Introduction

MCO

Molecular and Clinical Oncology

2049-9450 2049-9469

D.A. Spandidos

10.3892/mco.2016.959

MCO-0-0-959

Articles

DNA methylation and leukemia susceptibility in China: Evidence from an updated meta-analysis

Jiang

Danjie

1 * Li

Yirun

1 * Hong

Qingxiao

1 Shen

Yusheng

1 Xu

Chunjing

1 Xu

Yan

1 Zhu

Huangkai

1 Dai

Dongjun

1 Ouyang

Guifang

2 Duan

Shiwei

1Medical Genetics Center, School of Medicine, Ningbo University, Ningbo, Zhejiang 315211, P.R. China 2Department of Hematology, Ningbo First Hospital, Ningbo, Zhejiang 315010, P.R. China

Correspondence to: Dr Shiwei Duan, Medical Genetics Center, School of Medicine, Ningbo University, 818 Fenghua Road, Ningbo, Zhejiang 315211, P.R. China, Email: duanshiwei@nbu.edu.cn Dr Guifang Ouyang, Department of Hematology, Ningbo First Hospital, 59 Liuting Road, Ningbo, Zhejiang 315010, P.R. China, Email: ouyangguifang2000@aliyun.com *

Contributed equally

09 2016

12 07 2016

5 3 193 207 14112014 20052016

2016

Mounting evidence supports a role for DNA methylation in the pathogenesis of leukemia; however, there no overview of these results in the Chinese population. The present study performed a comprehensive meta-analysis to establish candidate genes with an altered methylation status in Chinese leukemia patients. Eligible studies were identified through searching the National Center of Biotechnology Information PubMed and Wanfang databases. Studies were pooled and overall odds ratios with corresponding confidence intervals were calculated. A total of 4,325 leukemia patients and 2,010 controls from 94 studies on 53 genes were included in this meta-analysis, and 47 genes were found to be aberrantly methylated in leukemia patients. A further subgroup meta-analysis by leukemia subtype demonstrated that hypermethylation of 5 genes, namely cyclin-dependent kinase (CDKN)2A, DNA-binding protein inhibitor-4, CDKN2B, glioma pathogenesis-related protein 1 and p73, contributed to the risk of various subtypes of leukemia. In addition, a strong association between CDKN2A and leukemia was identified in Chinese (P<0.00001) but not in European patients. The aberrantly methylated genes identified in the present meta-analysis may help elucidate the mechanisms underlying the development of leukemia in Chinese patients.

leukemia DNA methylation epigenetics ethnic difference Chinese population

Introduction

Leukemia is a complex hematological malignancy, characterised by clonal proliferation of malignant hematopoietic stem cells in the blood and bone marrow (1), with a total of 350,000 new cases and 25,700 deaths annually (2). Genetic as well as environmental factors have been suggested to be associated with leukemia, including trisomy 21, gender, cytotoxicity of anticancer drugs, exposure to benzene and ionising radiation (3–6). Leukemia is a heterogeneous disease that comprises acute lymphocytic leukemia (ALL), chronic lymphocytic leukemia (CLL), acute myelogenous leukemia (AML) and chronic myelogenous leukemia (CML). ALL accounts for 81% of childhood leukemia cases, while CLL and AML frequently occur in adults (7).

Racial and ethnic disparities have been identified in the expression of leukemia-related genes, the clinical outcome and the mortality rate of leukemia (8–12). These disparities are likely due to a combination of genetic, environmental and socioeconomic factors (13), which may affect epigenetic changes. Epigenetics, such as DNA methylation, have been shown to play an important role in cancer susceptibility (14,15). Therefore, DNA methylation studies may help elucidate these racial and ethnic disparities in leukemia patients.

Aberrant DNA methylation of genes has been shown to be associated with a large number of human malignancies (16,17). Although a recent meta-analysis by our group identified significant associations between a number of aberrantly methylated genes and leukemia (18), the majority of the studies published in the Chinese language are overlooked. Thus, the aim of the present study was to focus on the association of aberrant DNA methylation and leukemia susceptibility in the Chinese population and to investigate ethnic differences in DNA methylation using subgroup meta-analyses.

Materials and methods <sec> <title>Selection of studies

A systematic literature search was performed through the National Center for Biotechnology Information (NCBI) PubMed and Wanfang literature databases, updated until July 10, 2014. The search was performed using the keywords ‘leukemia’ and ‘methylation’. Potentially relevant articles were identified by their titles and abstracts, followed by selection of eligible studies based on full-text analysis. Case-control studies on gene methylation in Chinese leukemia patients containing sufficient information on methylation to calculate the odds ratios (ORs) and 95% confidence intervals (CIs) were considered to be eligible. A flow chart of the study selection process is shown in Fig. 1.

Data extraction

The following characteristics were extracted from each eligible study: First author's name, year of publication, disease category and methylation status of cases and controls. All the studies included were reviewed by three authors (D.J., Y.S. and C.X.). For genes with methylation data in other populations, the corresponding data were retrieved and subjected to meta-analyses for comparison with the Chinese population.

