecular methods, such as detection of activating mutation of FGFR3 (4), p53 mutation status (5), loss of heterozygosity (5, 6), and methylation profiling (7, 8), have shown promising results. Despite growing research efforts, the molecular mechanisms underlying bladder cancer development and progression remain to be fully dissected. A better understanding of the molecular alterations associated with bladder cancer is likely to contribute to improved diagnosis, clinical management, and outcome prediction.

Epigenetic modifications are defined as all meiotically and mitotically heritable changes in gene expression that are not coded in the DNA sequence itself. Methylation of the C5 positions of cytosine residues in DNA has long been recognized as an epigenetic silencing mechanism of fundamental importance (9). DNA methylation alters chromosome structure, inhibits the binding of proteins such as CTCF (a candidate tumor suppressor protein that binds to highly divergent DNA sequences), and defines regions of transcriptional regulation (10). DNA methylation can also promote the binding of proteins, such as MECP2, MBD1, MBD2, MBD3, and MBD4, which induce histone modification (11).

CpG dinucleotides are found at increased frequency in the promoter region of many genes, and methylation in the promoter region is frequently associated with “gene silencing”; the gene is not expressed in the presence of methylation but is expressed in its absence (12). Both global hypomethylation and gene-specific promoter hypermethylation are associated with malignancy (13, 14), and studies in animals and in humans have shown that these epigenetic changes are an early event in carcinogenesis and are present in the precursor lesions of a variety of cancers including lung (15), head and neck (16), and colon (17). This epigenetic alteration provides an alternative pathway to gene silencing in addition to gene mutation or deletion. Moreover, the finding of promoter methylation of several genes in small biopsies and bodily fluids of cancer patients has proven to be useful as a molecular tool for cancer detection (7, 18).

In bladder cancer, several gene promoters were found to be hypermethylated by using conventional or quantitative methylation-specific PCR (QMSP; refs. 7, 19, 20). Indeed, the methylation of individual genes was found to correlate with clinicopathologic indicators of poor prognosis (19, 20). Conventional MSP is of limited usefulness for specific cancer detection because benign lesions can be weakly positive and cannot be distinguished from cancer cases. This limitation can be overcome by the development of quantitative assays such as QMSP.

In this study, we sought to characterize and quantitate the promoter methylation status of 21 genes derived from previously tested genes in several human cancers and novel genes we identified recently by our pharmacologic unmasking strategy (21). We tested the methylation status of these 21 genes (CTNNB1, CALCA, hMLH1, PGP9.5, DAPK, CCND2, HIC1, AIM1, DCC, MINT1, MINT31, FANC-F, TGF-β, ATM, CRBP, THBS1, 14-3-3σ, CCNA1, ESR, FHIT, and MT1G) first in an evaluation set consisting of 25 bladder tumor samples and 5 bladder normal samples. Based on the frequency of methylation observed for these genes in the evaluation set, 7 genes were selected (CALCA, PGP9.5, CCND2, AIM1, MINT1, CRBP, and CCNA1) for further analysis in an independent set of 93 tumor samples and 26 normal controls. Relationships between methylation levels and clinicopathologic indicators were further assessed.

Materials and Methods

Study Cohort

The bladder cancer samples were retrospectively collected from our tissue archive. To be included in the cohort, an eligible patient had to have a confirmed diagnosis of bladder cancer and a sufficient amount of archived tumor material to allow for DNA extraction (tissue preserved in sectioned blocks; >50% of tumor cells). Cases of bladder cancers with mixed histology, metastatic tumors to the bladder, and those of indeterminate clinical stage were excluded. Ninetyfour percent (111 of 118) of our samples were urothelial cell carcinomas. Demographic and clinical information, including survival information, was obtained from the computerized tumor registry at The Johns Hopkins Healthcare System and M. D. Anderson Cancer Center. The total number of patient samples consisted of 93 males and 25 females. The tumors were classified according to the three-tier WHO histologic grading system (22), growth pattern (papillary versus nonpapillary), and DNA ploidy (23). Urothelial cell carcinomas were divided into a low-grade (grade 1 or 2) group or a high-grade (grade 3) group. The depth of invasion was recorded according to tumor-node-metastasis staging system (24). Tumors were divided into a superficial (Ta, Tis, and T1) group or a muscle-invasive (T2–T4) group. The independent test set consisted of 30 patients with clinical stage 1 (Ta and Tis), 32 with clinical stage 2 (T1), and 31 with clinical stage 3 (T2–T4). A detailed summary of the patient populations of the evaluation and test set are available in Table 1. The normal uroepithelium was obtained from archived autopsy samples (n = 16) or any benign condition of the uroepithelium (n = 10). The median age of the controls was 60.5 years (range, 8–99). Approval for research on human subjects was obtained from The Johns Hopkins University institutional review boards. This study qualified for exemption under the U.S. Department of Health and Human Services policy for protection of human subjects [45 CFR 46.101(b)].

