Oncotarget

Meta-Analysis:

CDKN2A methylation in esophageal cancer: a meta-analysis

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Oncotarget. 2017; 8:50071-50083. https://doi.org/10.18632/oncotarget.18412

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Chongchang Zhou, Jinyun Li and Qun Li _

Abstract

Chongchang Zhou1, Jinyun Li2 and Qun Li1

1Department of Otorhinolaryngology Head and Neck Surgery, Lihuili Hospital of Ningbo University, Ningbo 315040, Zhejiang, China

2Department of Medical Oncology, Affiliated Hospital of Ningbo University, Ningbo 315000, Zhejiang, China

Correspondence to:

Qun Li, email: 13566315517@163.com

Jinyun Li, email: woodrenemma@163.com

Keywords: CDKN2A, methylation, diagnosis, esophageal cancer, carcinogenesis

Received: January 31, 2017    Accepted: May 08, 2017    Published: June 08, 2017

ABSTRACT

CDKN2A is a tumor suppressor gene and is frequently inactivated in human cancers by hypermethylation of its promoter. However, the role and diagnostic value of CDKN2A methylation in esophageal cancer (EC) remains controversial. Therefore, we performed a meta-analysis, including data from 42 articles (2656 ECs, 612 precancerous lesions, and 2367 controls). A significant increase in the frequency of CDKN2A methylation was identified during EC carcinogenesis: cancer vs. controls, odds ratio (OR) = 12.60 (95 % CI, 8.90–17.85); cancer vs. precancerous lesions, OR = 2.89 (95% CI, 2.20–3.79); and precancerous lesions vs. controls, OR = 7.38, 95% (CI, 4.31–12.66). CDKN2A promoter methylation was associated with EC tumor grade (OR = 1.79; 95% CI, 1.20–2.67) and clinical stage (OR = 2.56; 95% CI, 1.33–4.92). Additionally, the sensitivity, specificity, and area under the summary receiver operating characteristic curve (AUC) for diagnosis of EC based on CDKN2A methylation were 0.52 (95% CI, 0.44–0.59), 0.96 (95% CI, 0.93–0.98), and 0.83 (95% CI, 0.79–0.86), respectively. AUCs for blood and tissue sample subgroups were 0.90 and 0.82, respectively. Our findings indicate that CDKN2A methylation has a vital role in EC tumorigenesis and could be a biomarker for early diagnosis of EC.


INTRODUCTION

Esophageal cancer (EC) is the eighth most common and the sixth most deadly cancer worldwide [1]. Esophageal adenocarcinoma (EAC) and esophageal squamous cell carcinoma (ESCC) are two major histologic subtypes of EC. EAC is more common in western Europe and north America, whereas ESCC is more prevalent in south-eastern and central Asia, particularly China and Japan [2]. In China alone, 477,900 newly diagnosed EC cases and 375,000 deaths from EC were projected to occur in 2015 [3]. Despite recent advances in combination therapy strategies, including surgery, chemotherapy, and radiotherapy, the prognosis for EC patients remains unsatisfactory, especially for advanced-stage patients, and the 5-year overall survival rate remains at less than 20% [4]. The rapidly increasing incidence, demanding treatment, and poor outcomes of EC highlight the need for effective potential biomarkers for early diagnosis, prognosis prediction, and novel therapeutic targets.

The etiology and pathogenesis of EC involve complicated interactions between epigenetic, genetic, and environmental factors [57]. DNA methylation is a common form of epigenetic modification, which has a crucial role in human malignancies, such as breast [8], lung [9], and gastric [10] cancers. Abnormal methylation in the promoter region of tumor suppressor genes is one of the most common mechanisms of modification, and results in target gene transcriptional silencing. Moreover, with the introduction of precise and convenient methods of detection, DNA methylation has become a credible potential biomarker for early detection and diagnosis of cancer [11].

The cyclin-dependent kinase inhibitor 2A (CDKN2A) gene on chromosome 9p21 is a classical tumor suppressor gene [12]. It is responsible for inhibiting various cyclin-dependent kinases and plays an important role in cell cycle regulation by decelerating cell cycle progression at the G1/S phase [13, 14]. Hypermethylation of the CDKN2A gene promoter region, resulting in its inactivation, has been reported in several types of malignancy, including lung [15], head and neck [16], hepatocellular [17], breast [18], and esophageal [19] cancers. Although they have been frequently investigated, the association between CDKN2A promoter methylation and EC and the role of this modification in EC carcinogenesis, remain controversial. For example, one study reported that the methylation rate at the CDKN2A promoter was similar in EC patients and healthy controls [20]; however, another investigation identified a differential frequency of CDKN2A promoter methylation between these two groups [21]. In addition, the value for EC diagnosis of testing for CDKN2A methylation, particularly using blood samples, has been less intensely investigated.