Statistical analysis

Review Manager 5.0 software (The Nordic Cochrane Centre, The Cochrane Collaboration, Copenhagen, Denmark) was used for the meta-analysis. The ORs and 95% CIs were calculated to evaluate the association between gene methylation and leukemia. Heterogeneity of the included studies was assessed using I² statistics (19). When there was significant heterogeneity (I²>50%), the random-effects model was used to calculate the overall OR and 95% CI; otherwise, the fixed-effects model was applied (20).

Results <sec> <title>Eligible studies

As shown in Fig. 1, 1,477 potentially relevant studies were identified for initial review from the NCBI PubMed and Wanfang literature databases. A total of 1,380 studies were excluded (1,036 irrelevant studies, 227 non-case-control studies and 117 studies with insufficient data). Finally, a total of 94 studies (61 studies on ALL, 76 on AML, 11 on CLL, 31 on CML and 3 other studies on leukemia), were included in the present meta-analysis (31,38,44–135). A total of 53 genes were identified among 4,325 leukemia patients and 2,010 control subjects, of which 19 were reported by only 1 study, 15 by 2 studies and 19 by ≥3 studies (Tables I and II). For 47 of these 53 genes, aberrant methylation was proved to be significantly associated with leukemia.

Meta-analysis of the association between cyclin-dependent kinase (CDKN)2A methylation and leukemia

As shown in Fig. 2, 566 cases and 361 controls were included in the meta-analysis of CDKN2A methylation. The results indicated that hypermethylation of CDKN2A was a risk factor for leukemia (P<0.00001; OR=19.99; 95% CI: 11.37–35.17). Subgroup analysis by type of leukemia revealed that hypermethylation of CDKN2A was associated with an increased risk of AML (P<0.00001; OR=17.86; 95% CI: 7.79–40.93), ALL (P<0.00001; OR=24.01; 95% CI: 10.23–56.33) and CLL (P=0.04; OR=15.95; 95% CI: 1.16–218.94), but not of CML (P=0.08). However, there was no significant difference in the association results among different leukemia types (P=0.87).

Meta-analysis of the association between CDKN2B methylation and leukemia

The meta-analysis of the association between CDKN2B methylation and leukemia included 24 studies comprising 463 cases and 302 controls (Fig. 3). The results revealed that hypermethylation of the CDKN2B gene was associated with the risk of leukemia (P<0.00001; OR=42.45; 95% CI=22.98–78.42). These 24 studies included 12 studies on ALL, 10 studies on AML and 2 studies on CML. A subtype meta-analysis revealed that CDKN2B promoter methylation was a risk factor for AML (P<0.00001; OR=54.11; 95% CI: 21.07–138.93), ALL (P<0.00001; OR=35.76; 95% CI: 14.92–85.69) and CML (P=0.004; OR=27.06; 95% CI: 2.88–254.55). There was no significant difference in the association results among different leukemia types (P=0.76).

Meta-analysis of the association between DNA-binding protein inhibitor-4 (ID4) methylation and leukemia

A total of 10 studies were included in the ID4 methylation analysis (Fig. 4). The meta-analysis revealed that ID4 methylation was a risk factor for leukemia (P<0.00001; OR=70.08; 95% CI: 24.12–203.64). Hypermethylation of the ID4 gene was associated with an increased risk of AML (P<0.00001; OR=116.32; 95% CI: 25.40–532.59), ALL (P<0.00001; OR=104.68; 95% CI: 17.27–634.39), CML (P=0.002; OR=20.17; 95% CI: 3.05–133.21) and CLL (P=0.002; OR=693.00; 95% CI: 11.87–40460.96). There was no significant difference in the association results among different leukemia types (P=0.33).

Meta-analysis of the association between glioma pathogenesis-related protein 1 (GliPR1) methylation and leukemia

As shown in Fig. 5, the meta-analysis of the association between GliPR1 methylation and leukemia included 9 studies. The results revealed that GliPR1 methylation was a risk factor for leukemia (P<0.00001; OR=6.45; 95% CI: 2.88–14.45). Hypermethylation of the GliPR1 gene was associated with an increased risk of AML (P<0.00001; OR=30.33; 95% CI: 15.83–58.11), ALL (P<0.0001; OR=3.39; 95% CI: 1.88–6.13) and CML (P=0.006; OR=2.49; 95% CI: 1.30–4.77). Moreover, there was a significant difference in the association of GliPR1 hypermethylation with the risk of leukemia among the different subtypes (P<0.00001).

Meta-analysis of the association between p73 methylation and leukemia

The meta-analysis of p73 methylation included 7 case-control studies (Fig. 6). The results revealed that hypermethylation of p73 was associated with an increased risk of leukemia (P<0.00001; OR=17.07; 95% CI: 6.20–47.02). In addition, the results showed that p73 methylation was a risk factor for AML (P=0.002; OR=20.83; 95% CI: 3.01–143.95) and ALL (P<0.00001; OR=15.92; 95% CI: 4.87–52.07), while there was no significant difference between the two subtypes.

Subgroup meta-analysis of gene methylation and leukemia by ethnicity

Based on our previous study (18), a further subgroup meta-analysis by ethnicity was performed for CDKN2A and CDKN2B methylation. Hypermethylation of CDKN2A and CDKN2B was associated with an increased risk of leukemia in Chinese populations (P<0.00001), while only CDKN2B was associated with leukemia in Europeans (P=0.007) (Figs. 7 and 8). Of note, there was a significant difference between European and Chinese populations regarding the association of CDKN2A and CDKN2B methylation with leukemia (P<0.00001 and P=0.02, respectively).