Table 1
Demographic and clinical characteristics of bladder cancer patients (evaluation set n = 25, independent test set n = 93)

Gene Selection

A total of 21 genes were selected for QMSP-based examination of methylation abnormalities. The gene profile included CTNNB1, CALCA, hMLH1, PGP9.5, DAPK, CCND2, HIC1, AIM1, DCC, MINT1, MINT31, FANC-F, TGF-β, ATM, CRBP, THBS1, 14-3-3σ, CCNA1, ESR, FHIT, and MT1G. The panel included genes reported as targets for epigenetic silencing in human cancer. ATM was included in this panel given the recent reports of hypermethylation in head and neck cancers (25), also a tobacco-related malignancy. hMLH1 is involved in DNA repair, the inactivation of which results in increased mutagenicity. DAPK gene is involved in apoptosis and CCNA1 and CCND2 affect cell cycle and are frequently inactivated in human cancers. The function of DCC, AIM1, and PGP9.5 are not yet well understood. Detailed summary of these genes including their proposed function is available in Supplementary Table S1.

DNA Extraction

After initial patient de-identification, all original histologic slides from the bladder cancer specimens were reviewed to reconfirm the diagnosis by a senior pathologist (W.H.W.). A representative block was retrieved for DNA extraction. Microdissected normal bladder epithelium was isolated from autopsy material or any noncancerous condition. Histologic slides from the formalin-fixed, paraffin-embedded tissue were taken. Slides were microdissected to obtain >70% neoplastic cells. DNA was extracted using standard protocols as described previously (26).

Bisulfite Treatment

DNA extracted from primary tumors and normal uroepithelium was subjected to bisulfite treatment, which converts unmethylated cytosine residues to uracil residues, as described previously (27) with minor modification. Briefly, 2 µg genomic DNA from each sample was denatured with NaOH (final concentration, 0.2 mol/L) in a total volume of 20 µL for 20 min at 50°C. The denatured DNA was diluted in 500 µL of a freshly prepared solution of 10 mmol/L hydroquinone and 3 mol/L sodium bisulfite and incubated for 3 h at 70°C. Bisulfite-modified DNA was purified using a Wizard DNA Clean-Up System (Promega), treated with 0.3 mol/L NaOH for 10 min at room temperature, precipitated with ethanol, resuspended in 120 µL LoTE [2.5 mmol/L EDTA, 10 mmol/L Tris-HCl (pH 8)], and stored at −80°C.

Methylation Analysis

Bisulfite-modified DNA was used as a template for fluorescence-based real-time PCR as described previously (28). Amplification reactions were carried out in triplicate in a final volume of 20 µL that contained 3 µL bisulfite-modified DNA; 600 nmol/L concentrations of forward and reverse primers; 200 nmol/L probe; 0.6 units platinum Taq polymerase (Invitrogen); 200 µmol/L concentrations each of dATP, dCTP, dGTP, and dTTP; and 6.7 mmol/L MgCl2. Primers and probes were designed to specifically amplify the promoters of the 21 genes of interest and the promoter of a reference gene, ACTB; primer and probe sequences and annealing temperatures are provided in Supplementary Table S2. Amplifications were carried out using the following profile: 95°C for 3 min followed by 50 cycles at 95°C for 15 s and 60°C for 1 min. Amplification reactions were carried out in 384-well plates in a 7900 sequence detector (Perkin-Elmer Applied Biosystems) and analyzed by a sequence detector system (SDS 2.2.1; Applied Biosystems). Each plate included patient DNA samples, positive (in vitro methylated leukocyte DNA) and negative (normal leukocyte DNA or DNA from a known unmethylated cell line) controls, and multiple water blanks. Leukocyte DNA from a healthy individual was methylated in vitro with excess SssI methyltransferase (New England Biolabs) to generate completely methylated DNA, and serial dilutions (90-0.009 ng) of this DNA were used to construct a calibration curve for each plate. All samples were within the assay’s range of sensitivity and reproducibility based on amplification of internal reference standard [threshold cycle (CT) value for ACTB of 40]. The relative level of methylated DNA for each gene in each sample was determined as a ratio of MSP-amplified gene to ACTB (reference gene) and then multiplied by 1,000 for easier tabulation (average value of triplicates of gene of interest divided by the average value of triplicates of ACTB × 1,000). The samples were categorized as unmethylated or methylated based on the sensitivity of the assay. The threshold of the assay of individual genes was determined by maximizing sensitivity and specificity.