Here, we performed a meta-analysis to generate a quantitative estimate of the association of CDKN2A methylation with EC risk and its role in EC carcinogenesis. Furthermore, we assessed whether there were associations between CDKN2A promoter methylation and the clinical characteristics of EC patients. In addition, we comprehensively evaluated the diagnostic utility of CDKN2A methylation for EC, to assess the future potential applicability of CDKN2A methylation testing for the prevention, diagnosis, and therapy of EC.

RESULTS

Study characteristics

The flow chart of the selection process for inclusion of studies in our analysis is presented in Figure 1. A total of 423 articles were initially identified, with 421 identified by database, and two by manual searching. After a careful initial review of the titles and abstracts, 237 duplicate and 83 irrelevant articles were excluded. Next, we reviewed the full text of the remaining articles. Among these, 61 articles were excluded, since 22 were focused on cell lines or animals trials, three were reviews, and 36 lacked methylation data. Finally, 42 articles including 44 studies (41 case-control and 3 cohort studies) were included in the meta-analysis. The basic characteristics of all eligible studies are presented in Table 1.

Flow diagram of the study search strategy for this meta-analysis.

Figure 1: Flow diagram of the study search strategy for this meta-analysis.

Table 1: The main characteristics of studies included in the analysis of the association of CDKN2A promoter methylation with esophageal cancer