Discussion

In the present study, eligible studies were retrieved from the NCBI PubMed and Wanfang literature databases and a systematic meta-analysis was performed to investigate the association between the methylation status of 53 genes and leukemia, with the aim of providing evidence regarding the role of gene methylation in the pathogenesis of leukemia, particularly in different leukemia subgroups and ethnic groups.

Aberrant gene promoter methylation, occurring in almost every tumor type, is one of several mechanisms of gene inactivation (21). Promoter hypermethylation of tumor suppressor genes often contributes to loss of function and cancer development (22,23). One potential mechanism for hypermethylation-induced silencing is changing the structure of specific binding sites for certain transcriptional regulators (24). Epigenetic silencing of genes by promoter hypermethylation is associated with the loss of tumor suppression, increasing tumor severity and reducing patient survival (25). The present meta-analysis revealed significant changes in the methylation status of the CDKN2A, CDKN2B, ID4, GliPR1, p73 and Wilms' tumor 1 (WT1) genes in the major types of leukemia (21,23,26–28).

Numerous studies revealed that CDKN2A and CDKN2B methylation is frequent during malignant transformation (29–31). As tumor suppressors, CDKN2A and CDKN2B generate 3 transcript variants (p16^INK4A, p14^ARF and p15^INK2B) according to differences in the first exons and control the progression of cells from the G1 to the S phase (29,125). The present meta-analysis demonstrated that hypermethylation of CDKN2A and CDKN2B are risk factors for leukemia. According to the subgroup meta-analysis, hypermethylation of CDKN2A was significantly associated with AML, ALL and CLL, but not with CML, while CDKN2B hypermethylation was significantly associated with AML, ALL and CML. The lack of association of CDKN2A with CML may be attributed to the limited sample included in the the meta-analyses (CML power, 6.4%; and CLL power, 6.3%).

The ID4 protein is a member of the dominant-negative basic helix-loop-helix transcription factor family that lacks DNA-binding activity (126) and has a tumor suppressor function. The promoter of ID4 was reported to be consistently methylated to various degrees in CLL and a univariate analysis demonstrated that increased promoter methylation of ID4 was correlated with shortened patient survival (127). Previous studies also reported that ID4 gene promoter hypermethylation was highly correlated with acute leukemia and may reflect the malignant degree of AML (128,129). The results of the present meta-analysis demonstrated that methylation of the ID4 gene was associated with an increased risk of leukemia, particularly CML.

The GliPR1 protein, encoded by the GliPR1 gene, has been identified as an epigenetically regulated tumor suppressor in prostate cancer and AML. GliPR1 may serve as a marker for monitoring disease activity in AML patients during therapy (61,130). Moreover, GliPR1 expression was found to be significantly increased in bone marrow samples of AML patients, while being markedly reduced in ALL, unchanged in myelodysplastic syndrome and marginally decreased in CLL and CML (131). The present meta-analysis identified hypermethylation of the GliPR1 promoter as a risk factor for leukemia in the Chinese population.

p73, a homologue of the p53 tumor suppressor family, is involved in neurogenesis, sensory pathways, immunity, inflammation and tumorigenesis (132). Furthermore, p73 hypermethylation resulting in its deactivation is frequently observed in malignant lymphoproliferative disorders, particularly ALL (21). In line with these results, the present meta-analysis also identified p73 hypermethylation as a risk factor for leukemia in the Chinese population.

The WT1 gene encodes a zinc finger transcription factor that is an RNA-binding protein with important roles in the development of several organs and tissues. WT1 has been reported to have tumor suppressor as well as oncogenic activity; however. the reasons and mechanisms underlying these opposing functions remain to be fully elucidated (133). The present study demonstrated that WT1 hypermethylation played a protective role against the progression of leukemia.

Previous studies have reported that the risk of hematological malignancies varies significantly among different ethnic groups (9,13,134,135). The present meta-analysis indicated that there was no association between CDKN2A methylation and the risk of leukemia (P=0.16) in Europeans, while a significant association was observed in Chinese populations (P<0.00001). A significant difference in the association of CDKN2A methylation with leukemia was observed between European and Chinese populations (P<0.00001). This result may provide molecular evidence to guide future individualization of chemotherapy for leukemia, although further research is required to elucidate the precise nature of the ethnic differences in leukemia.

Of note, the present meta-analysis had certain limitations. First, the numbers of the studies regarding each gene and leukemia subtype were uneven. For certain leukemia subtypes, only a few studies on certain genes were available. The lack of association of the methylation status of certain genes with several leukemia subtypes may have been due to a lack of statistical power of the respective studies, so that the negative results must be interpreted with caution. Furthermore, a language bias was present, as only studies written in Chinese and English were included.

In conclusion, the present meta-analysis revealed that aberrant DNA methylation of the promoters of 47 genes was associated with leukemia. Further subgroup meta-analysis revealed 5 hypermethylated genes (CDKN2A, CDKN2B, ID4, GliPR1 and p73) in various leukemia subtypes. In addition, a difference in the association of CDKN2A and CDKN2B hypermethylation with leukemia was identified between Chinese and European populations. The results of the present study may enhance the current understanding of the association of DNA methylation with leukemia in the Chinese population.