Statistical Analysis

We used Fisher’s exact method to test the methylation frequency differences in gene evaluation set. In the test set, the major statistical endpoints in this study involved comparing normal and cancer methylation levels of seven genes thought to be associated with cancer. Methylation values were visually compared using boxplots (29) of the log-transformed values. In these plots, the length of the box is the interquartile range of the data and depicts the spread of the middle 50% of the observations. The median is displayed with a horizontal line inside of this box. The lines extending out from the box extend from the upper and lower quartiles to values defined as adjacent values. The adjacent values are the upper quartile plus 1.5 × interquartile range and the lower quartile minus 1.5 × interquartile range. Any value lying outside of this range is displayed with an open circle and can be considered an outlier.

The presence or absence of methylation was evaluated for an association with cancer using cross-tabulations and χ2 or Fisher’s exact tests as appropriate. Continuous methylation levels were evaluated using logistic regression (30). Internal validation of the logistic regression models was done using an approximation to the leave-one-out jackknife procedure provided by the SAS classification table option (31).

Methylation index was calculated as the total number of genes having anymethylation divided by the total number of genes examined. Mean methylation index was compared across age, stage, and grade categories with ANOVA models. Correlations of the methylation levels of genes were calculated with Spearman correlation coefficients.

The presence or absence of methylation was also evaluated for an association with survival. Event time distributions were estimated with the method of Kaplan and Meier (32) and compared using the log-rank statistic (33) or the proportional hazards regression model (34). All P values are two-sided. Computations were done using the Statistical Analysis System (31, 35).


Clinical and Pathologic Data

We tested tissue samples from a total of 118 patients (evaluation and test set) with bladder cancer and a total of 31 normals (evaluation and test set) from autopsies and benign diseases of uroepithelium. The clinicopathologic and demographic characteristics of these patients (evaluation and independent test set) are depicted in Table 1.

Determination of Appropriate Genes in a Gene Evaluation Set by QMSP

We initially tested 21 genes in DNA from 25 primary bladder tumor tissues and 5 normal uroepithelium samples from the deceased archive or uroepithelium of any benign condition. An overview of the frequency of methylation in the investigated samples is given in Table 2. In general, methylation levels were higher in tumors compared with normal samples (Fig. 1). The most appropriate genes for our additional analyses were determined to be those that met one of the following criteria: (a) unmethylated in normal samples from controls and ≥48% methylated in primary bladder cancer tissues or (b) ≤40% methylated in normal controls and >72% methylated in primary bladder cancer tissues. A total of seven genes, CALCA, PGP9.5, CCND2, AIM1, MINT1, CRBP, and CCNA1, met at least one of these criteria (Table 2).

Figure 1
Promoter methylation levels for the seven markers in the bladder cancer patient samples (CA; n = 93) and normal bladder tissues (N; n = 26). The quantity of each methylated gene promoter was expressed as the ratio of the amount of PCR products amplified ...
Table 2
Promoter methylation frequency for the 21 genes analyzed in the evaluation set of samples and 7 genes in the independent test set

Frequency of Methylation in Bladder Tissues in Independent Set by QMSP

We then tested these 7 genes in the independent set of cancer and control tissues. The frequencies of methylation of the 7 genes in 93 bladder cancers samples and 26 nonmalignant tissues are detailed in Table 2. The differences of methylation values between tumor tissue and normal were all statistically significant (Fisher’s exact test) (Table 2). The sensitivity and specificity of each gene vary from 31% to 84% and 73% to 100%, respectively (Table 2). There was no methylation of CCNA1 or MINT1 in normal bladder tissues. Most cancers (89 of 93, 95.6%) had methylation of one or more genes. Thirteen (14%) had methylation of 2 genes, 8 (9%) had methylation of 3 genes, 12 (13%) had methylation of 4 genes, 11 (12%) had methylation of 5 genes, 21 (23%) had methylation of 6 genes, and 12 (13%) had methylation of 7 genes. There was no methylation of any gene in 4 (4%) cancer cases. By contrast, 50% of nonmalignant tissue (13 of 26) lacked methylation of any gene tested, 7 had methylation of 1 gene, 3 had methylation of 2 genes, 3 had methylation of 3 genes, and none had methylation of ≥4 genes.

The methylation values differ among normal and tumor and within the tumor and normal samples. The boxplots in Fig. 1 show the distribution of relative methylation values for each of the 7 genes of interest versus ACTB obtained by QMSP in cancer patients versus normal samples. Methylation markers have been categorized as methylated for any value greater than zero.