First author

Year

Country

Ethnicity

Method

Histology

Type

Carcinoma

Normal

Pre-carcinoma

M+

Total

M+

Total

Source

M+

Total

Wang

2011

China

Asian

MSP

ESCC

Tissue

6

13

0

12

H

14

64

Song

2009

China

Asian

MSP

ESCC

Tissue

41

130

18

260

A

Na

Na

Song

2007

China

Asian

MSP

ESCC

Tissue

65

140

31

280

A

Na

Na

Yan

2003

China

Asian

MSP

ESCC

Tissue

5

34

0

6

H

Na

Na

Guo

2008

China

Asian

MSP

ESCC

Tissue

35

51

1

10

H

18

44

Yu

2006

China

Asian

MSP

ESCC

Tissue

33

45

26

45

A

Na

Na

Guo

2006

China

Asian

MSP

ESCC

Blood

14

51

0

10

H

0

44

Zhao

2011

China

Asian

MSP

ESCC

Tissue

31

71

10

71

A

Na

Na

2

71

H

Na

Na

Wang

2012

China

Asian

qMSP

ESCC

Tissue

66

76

3

76

A

Na

Na

Wang

2012

China

Asian

qMSP

ESCC

Blood

54

76

2

60

H

Na

Na

Yao

2005

China

Asian

nMSP

ESCC

Blood

34

56

0

22

H

Na

Na

Wang

1997

USA

Caucasian

MSP

EAC

Tissue

5

11

0

21

A

3

10

Kempste

2000

Australia

Caucasian

MSP

ESCC

Tissue

3

7

0

7

A

Na

Na

EAC

Tissue

6

9

0

9

A

Na

Na

Bian

2002

Switzerland

Caucasian

MS-SSCA

EAC

Tissue

18

22

0

10

H

10

33

Nie

2002

China

Asian

MSP

ESCC

Tissue

7

21

0

25

A

5

13

Sarbia

2004

Germany

Caucasian

qMSP

EAC

Tissue

27

50

0

50

A

Na

Na

Zhang

2004

China

Asian

MSP

ESCC

Tissue

5

34

0

6

H

Na

Na

Schulmann

2005

USA

Caucasian

qMSP

EAC

Tissue

34

76

2

64

A

14

93

Abbaszadegan

2005

Iran

Caucasian

MSP

ESCC

Tissue

22

30

0

30

H

Na

Na

Abbaszadegan

2005

Iran

Caucasian

MSP

ESCC

Blood

13

30

0

30

H

Na

Na

Roth

2006

China

Asian

MSP

ESCC

Tissue

5

13

0

11

H

3

15

Clement

2006

Switzerland

Caucasian

MS-SSCA

EAC

Tissue

13

27

0

16

H

Na

Na

Guo

2006

China

Asian

MSP

ESCC

Tissue

36

69

0

17

H

20

60

Ishii

2007

Japan

Asian

COBRA

ESCC

Tissue

22

56

18

56

A

8

21

Tissue

0

42

H

Na

Na

Wang

2008

China

Asian

MSP

ESCC

Tissue

110

125

46

125

A

Na

Na

Tissue

0

10

H

Na

Na

Salam

2009

India

African

MSP

ESCC

Tissue

36

69

2

69

A

Na

Na

Wang

2009

USA

Caucasian

MSP

ESCC

Tissue

22

41

0

17

H

33

92

Ganji

2010

Italy

Caucasian

MSP

ESCC

Tissue

12

44

0

19

H

Na

Na

Taghavi

2010

Iran

Caucasian

MSP

ESCC

Tissue

31

50

2

50

A

Na

Na

Lu

2011

China

Asian

MSP

ESCC

Tissue

106

120

46

120

A

Na

Na

Ikoma

2007

Japan

Asian

MSP

ESCC

Blood

6

44

0

12

H

Na

Na

Xing

1999

China

Asian

MSRE

ESCC

Tissue

17

34

3

34

A

Na

Na

Brock

2003

Japan

Asian

MSP

EAC

Tissue

16

41

10

41

A

Na

Na

Vieth

2004

Germany

Caucasian

MSP

EAC

Tissue

8

15

0

15

A

17

55

Hardie

2005

UK

Caucasian

MSP

EAC

Tissue

18

21

9

21

H

14

18

Li

2011

China

Asian

MSP

ESCC

Tissue

21

47

10

47

A

Na

Na

Ling

2010

China

Asian

MSP

ESCC

Tissue

25

50

1

50

H

10

50

Chen

2012

China

Asian

MSP

ESCC

Tissue

210

257

75

257

A

Na

Na

Talukdar

2013

India

African

MSP

ESCC

Tissue

42

112

2

30

A

Na

Na

Liao

2009

China

Asian

MSP

ESCC

Tissue

53

105

8

105

A

Na

Na

Hoshimoto

2015

USA

Caucasian

qMSP

ESCC

Tissue

27

114

0

28

A

Na

Na

Das

2014

India

African

MSP

ESCC

Blood

81

100

Na

Na

Na

Na

Na

Ito

2007

Japan

Asian

MSP

ESCC

Tissue

29

38

Na

Na

Na

Na

Na

Hibi

2001

Japan

Asian

MSP

ESCC

Blood

7

31

Na

Na

Na

Na

Na

M+, positive for CDKN2A methylation; MSP, methylation specific polymerase chain reaction; qMSP, quantitative methylation specific polymerase chain reaction; MSRE, methylation sensitive restriction endonuclease; nMSP, nested methylation specific polymerase chain reaction; MS-SSCA, methylation-sensitive-single-strand conformation analysis; COBRA, combined bisulfite restriction analysis; A, autologous (control sample from the same patients); H, heterogeneous (control samples from other individuals); ESCC, esophageal squamous cell carcinoma; EAC, esophageal adenocarcinoma; Na, not available.

Comparison of the frequency of methylation of the CDKN2A promoter in EC cases and healthy controls

A total of 41 case-control studies, including 2487 EC cases and 2367 healthy controls, were included in the current meta-analysis. Our results indicated that the frequency of methylation of the CDKN2A promoter was significantly higher in EC cases than in healthy controls (odds ratio (OR) = 12.60, 95 % CI = 8.90–17.85; Figure 2A). Meta-regression and subgroup analysis were applied to investigate potential sources of the substantial heterogeneity that was detected among the studies. Although, meta-regression analysis did not identify any obvious source of heterogeneity (Table 2), decreased heterogeneity was observed in several of the subgroup analyses (conducted according to ethnicity, sample source, control type, and detection method; Table 3 ), suggesting that the heterogeneity may derive from multiple sources. Additionally, all subgroups showed that CDKN2A hypermethylation was significantly associated with EC. Moreover, the subgroup analysis showed that the OR of the EAC subgroup (OR = 19.03) was greater than that of the ESCC subgroup (OR = 12.00), and the OR of the Caucasian subgroup (OR = 32.67) was greater than that of the Asian (OR = 9.37) and African subgroups (OR = 17.59), as well as the OR of the blood sample subgroup (OR = 34.98) was greater than that of the tissue sample subgroup (OR = 11.60). To test the robustness of our results, a sensitivity analysis to determine the influence of single studies on overall pooled ORs was performed, and the results supported the stability and credibility of our data (Figure 3A). Application of Begg’s funnel plot analysis indicated no significant publication bias among the studies (P = 0.47; Figure 4A).

Pooled forest plot of CDKN2A methylation status during the carcinogenesis of esophageal cancer.

Figure 2: Pooled forest plot of CDKN2A methylation status during the carcinogenesis of esophageal cancer. (A) Cancer vs. controls: OR = 12.60; 95 % CI, 8.90–17.85. (B) Cancer vs. precancerous lesions: OR = 2.89; 95% CI, 2.20–3.79. (C) Precancerous lesions vs. control: OR = 7.38; 95% CI, 4.31–12.66.