Acknowledgements

The present study was supported by grants from National Natural Science Foundation of China (nos. 31100919 and 81371469), Natural Science Foundation of Zhejiang Province (no. LR13H020003), K.C. Wong Magna Fund at Ningbo University and Ningbo Social Development Research Projects (nos. 2010C50019 and 2012C50032).

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Cai

RIZ1 gene expression and promoter methylation status in acute childhood leukemia

PhD dissertation, Huazhong University Science and Technology2012

Shi

Fan

Zhou

Study of abnormal methylation in the promoter region of SFRP4 gene in acute myeloid leukemia

Chin J Clin, Electron Ed.59909952011

Dou

Liu

Wang

[Study on the involvement of ZO-1 gene in leukemogenesis]

Chin J Hematol304734762009

Wang

Tan

Liu

The methylation pattern and clinical significance of Zonula occludens-1 gene promoter in acute leukemia

Chin J Intern Med471111132008

Chim

Wong

Pang

Aberrant promoter methylation of the retinoic acid receptor alpha gene in acute promyelocytic leukemia

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10.1038/sj.leu.2403937

16239915

Wang

Liu

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Promoter methylation profile of androgen receptor gene in leukemic cells

J Third Mil Med Univ299329342007

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Guo

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Methylation Status of JunB and CDH13 gene promoter in CD34 CD38⁻chronic myelogenous leukemia cells

J Exp Hematol17140514082009

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CDH13 gene promoter methylation and expression in acute myeloid leukemia

Master's degree dissertation, Guangzhou Medical University2013

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The study of DDIT3 gene promoter methylation in hematological tumors

Master's degree dissertation, Jiangsu University2009

Zhu

Wan

Analysis of methylation of the Dickkopf1 (DKK-1) gene in acute leukemia

J Shandong Univ (Health Sci)5084872012

Yuan

The study of EDNRB gene promoter methylation in acute leukemia

Master's degree dissertation, Zhengzhou University2010

The study of FANCF methylation in patients with acute myeloid leukemia and myelodysplastic syndrome

Master's degree dissertation, China Medical University2008

Deng

The study of FANCF/FANCD2 expression in patients with acute myeloid leukemia and myelodysplastic syndrome

Master's degree dissertation, China Medical University2009

100

Qian

The significance of GRAF gene promoter methylation in hematopoietic tumors

Master's degree dissertation, Jiangsu University2010

101

Chen

HAGE gene promoter hypomethylation in myeloid tumors

Master's degree dissertation, Jiangsu University2012

102

Wang

Zhou

Fang

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Analysis and clinical significance of hPer3 promoter methylation status for CML monitoring

Chin J Pathophysiol27211121152011

103

Wang

Zhou

Promoter methylation status of hPer3 gene in AML patients and the in vitro effect of decitabine on the status

Chin J Hematol323173212011

104

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MiR-34b expression, methylation regulation and clinical significance in acute childhood leukemia

Master's degree dissertation, Suzhou University2013

105

Chai

RAGE-1 gene promoter hypomethylation in myeloid tumors

Master's degree dissertation, Shanxi Medical University2013

106

Lin

Fan

Liu

Significance of DNA methylation status of runx3 gene promoter region in acute leukemia

J Exp Hematol162632662008

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Chim

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Kwong

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[Study of WIF-1 promoter methylation with expressions of β-catenin in acute leukemia]

Chin Med J91285828602011

109

Liu

Guan

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Aberrant promoter hypermethylation of AKAP12 gene in acute lymphoblastic leukemia in children

Lab Med233443482008

110

Gao

Liu

Studies on gene expression and the 5′ CpG islands methylation status of E-cadherin in acute myeloid leukemia

Chin J Hematol2725272006

111

Yao

Qian

Lin

Aberrant methylation of CCAAT/enhancer binding protein zeta promoter in acute myeloid leukemia

Leuk Res359579602011

10.1016/j.leukres.2010.10.031

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112

Guan

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Identification of aberrant methylation of DLC-1 by pyrosequencing in acute lymphoblastic leukemia in children

Chin J Lab Med313893932008

113

Wang

Shi

Jin

Study on aberrant DNA methylation of dopamine D4 receptor gene in acute myeloid leukemia

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Abnormal methylation of the promoter region of the p53 gene in acute leukemia

Master's degree dissertation, Sun Yat-sen University2007

116

Qian

Chen

Expression pattern and promoter hypermethylation of PDLIM4 gene in patients with chronic myeloid leukemia

J Jiangsu Univ (Med Ed.)2317212013

117

Song

Liu

Epigenetic inactivation of PLCD1 in chronic myeloid leukemia

Int J Mol Med301791842012

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Yao

Qian

Lin

Chen

Alteration of methylation status of PRAME gene promoter in patients with chronic myeloid leukemia

J Pract Med29349534982013

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Yan

Yang

Expression and methylation study of PRDX2 gene in acute myeloid leukemia

J China Med Univ41104710492012

120

Yang

Liu

Zheng

Huang

Research on PTEN expression and its promoter methylation in B-ALL patients

J Intern Intens Med131411432007

121

Mao

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The clinical significance of the RIL gene methylation levels in acute myeloid leukemia