Correlation of Primary Tumor Hypermethylation Profiles with Clinicopathologic Variables

There were 93 cancer samples and 26 normal samples in the independent set of genes of this study. Age, gender, and race were compared between groups. Race was not significantly different, and the median age in the cancer group was 67 compared to 60.5 years in the normal group.

Several clinicopathologic and demographic variables (age, gender, histologic type, tumor stage, tumor grade, metastasis, recurrence, smoking, and overall patient survival) were compared with the methylation patterns observed in the tumor DNA. Logistic regression analysis was done to determine whether the frequency of QMSP markers or the combination thereof correlated with these variables associated with bladder cancer prognosis. In a univariate analysis, age, tumor-node-metastasis stage, and invasion were associated with methylation of at least one of the seven gene promoters. Patients ages ≥65 years were significantly more likely to have CCNA1 (P = 0.004), PGP9.5 (P = 0.01), or CCND2 (P = 0.02) methylation. PGP9.5 (P = 0.04) and AIM1 (P = 0.02) were associated with invasive tumors. CALCA methylation was associated with late-stage (≥pT2) tumors (P = 0.0001).

Mean methylation index was significantly greater in patients ages ≥65 years, 3.67 [95% confidence interval (95% CI), 3.16–4.18] compared with the younger group, 2.56 (95% CI, 2.0–3.1; P = 0.004). Stage III mean methylation index, 4.4 (95% CI, 3.8–5.0), was also significantly greater than that in stage I, 3.4 (95% CI, 2.7–4.2; P = 0.05), or stage II, 3.5 (95% CI, 2.7–4.2; P = 0.05). The mean methylation index is significantly higher in the muscle-invasive stage then in the superficial stages, 4.4 (95% CI, 3.8–5.0) and 3.4 (95% CI, 2.9–4.0), respectively (P < 0.0001; Fig. 2). Mean methylation index was the lowest in the normal samples, 0.85 (95% CI, 0.4–1.3). Low-grade and high-grade mean indexes were similar, low-grade mean, 3.06 (95% CI, 1.9–4.2) and high-grade mean, 3.9 (95% CI, 3.4–4.3; P = 0.2). For all markers, higher methylation level was significantly associated with an increased probability of cancer. The boxplots in Fig. 3 show the distribution of relative promoter methylation levels for four representative markers (CCND2, PGP9.5, CCNA1, and CALCA) in the bladder cancer patient samples in noninvasive stages (<pT2) and muscle-invasive stage (≥pT2). The median of normalized methylation value and the frequency for CCND2, PGP9.5, CCNA1, and CALCA genes are higher in ≥pT2 tumors (Fig. 3). Aberrant methylation in primary bladder cancer had no correlation with disease metastasis, smoking history, recurrence, tumor grade, and survival of the patients (data not shown).

Figure 2
Mean methylation index levels markers in bladder cancer samples compared across noninvasive stages and muscle-invasive stage. Methylation index was calculated as the total number of genes having any methylation divided by the total number of genes examined. ...
Figure 3
Promoter methylation levels for four representative markers in bladder cancer samples for noninvasive stages (1 + 2) and muscle-invasive stage (3). The quantity of methylated allele of each gene was expressed as the ratio of the amount of PCR products ...

Finally, we performed a correlation analysis for all pairs of markers (Table 3). Promoter methylation of every pair of genes was statistically significantly correlated (all P < 0.0014). The strongest correlations were between CCND2 and PGP9.5, CRBP, and MINT1 (r ≥ 0.59). In addition, promoter methylation of CALCA was highly correlated with MINT1 (r = 0.53).

Table 3
Spearman correlation matrix of promoter methylation levels for all genes in bladder cancer samples


There is growing evidence that DNA methylation is an important mechanism of gene inactivation in cancer and its potential as a molecular marker. Considerable variations exist in promoter methylation profiles of different cancers, such as individual tumor types have characteristic methylation profiles (36, 37). Some methylation markers have been shown to be promising in the noninvasive diagnosis of bladder cancer and predicting patient survival and tumor progression (7, 8, 38). Based on our experience and literature review, it is evident that methylation can occur in a tissue-specific or cancer-specific manner. Cancer-specific methylation is usually limited to tumor tissues and seldom observed in progenitor tissues (21, 39).

We determined the methylation profile of bladder cancers testing a panel of 21 genes, which include genes extensively studied in other tumor types (CTNNB1, CALCA, hMLH1, PGP9.5, DAPK, CCND2, HIC1,DCC,MINT1,MINT31, FANC-F, TGF-β, ATM, CRBP, THBS1, 14-3-3σ, CCNA1, ESR, FHIT, and MT1G), genes not yet reported (AIM1), and genes reported to be methylated in bladder cancer by the widely used conventional MSP assay (hMLH1, CCND2, DAPK, HIC1, and FHIT). In addition to the 7 genes in the independent set, further studies of the remaining genes in a larger number of samples will elucidate the role of these genes in bladder cancer stratification. Most of the genes that we examined have not been studied previously for methylation in bladder cancers and we identified 8 methylated genes, which are bladder cancer specific (methylation in tumor not in normal). Although some genes seemed to have strong individual associations with different clinicopathologic variables, we were keen to develop a panel of loci that would be specific to all urothelial cell carcinoma and represent the extent of global epigenetic disruption. These findings also corroborate the use of candidate gene approach for the characterization of organ-specific methylated genes.

In this study, the frequency of CCND2 promoter methylation was higher than reported previously (40). Besides possible differences in the patient population, it is noteworthy that conventional MSP was used previously, whereas we used a quantitative methodology with different PCR conditions that might be more sensitive than conventional MSP. Interestingly, divergent results were also obtained by QMSP and conventional MSP for the frequency of APC methylation in the same small-cell lung cancer cell lines: 58% by QMSP versus 26% by conventional MSP (41, 42). Yet, we found low level of methylation in normals, in contrasting to previous reports (40). This discrepancy is likely due to the high sensitivity of QMSP and its ability to detect rare methylated cells in normal urothelium. Hence, precautions should be taken when comparing results of different studies concerning aberrant promoter methylation in the same tumor type, because different methodologies and use of different primers may yield varied results. The major advantage of QMSP is the ability to quantitatively compare samples and to more accurately segregate varied pathologic covariates based on appropriate cutoffs. For example, we analyzed 5 genes using optimal cutoffs; as shown, by compromising sensitivity, specificity was increased to almost 90% for all 5 genes combined (data not shown).

In several human cancer cell lines, aberrant promoter methylation of the majority of genes examined in this study has been shown to abrogate transcription (43), and reactivation was observed in the nonexpressing cell lines after treatment with demethylating agents (21, 44, 45). Moreover, these genes are involved in important molecular pathways of carcinogenesis such as cell cycle regulation, DNA repair, apoptosis, and signal transduction (13, 46, 47). Two of the seven genes tested (CCNA1 and MINT1) displayed high frequency of methylation in bladder cancer and no methylation in normal uroepithelium. For the remaining 5 genes (CRBP, CALCA, PGP9.5, CCND2, and AIM1), we found significantly more methylation in bladder cancer cases than normal epithelium. Although we found a low frequency of methylation in normal uroepithelium, the differences in methylation levels of these genes between neoplastic and normal controls are even more striking than the differences in methylation frequency. Indeed, the isolated presence of methylation is not necessarily specific for malignant disease, whereas quantitation of methylation allows for accurate discrimination between bladder carcinoma and uroepithelium, confirming our previous observations for different cancer types and various genes (7, 48). By establishing an empiric cutoff value, we calculated the frequency of methylation in the independent set, which shows an increased specificity but decreased sensitivity (Table 4).

Table 4
Promoter methylation frequency based on an empiric cut off value for the 7 genes in the independent test set

We also showed a correlation between methylation levels of individual or panel of genes and standard clinicopathologic conditions (tumor grade, invasiveness, recurrence, and stage). CCNA1 was reported previously to be associated with invasive cervical cancer (43). We found an increased methylation frequency in muscle-invasive tumors. These findings indicate a potential role of CCNA1 in the pathogenesis and spread of bladder cancer. Cancer-specific promoter hypermethylation of PGP9.5 was reported in different cancer types by our group and others (45, 49, 50); however, cancer-specific hypomethylation (PGP9.5) was also reported in gall bladder cancer (51). We found a high frequency of bladder cancer-specific methylation of PGP9.5 in the present study and the median methylation values are significantly higher in invasive tumors (odds ratio, 2.68; 95% CI, 1.05–6.82; P = 0.04). Furthermore, we found that patients with higher PGP9.5 methylation ratios in the primary tumor showed poorer 5-year survival rates than those without PGP9.5 methylation in esophageal cancer (45). Hypermethylation of the cell cycle gene CCND2 was also associated with advanced stage of the disease in ovarian cancer (52), which is consistent with our findings here in bladder cancer. The significant association of several methylated genes were also observed in higher stages of tumor in bladder cancer using urine sediment of bladder cancer patients (7). These findings may have clinical implications for the management of bladder cancer. Because methylation levels were found to correlate with tumor aggressiveness, these molecular assays could be included in predictive models for preoperative bladder cancer staging. If confirmed, carcinomas found to harbor high levels of promoter methylation in bladder biopsy may be more likely to benefit from appropriate therapeutic intervention.