China J Mod Med2346512013

122

Jiao

Chen

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SALL4 genes methylation changes and clinical significance in acute myeloid leukemia patients

J Jiangsu Univ (Med Ed.)235325352013

123

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The methylation of SOCS-1 gene in adult acute myeloid leukemia

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22134481

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ID4 methylation patterns in childhood T line and B line lymphocytic leukemia

Chin J Contemp Pediatr129409422010(In Chinese)

127

Chen

Claus

Lucas

Silencing of the inhibitor of DNA binding protein 4 (ID4) contributes to the pathogenesis of mouse and human CLL

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10.1182/blood-2010-05-284638

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Kang

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Zhu

Establishment of methylation-specific quantitative PCR system for ID4 gene in acute leukemia cells and its specificity and sensitivity

J Exp Hematol222692742014(In Chinese)

129

Liu

Study on the correlation between Chinese medical syndrome types and ID4 gene promoter methylation in human acute myeloid leukemia

Chin J Integr Tradit West Med324714732012(In Chinese)

130

Awasthi

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10.3389/fonc.2013.00225

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Endoplasmic reticulum protein GliPR1 regulates G protein signaling and the cell cycle and is overexpressed in AML

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24008279

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Agostini

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10.1038/cdd.2013.104

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133

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Mechanisms of transcriptional regulation by WT1 (Wilms tumour 1)

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10.1042/BJ20131587

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134

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Figure 1.

Flow diagram of the study selection process.

Figure 2.

Meta-analyses of aberrantly methylated cyclin-dependent kinase 2A gene in leukemia. ALL, acute lymphocytic leukemia; CLL, chronic lymphocytic leukemia; AML, acute myelogenous leukemia; CML, chronic myelogenous leukemia; M-H, Mantel-Haenszel model; CI, confidence interval; df, degree of freedom.

Figure 3.

Meta-analyses of aberrantly methylated cyclin-dependent kinase 2B gene in leukemia. ALL, acute lymphocytic leukemia; AML, acute myelogenous leukemia; CML, chronic myelogenous leukemia; M-H, Mantel-Haenszel model; CI, confidence interval; df, degree of freedom.

Figure 4.

Meta-analyses of aberrantly methylated DNA-binding protein inhibitor-4 gene in leukemia. ALL, acute lymphocytic leukemia; CLL, chronic lymphocytic leukemia; AML, acute myelogenous leukemia; CML, chronic myelogenous leukemia; M-H, Mantel-Haenszel model; CI, confidence interval; df, degree of freedom.

Figure 5.

Meta-analyses of aberrantly methylated glioma pathogenesis-related protein 1 gene in leukemia. ALL, acute lymphocytic leukemia; AML, acute myelogenous leukemia; CML, chronic myelogenous leukemia; M-H, Mantel-Haenszel model; CI, confidence interval; df, degree of freedom.

Figure 6.

Meta-analyses of aberrantly methylated p73 in leukemia. ALL, acute lymphocytic leukemia; AML, acute myelogenous leukemia; M-H, Mantel-Haenszel model; CI, confidence interval; df, degree of freedom.

Figure 7.

Meta-analyses of aberrantly methylated CDKN2A in Asian and European populations. ALL, acute lymphocytic leukemia; CLL, chronic lymphocytic leukemia; AML, acute myelogenous leukemia; CML, chronic myelogenous leukemia; M-H, Mantel-Haenszel model; CI, confidence interval; df, degree of freedom.

Figure 8.

Meta-analyses of aberrantly methylated cyclin-dependent kinase 2B gene in Chinese and European populations. ALL, acute lymphocytic leukemia; AML, acute myelogenous leukemia; CML, chronic myelogenous leukemia; M-H, Mantel-Haenszel model; CI, confidence interval; df, degree of freedom.

Table I.

Eligible case-control leukemia studies in the Chinese population.