A correlation between the methylation index (the proportion of methylated genes) and markers of poor outcome in bladder cancer has been reported by our group and others (19, 38). However, in this study, we did not find any statistically significant association between the methylation status of a single gene or methylation index as a marker of clinical outcome. Longer follow-up of this cohort of patients is needed to clarify any association of any methylated gene with clinical outcome.

There are differences in the pattern of gene methylation in tumors of different organs. Methylated genes can be divided into those that occur generally in most cancers (such as E-cadherin and RASSF1a), those that occur commonly in a few cancers (e.g., APC in bladder and colonic cancer), and those that are mostly specific to one organ (e.g., GSTP1 in prostate cancer or VHL in renal cancer; ref. 36). Although the loci and extent of genes affected by hypermethylation vary between tumors, their loss reflects global changes in epigenetic control. By selecting loci for various tumors, one could devise organ-specific gene panels that reveal events across the entire epigenome. This knowledge is useful as epigenetic alterations affect the molecular mechanisms within a tumor and thus the subsequent clinical phenotype. For example, high levels of methylation are found in sporadic cancers with microsatellite instability due to hMLH1 methylation (53). Tumors with this form of genomic instability are relatively chemoresistant and behave more indolently than matched tumors without microsatellite changes (54). Similarly, MGMT methylation alters sensitivity to temozolomide and could eventually be used to stratify patient treatment (55).

The present study constitutes one of the most comprehensive methylation profile reports available for bladder cancer. We have identified groups of tumor-specific genes as well as genes that are frequently methylated in normal urothelium. We have confirmed and extended the observations of others that methylation of some genes are tightly correlated in tumors as well as genes that show age-related methylation. Organ-specific panels of loci could be developed that reflect global epigenetic events and would be small enough to be practical for routine application. Further multi-institutional studies are required to validate these markers and their general applicability in the management of patients with urothelial cancer.

Supplementary Material

Supplemental Table 1-2


We thank Marianna Zahurak for contributing with statistical analysis.

Grant support: National Cancer Institute grant U01-CA84986 and Oncomethylome Sciences; Flight Attendant Medical Research Institute Young Clinical Scientist Award and International Association for the Study of Lung Cancer Young Investigator Award (M.O. Hoque); and Fundação de Amparo à Pesquisa do Estado de São Paulo and Coordenação de Aperfeiçoamento de Pessoal de Nível Superior PDEE scholarships (M. Brait).


Note: Supplementary data for this article are available at Cancer Epidemiology, Biomarkers & Prevention Online (http://cebp.aacrjournals.org/).

Disclosure of Potential Conflicts of Interest

D. Sidransky: Oncomethylome Sciences, commercial research grant and ownership interest. The other authors disclosed no potential conflicts of interest.


1. Jemal A, Siegel R, Ward E, et al. Cancer statistics 2008. CA Cancer J Clin. 2008;58:71–96. [PubMed]
2. Parkin DM, Pisani P, Ferlay J. Estimates of the worldwide incidence of 25 major cancers in 1990. Int J Cancer. 1999;80:827–841. [PubMed]
3. Williams SG, Stein JP. Molecular pathways in bladder cancer. Urol Res. 2004;32:373–385. [PubMed]
4. Billerey C, Chopin D, Aubriot-Lorton MH, et al. Frequent FGFR3 mutations in papillary non-invasive bladder (pTa) tumors. Am J Pathol. 2001;158:1955–1959. [PMC free article] [PubMed]
5. Sidransky D, Von Eschenbach A, Tsai YC, et al. Identification of p53 gene mutations in bladder cancers and urine samples. Science. 1991;252:706–709. [PubMed]
6. Knowles MA. What we could do now: molecular pathology of bladder cancer. Mol Pathol. 2001;54:215–221. [PMC free article] [PubMed]
7. Hoque MO, Begum S, Topaloglu O, et al. Quantitation of promoter methylation of multiple genes in urine DNA and bladder cancer detection. J Natl Cancer Inst. 2006;98:996–1004. [PubMed]
8. Yates DR, Rehman I, Abbod MF, et al. Promoter hypermethylation identifies progression risk in bladder cancer. Clin Cancer Res. 2007;13:2046–2053. [PubMed]
9. Riggs AD. X inactivation, differentiation, and DNA methylation. Cytogenet Cell Genet. 1975;14:9–25. [PubMed]
10. Hark AT, Schoenherr CJ, Katz DJ, Ingram RS, Levorse JM, Tilghman SM. CTCF mediates methylation-sensitive enhancer-blocking activity at the H19/Igf2 locus. Nature. 2000;405:486–489. [PubMed]
11. Jaenisch R, Bird A. Epigenetic regulation of gene expression: how the genome integrates intrinsic and environmental signals. Nat Genet. 2003;(33 Suppl):245–254. [PubMed]
12. Leonhardt H, Rahn HP, Cardoso MC. Functional links between nuclear structure, gene expression, DNA replication, and methylation. Crit Rev Eukaryot Gene Expr. 1999;9:345–351. [PubMed]
13. Momparler RL. Cancer epigenetics. Oncogene. 2003;22:6479–6483. [PubMed]
14. Ehrlich M. DNA hypomethylation, cancer, the immunodeficiency, centromeric region instability, facial anomalies syndrome and chromosomal rearrangements. J Nutr. 2002;132:2424S–2429S. [PubMed]
15. Belinsky SA, Nikula KJ, Palmisano WA, et al. Aberrant methylation of p16(INK4a) is an early event in lung cancer and a potential biomarker for early diagnosis. Proc Natl Acad Sci U S A. 1998;95:11891–11896. [PMC free article] [PubMed]
16. Hoque MO, Rosenbaum E, Westra WH, et al. Quantitative assessment of promoter methylation profiles in thyroid neoplasms. J Clin Endocrinol Metab. 2005;90:4011–4018. [PubMed]
17. Esteller M, Sparks A, Toyota M, et al. Analysis of adenomatous polyposis coli promoter hypermethylation in human cancer. Cancer Res. 2000;60:4366–4371. [PubMed]
18. Sidransky D. Emerging molecular markers of cancer. Nat Rev Cancer. 2002;2:210–219. [PubMed]
19. Maruyama R, Toyooka S, Toyooka KO, et al. Aberrant promoter methylation profile of bladder cancer and its relationship to clinicopathological features. Cancer Res. 2001;61:8659–8663. [PubMed]
20. Christoph F, Hinz S, Kempkensteffen C, et al. A gene expression profile of tumor suppressor genes commonly methylated in bladder cancer. J Cancer Res Clin Oncol. 2007;133:343–349. [PubMed]
21. Yamashita K, Upadhyay S, Osada M, et al. Pharmacologic unmasking of epigenetically silenced tumor suppressor genes in esophageal squamous cell carcinoma. Cancer Cell. 2002;2:485–495. [PubMed]
22. Mostofi FK. Histological typing of urinary bladder tumors. New York: Springer; 1999.
23. Koss LG, Czerniak B, Herz F, Wersto RP. Flow cytometric measurements of DNA and other cell components in human tumors: a critical appraisal. Hum Pathol. 1989;20:528–548. [PubMed]
24. Sobin LH, Fleming ID. TNM classification of malignant tumors, fifth edition (1997). Union Internationale Contre le Cancer and the American Joint Committee on Cancer. Cancer. 1997;80:1803–1804. [PubMed]
25. Ai L, Vo QN, Zuo C, et al. Ataxia-telangiectasia-mutated (ATM) gene in head and neck squamous cell carcinoma: promoter hypermethylation with clinical correlation in 100 cases. Cancer Epidemiol Biomarkers Prev. 2004;13:150–156. [PubMed]
26. Hoque MO, Topaloglu O, Begum S, et al. Quantitative methylation-specific polymerase chain reaction gene patterns in urine sediment distinguish prostate cancer patients from control subjects. J Clin Oncol. 2005;23:6569–6575. [PubMed]
27. Herman JG, Graff JR, Myohanen S, Nelkin BD, Baylin SB. Methylation-specific PCR: a novel PCR assay for methylation status of CpG islands. Proc Natl Acad Sci U S A. 1996;93:9821–9826. [PMC free article] [PubMed]