Gene	No. of studies	Overall OR (95% CI)
CDKN2A	26	19.99 (11.37–35.17)
CDKN2B	24	42.45 (22.98–78.42)
ID4	10	70.08 (24.12–203.64)
GliPR1	9	6.45 (2.88–14.45)
p73	7	17.07 (6.20–47.02)
CT	6	46.85 (12.15–180.65)
DAPK	6	17.19 (5.43–54.41)
PRB	6	32.10 (9.19–112.16)
SFRP5	5	11.45 (3.19–41.12)
IGSF4	4	14.41 (3.21–64.72)
PRA	4	38.11 (8.14–178.29)
RASSF1A	4	22.62 (5.11–100.17)
SFRP2	4	30.28 (7.18–127.74)
LRP15	3	41.45 (6.63–259.10)
RIZ1	3	9.86 (1.84–52.78)
SFRP4	3	14.31 (2.77–73.90)
WT1	3	0.24 (0.10–0.54)
ZO-1	3	99.65 (18.14–547.54)
RARA	3	3.46 (0.65–18.39)
AR	2	98.28 (5.22–1849.18)
CDH13	2	10.18 (1.71–60.56)
DDIT3	2	41.35 (5.41–316.24)
DKK-1	2	24.28 (3.15–187.26)
EDNRB	2	32.57 (3.92–270.80)
FANCF	2	7.09 (3.71–13.53)
GRAF	2	64.36 (8.67–477.53)
HAGE	2	12.23 (1.66–90.39)
hPER3	2	85.56 (10.95–668.76)
miR-34B	2	74.99 (9.65–582.64)
RAGE-1	2	38.88 (5.25–287.87)
RUNX3	2	11.91 (1.45–97.86)
SFRP1	2	23.65 (3.05–183.57)
SHP1	2	11.05 (1.41–86.28)
WIF1	2	14.15 (1.78–112.81)
AKAP12	1	34.44 (1.85–640.43)
CDH1	1	33.00 (1.78–610.61)
CEBPZ	1	35.84 (2.12–604.80)
DRD4	1	27.26 (1.44–516.59)
E-cad	1	33.00 (1.78–610.61)
JUNB	1	55.00 (1.86–1622.60)
MT3	1	5.76 (1.17–28.24)
PLCD1	1	39.38 (2.21–702.41)
PRAME	1	42.49 (2.45–737.45)
PRDX2	1	36.82 (2.14–633.67)
RIL	1	197.19 (11.04–3523.69)
SOCS-1	1	34.26 (1.79–654.46)
WNT5A	1	121.51 (7.08–2085.83)
WWOX	1	41.70 (2.43–715.18)
DLC-1	1	17.52 (0.90–342.83)
p53	1	19.46 (0.92–411.20)
PDLIM4	1	14.23 (0.81–249.59)
PTEN	1	16.03 (0.83–308.79)
SALL4	1	10.67 (0.59–192.94)

OR, odds ratio; CI, confidence interval.

Table II.

List of methylated genes and associated case-control studies.

				Cases (n)		Controls (n)

Authors	Year	Gene	Disease	Meth	Total	Meth	Total	Refs.
Hsiao et al	2008	CDKN2A	ALL	11	13	0	8	(31)
Hsiao et al	2008	CDKN2A	CLL	1	1	0	8	(31)
Hsiao et al	2008	CDKN2A	AML	5	6	0	8	(31)
Hsiao et al	2008	CDKN2A	CML	1	3	0	8	(31)
Zheng et al	2004	CDKN2A	ALL	12	20	0	20	(32)
Xiao et al	2010	CDKN2A	AML	7	21	0	16	(33)
Xiao et al	2010	CDKN2A	ALL	7	17	0	16	(33)
Xiao et al	2010	CDKN2A	CML	1	7	0	16	(33)
Xiao et al	2010	CDKN2A	CLL	1	6	0	16	(33)
Yang et al	2003	CDKN2A	ALL	5	28	0	20	(34)
Yang et al	2003	CDKN2A	AML	9	43	0	20	(34)
Song et al	2004	CDKN2A	ALL	5	28	0	20	(35)
Tan et al	2001	CDKN2A	AML	14	20	0	20	(36)
Zhu et al	2005	CDKN2A	ALL	8	19	0	10	(37)
Zhang et al	2000	CDKN2A	ALL	20	40	0	15	(38)
Fan et al	2007	CDKN2A	AML	24	58	0	16	(39)
Fan et al	2007	CDKN2A	ALL	8	24	0	16	(39)
Jiang et al	2002	CDKN2A	ALL	19	31	0	20	(40)
Jiang et al	2002	CDKN2A	AML	14	18	0	20	(40)
Meng et al	2005	CDKN2A	AML	3	26	0	10	(41)
Meng et al	2005	CDKN2A	ALL	2	14	0	10	(41)
Wang et al	2002	CDKN2A	ALL	11	15	0	12	(42)
Yin et al	2002	CDKN2A	ALL	6	15	0	12	(43)
Chen et al	2003	CDKN2A (HapII)	AML	11	31	0	8	(44)
Chen et al	2003	CDKN2A (NruI)	AML	22	31	2	8	(44)
Chen et al	2003	CDKN2A (SacII)	AML	19	31	1	8	(44)
Lin et al	2012	CDKN2B	ALL	17	25	0	10	(45)
Zheng et al	2004	CDKN2B	ALL	18	26	0	20	(32)
Chen et al	2003	CDKN2B	AML	16	31	0	8	(44)
Tan et al	2001	CDKN2B	AML	16	20	0	20	(36)
Zhu et al	2001	CDKN2B	ALL	12	21	0	10	(46)
Zhu et al	2005	CDKN2B	ALL	7	19	0	10	(37)
Shen et al	2002	CDKN2B	ALL	6	10	0	10	(47)
Shen et al	2002	CDKN2B	AML	10	25	0	10	(47)
Fan et al	2001	CDKN2B	CML	5	7	0	20	(48)
Tong et al	2004	CDKN2B	AML	5	10	0	10	(49)
Tong et al	2004	CDKN2B	ALL	4	10	0	10	(49)
Tong et al	2004	CDKN2B	CML	5	14	0	10	(49)
Guo et al	2000	CDKN2B	AML	26	31	0	30	(50)
Qiao et al	2005	CDKN2B	AML	34	42	0	14	(51)
Qiao et al	2005	CDKN2B	ALL	9	14	0	14	(51)
Chen et al	2000	CDKN2B	ALL	5	10	0	10	(52)
Yin et al	2003	CDKN2B	ALL	6	15	0	12	(53)
Yin et al	2003	CDKN2B	AML	17	22	0	12	(53)
Meng et al	2005	CDKN2B	AML	24	26	0	10	(41)
Meng et al	2005	CDKN2B	ALL	10	14	0	10	(41)
Wu et al	2013	CDKN2B	ALL	14	14	0	14	(54)
Wu et al	2013	CDKN2B	AML	6	14	0	14	(54)
Wang et al	2002	CDKN2B	ALL	7	10	0	7	(55)
Wang et al	2002	CDKN2B	AML	22	33	0	7	(55)
Zhao et al	2008	ID4	AML	15	32	0	18	(56)
Wang et al	2010	ID4	CML	6	48	0	10	(57)
Liu et al	2011	ID4	AML	39	46	0	10	(58)
Jie et al	2012	ID4	AML	21	23	1	20	(59)
Jie et al	2012	ID4	ALL	9	13	1	20	(59)
Jie et al	2012	ID4	CML	9	11	1	20	(59)
Zhao et al	2005	ID4	AML	21	25	0	49	(60)
Zhao et al	2005	ID4	CML	2	4	0	49	(60)
Zhao et al	2005	ID4	ALL	12	14	0	49	(60)
Zhao et al	2005	ID4	CLL	3	3	0	49	(60)
Xiao et al	2011	GLIPR1	AML	58	70	14	93	(61)
Xiao et al	2011	GLIPR1	CML	11	40	14	93	(61)
Xiao et al	2011	GLIPR1	ALL	22	57	14	93	(61)
Liang et al	2009	GLIPR1	AML	44	54	5	35	(62)
Liang et al	2009	GLIPR1	CML	11	40	5	35	(62)
Liang et al	2009	GLIPR1	ALL	18	48	5	35	(62)
Jie et al	2012	GLIPR1	AML	22	23	4	20	(59)
Jie et al	2012	GLIPR1	ALL	5	13	4	20	(59)
Jie et al	2012	GLIPR1	CML	6	11	4	20	(59)
Zhang et al	2010	p73	AML	1	30	1	123	(63)
Zhang et al	2010	p73	ALL	10	112	1	123	(63)
Zhang et al	2012	p73	AML	21	58	0	31	(64)
Wu et al	2008	p73	ALL	10	30	0	16	(65)
Liu et al	2005	p73	ALL	10	26	0	18	(66)
Xu et al	2005	p73	ALL	12	42	0	10	(67)
Yu et al	2014	p73	ALL	10	32	0	30	(68)
Xie et al	2003	CT	AML	25	31	0	14	(69)
Xie et al	2003	CT	CML	13	45	0	14	(69)
Tang et al	2001	CT	CLL	1	3	0	30	(70)
Tang et al	2001	CT	CML	8	10	0	30	(70)
Tang et al	2001	CT	ALL	12	14	0	30	(70)
Wang et al	1998	CT	CML	13	31	0	10	(71)
Qian et al	2010	DAPK	AML	82	112	0	15	(72)
Niu et al	2014	DAPK	AML	33	102	0	7	(73)
Niu et al	2014	DAPK	ALL	8	17	0	7	(73)
Zhao et al	2009	DAPK	AML	3	60	0	17	(74)
Zhao et al	2009	DAPK	ALL	16	55	0	17	(74)
Qian J	2008	DAPK	CML	25	49	0	13	(75)
Lin W	2010	PRB	CLL	18	27	0	15	(76)
Wu B	2008	PRB	CLL	5	9	0	5	(77)
Zhang et al	2003	PRB	ALL	6	11	0	10	(78)
Zhang et al	2003	PRB	CLL	6	8	0	10	(78)