28. Harden SV, Guo Z, Epstein JI, Sidransky D. Quantitative GSTP1 methylation clearly distinguishes benign prostatic tissue and limited prostate adenocarcinoma. J Urol. 2003;169:1138–1142. [PubMed]
29. Tukey JW. Exploratory data analysis. Reading (MA): Addison-Wesley; 1977.
30. Cox DR. The analysis of binary data. London: Meuthuen; 1970.
31. SAS/STAT users guide. Vol. 2. Cary: SAS Institute; 1999.
32. Kaplan EL, Meier P. Nonparametric estimation from incomplete observations. J Am Stat Assoc. 1958;53:457–480.
33. Mantel N, Haenszel W. Statistical aspects of the analysis of data from retrospective studies of disease. J Natl Cancer Inst. 1959;22:719–748. [PubMed]
34. Cox DR. Regression models and life-tables [with discussion] J R Stat Soc. 1972;34:187–220.
35. Team RDC. R: a language and environment for statistical computing. Viena: R Foundation for Statistical Computing. 2004
36. Esteller M, Corn PG, Baylin SB, Herman JG. A gene hypermethylation profile of human cancer. Cancer Res. 2001;61:3225–3229. [PubMed]
37. Costello JF, Fruhwald MC, Smiraglia DJ, et al. Aberrant CpG-island methylation has non-random and tumour-type-specific patterns. Nat Genet. 2000;24:132–138. [PubMed]
38. Hoque MO, Begum S, Brait M, et al. Tissue inhibitor of metalloproteinases-3 promoter methylation is an independent prognostic factor for bladder cancer. J Urol. 2008;179:743–747. [PMC free article] [PubMed]
39. Jeronimo C, Henrique R, Hoque MO, et al. A quantitative promoter methylation profile of prostate cancer. Clin Cancer Res. 2004;10:8472–8478. [PubMed]
40. Pu RT, Laitala LE, Clark DP. Methylation profiling of urothelial carcinoma in bladder biopsy and urine. Acta Cytol. 2006;50:499–506. [PubMed]
41. Virmani AK, Rathi A, Sathyanarayana UG, et al. Aberrant methylation of the adenomatous polyposis coli (APC) gene promoter 1A in breast and lung carcinomas. Clin Cancer Res. 2001;7:1998–2004. [PubMed]
42. Virmani AK, Tsou JA, Siegmund KD, et al. Hierarchical clustering of lung cancer cell lines using DNA methylation markers. Cancer Epidemiol Biomarkers Prev. 2002;11:291–297. [PubMed]
43. Kitkumthorn N, Yanatatsanajit P, Kiatpongsan S, et al. Cyclin A1 promoter hypermethylation in human papillomavirus-associated cervical cancer. BMC Cancer. 2006;6:55. [PMC free article] [PubMed]
44. Tokumaru Y, Yamashita K, Osada M, et al. Inverse correlation between cyclin A1 hypermethylation and p53 mutation in head and neck cancer identified by reversal of epigenetic silencing. Cancer Res. 2004;64:5982–5987. [PubMed]
45. Yamashita K, Park HL, Kim MS, et al. PGP9.5 methylation in diffuse-type gastric cancer. Cancer Res. 2006;66:3921–3927. [PubMed]
46. Esteller M. Cancer epigenomics: DNA methylomes and histone-modification maps. Nat Rev Genet. 2007;8:286–298. [PubMed]
47. Maxwell SA, Davis GE. Differential gene expression in p53-mediated apoptosis-resistant vs. apoptosis-sensitive tumor cell lines. Proc Natl Acad Sci U S A. 2000;97:13009–13014. [PMC free article] [PubMed]
48. Hoque MO, Feng Q, Toure P, et al. Detection of aberrant methylation of four genes in plasma DNA for the detection of breast cancer. J Clin Oncol. 2006;24:4262–4269. [PubMed]
49. Mandelker DL, Yamashita K, Tokumaru Y, et al. PGP9.5 promoter methylation is an independent prognostic factor for esophageal squamous cell carcinoma. Cancer Res. 2005;65:4963–4968. [PubMed]
50. Bittencourt Rosas SL, Caballero OL, Dong SM, da Costa Carvalho Mda G, Sidransky D, Jen J. Methylation status in the promoter region of the human PGP9.5 gene in cancer and normal tissues. Cancer Lett. 2001;170:73–79. [PubMed]
51. Lee YM, Lee JY, Kim MJ, et al. Hypomethylation of the protein gene product 9.5 promoter region in gallbladder cancer and its relationship with clinicopathological features. Cancer Sci. 2006;97:1205–1210. [PubMed]
52. Dhillon VS, Shahid M, Husain SA. CpG methylation of the FHIT, FANCF, cyclin-D2, BRCA2 and RUNX3 genes in granulosa cell tumors (GCTs) of ovarian origin. Mol Cancer. 2004;3:33. [PMC free article] [PubMed]
53. Weisenberger DJ, Siegmund KD, Campan M, et al. CpG island methylator phenotype underlies sporadic microsatellite instability and is tightly associated with BRAF mutation in colorectal cancer. Nat Genet. 2006;38:787–793. [PubMed]
54. Ribic CM, Sargent DJ, Moore MJ, et al. Tumor microsatellite-instability status as a predictor of benefit from fluorouracil-based adjuvant chemotherapy for colon cancer. N Engl J Med. 2003;349:247–257. [PMC free article] [PubMed]
55. Hegi ME, Diserens AC, Gorlia T, et al. MGMT gene silencing and benefit from temozolomide in glioblastoma. N Engl J Med. 2005;352:997–1003. [PubMed]
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