Zhang et al	2003	PRB	AML	9	15	0	10	(78)
Zhang et al	2003	PRB	CML	6	10	0	10	(78)
Shi et al	2011	SFRP5	AML	10	99	1	70	(79)
Wang et al	2012	SFRP5	CML	3	3	0	6	(80)
Wang et al	2012	SFRP5	AML	4	7	0	6	(80)
Xu et al	2010	SFRP5	AML	6	59	0	20	(81)
Xu et al	2010	SFRP5	ALL	9	28	0	20	(81)
Li et al	2004	IGSF4	AML	16	29	0	8	(82)
Li et al	2004	IGSF4	ALL	12	21	0	8	(82)
Li et al	2004	IGSF4	CML	6	18	0	8	(82)
Li et al	2004	IGSF4	CLL	2	7	0	8	(82)
Zhang et al	2003	PRA	ALL	7	11	0	10	(78)
Zhang et al	2003	PRA	CLL	5	8	0	10	(78)
Zhang et al	2003	PRA	AML	10	15	0	10	(78)
Zhang et al	2003	PRA	CML	7	10	0	10	(78)
Chen et al	2012	RASSF1A	AML	2	24	0	60	(83)
Chen et al	2012	RASSF1A	CML	1	23	0	60	(83)
Chen et al	2012	RASSF1A	ALL	5	19	0	60	(83)
Chen et al	2012	RASSF1A	CLL	4	20	0	60	(83)
Song et al	2011	SFRP2	CML	25	38	0	13	(84)
Shi et al	2011	SFRP2	AML	27	99	0	70	(79)
Xu et al	2010	SFRP2	AML	14	59	0	20	(81)
Xu et al	2010	SFRP2	ALL	8	28	0	20	(81)
Dou et al	2004	LRP15	AML	37	53	0	9	(85)
Dou et al	2004	LRP15	ALL	15	20	0	9	(85)
Dou et al	2004	LRP15	CLL	1	2	0	9	(85)
Yao et al	2010	RIZ1	AML	11	37	0	15	(86)
Cai et al	2012	RIZ1	ALL	15	64	0	9	(87)
Cai et al	2012	RIZ1	AML	12	32	0	9	(87)
Shi et al	2011	SFRP4	AML	17	99	0	70	(88)
Xu et al	2010	SFRP4	AML	4	59	0	20	(81)
Xu et al	2010	SFRP4	ALL	7	28	0	20	(81)
Jie et al	2012	WT1	AML	8	23	15	20	(59)
Jie et al	2012	WT1	ALL	4	13	15	20	(59)
Jie et al	2012	WT1	CML	7	11	15	20	(59)
Dou et al	2009	ZO-1	Leukemia	7	10	0	10	(89)
Wang et al	2008	ZO-1	AML	32	52	0	40	(90)
Wang et al	2008	ZO-1	ALL	17	29	0	40	(90)
Chim et al	2005	RARA	APL	25	63	0	8	(91)
Chim et al	2005	RARA	AML	1	50	0	8	(91)
Chim et al	2005	RARA	ALL	1	25	0	8	(91)
Wang et al	2007	AR	ALL	4	4	0	3	(92)
Wang et al	2007	AR	AML	11	11	0	3	(92)
Wang et al	2009	CDH13	CML	4	8	0	5	(93)
Liu et al	2013	CDH13	AML	23	44	1	10	(94)
Wang et al	2009	DDIT3	AML	62	133	0	16	(95)
Wang et al	2009	DDIT3	CML	39	59	0	16	(95)
Zhu et al	2012	DKK-1	ALL	14	34	0	20	(96)
Zhu et al	2012	DKK-1	AML	10	31	0	20	(96)
Yuan et al	2010	EDNRB	AML	15	22	0	8	(97)
Yuan et al	2010	EDNRB	ALL	11	17	0	8	(97)
Yu et al	2008	FANCF	AML	41	58	7	20	(98)
Deng et al	2009	FANCF	AML	85	111	11	42	(99)
Qian et al	2010	GRAF	AML	87	132	0	20	(100)
Qian et al	2010	GRAF	CML	34	61	0	20	(100)
Chen et al	2012	HAGE	AML	32	214	0	24	(101)
Chen et al	2012	HAGE	CML	22	87	0	24	(101)
Li et al	2011	hPER3	CML	12	29	0	40	(102)
Wang et al	2011	hPER3	AML	116	206	0	40	(103)
Wang et al	2013	miR-34B	ALL	24	31	0	23	(104)
Wang et al	2013	miR-34B	AML	8	19	0	23	(104)
Chai et al	2013	RAGE-1	AML	52	121	0	25	(105)
Chai et al	2013	RAGE-1	CML	33	76	0	25	(105)
Lin et al	2008	RUNX3	AML	7	23	0	10	(106)
Lin et al	2008	RUNX3	ALL	7	17	0	10	(106)
Xu et al	2010	SFRP1	AML	20	59	0	20	(81)
Xu et al	2010	SFRP1	ALL	11	28	0	20	(81)
Chim et al	2004	SHP1	AML	26	50	0	8	(107)
Chim et al	2004	SHP1	ALL	6	25	0	8	(107)
Wang et al	2011	WIF1	AML	11	34	0	15	(108)
Wang et al	2011	WIF1	ALL	6	21	0	15	(108)
Liu et al	2008	AKAP12	ALL	20	32	0	10	(109)
Gao et al	2006	CDH1	AML	38	55	0	7	(110)
Yao et al	2011	CEBPZ	AML	62	133	0	20	(111)
Guan et al	2008	DLC-1	ALL	21	34	0	5	(112)
Yu et al	2000	DRD4	AML	16	27	0	9	(113)
Gao et al	2006	E-cad	AML	38	55	0	7	(110)
Wang et al	2009	JUNB	CML	7	8	0	5	(93)
Tao et al	2014	MT3	AML	16	41	2	20	(114)
Zheng et al	2007	p53	ALL	5	11	0	11	(115)
Li et al	2013	PDLIM4	CML	13	59	0	24	(116)
Song et al	2012	PLCD1	CML	23	41	0	15	(117)
Yao et al	2013	PRAME	CML	28	55	0	20	(118)
Yan et al	2012	PRDX2	AML	17	55	0	40	(119)
Yang et al	2007	PTEN	ALL	5	22	0	25	(120)
Du et al	2013	RIL	AML	50	60	0	20	(121)
Jiao et al	2013	SALL4	AML	9	45	0	20	(122)
Zhuang et al	2011	SOCS-1	AML	15	24	0	10	(123)
Deng et al	2011	WNT5A	Leukemia	47	68	0	27	(124)
Zhang et al	2012	WWOX	AML	23	58	0	31	(64)

ALL, acute lymphocytic leukemia; CLL, chronic lymphocytic leukemia; AML, acute myelogenous leukemia; CML, chronic myelogenous leukemia; Meth, methylated.