Table 2: Meta regression analyses of CDKN2A promoter methylation in esophageal cancer

Heterogeneity source

Coefficient

95% CI

P

Lower

Upper

Publication year

0.031

-0.097

0.159

0.625

Sample size

0.003

-0.005

0.011

0.449

Ethnicity

Asian

-0.831

-2.42

0.759

0.295

Caucasian

0.672

-1.197

2.54

0.469

Detection method

MSP

-0.883

-1.79

0.024

0.056

Sample source

Tissue

-0.248

-1.901

1.405

0.762

Histology

ESCC

0.721

-0.581

2.022

0.268

Control type

Heterogeneous

0.64

-0.426

1.706

0.23

Table 3: Subgroup analyses of CDKN2A promoter methylation in esophageal cancer

Subgroup

Case

Control

Pooled OR (95% CI)

Heterogeneity

M

U

M

U

I2(%)

P

Race

African

259

288

13

374

17.59 (6.20–49.94)

47

0.17

Caucasian

1023

736

310

1571

32.67 (17.61–60.62)

0

0.93

Asian

78

103

4

95

9.37 (6.28–13.99)

68.7

< 0.01

Sample source

Tissue

1239

991

325

1908

11.60 (8.14–16.54)

61.8

< 0.01

Blood

121

136

2

132

34.98 (12.01–101.85)

2

0.4

Histology

ESCC

1215

1000

306

1814

12.00 (8.35–17.26)

60.9

< 0.01

EAC

145

127

21

226

19.03 (6.03–60.11)

62.4

< 0.01

Control type

Autologous

1017

764

312

1573

9.75 (6.33–15.04)

75.5

< 0.01

Heterogeneous

506

452

15

467

28.57 (16.96–48.12)

0

0.69

Methods

MSP

1048

852

299

1610

9.67 (5.46–27.95)

47

< 0.01

Not MSP

312

275

28

430

31.50 (9.66–102.73)

78.7

< 0.01

Sample size

< 60

449

498

80

644

5.64 (3.078–10.333)

57.7

< 0.01

≥ 60

911

629

247

1396

10.948 (1.247–96.116)

62.2

< 0.01

Publication year

< 2010

729

728

174

1302

10.74 (7.00–16.49)

54.9

< 0.01

≥ 2010

631

399

153

738

16.83 (9.17–30.89)

68.9

< 0.01

M, methylated; U, unmethylated.

Comparison of the frequency of methylation of the CDKN2A promoter in EC and precancerous lesions

There were 14 studies involving 510 ECs and 612 precancerous lesions included in this analysis. In the absence of heterogeneity (I2 = 25%, P = 0.183), a fixed-effects model was applied to evaluate the association of between methylation of CDKN2A in EC and precancerous lesions. We observed that the frequency of CDKN2A methylation was significantly higher in EC than precancerous lesion samples (OR = 2.89; 95% CI, 2.20–3.79; Figure 2B). The results of a sensitivity analysis indicated the authenticity of our results (Figure 3B) and a Begg’s test for publication bias was not statistically significant (P = 0.74; Figure 4B)

Sensitivity analysis of pooled odds ratios for CDKN2A methylation during the carcinogenesis of esophageal cancer.

Figure 3: Sensitivity analysis of pooled odds ratios for CDKN2A methylation during the carcinogenesis of esophageal cancer. (A) Cancer vs. controls. (B) Cancer vs. precancerous lesions. (C) Precancerous lesions vs. control.

Begg&#x2019;s funnel plots of publication bias for CDKN2A methylation during the carcinogenesis of esophageal cancer.

Figure 4: Begg’s funnel plots of publication bias for CDKN2A methylation during the carcinogenesis of esophageal cancer. (A) Cancer vs. controls. (B) Cancer vs. precancerous lesions. (C) Precancerous lesions vs. control.

Comparison of the frequency of methylation of the CDKN2A promoter between precancerous esophageal lesions and healthy controls

The analysis of the association between methylated CDKN2A and esophageal precancerous lesions included 612 precancerosis samples and 381 healthy controls from 14 studies. As demonstrated in Figure 2C, the methylation frequency of CDKN2A was significantly elevated in precancerous lesions compared with controls (OR = 7.38; 95% CI, 4.31–12.66). Sensitivity analysis confirmed the robustness of our results (Figure 3C) and a Begg’s test revealed no publication bias (P > 0.05, Figure 4C).

Association of methylation of the CDKN2A promoter with clinicopathological features of EC

A total of 1313 ECs from 16 studies were used to evaluate the relationships between CDKN2A methylation and clinicopathological features, including age, gender, smoking behavior, alcohol consumption, tumor location, tumor diameter, differentiation grade, tumor stage, clinical stage, and lymph node metastasis (Table 4). Our findings demonstrated that CDKN2A methylation was significantly associated with differentiation grade (poor vs. well and moderate: OR = 1.79; 95% CI, 1.20–2.67; P < 0.01) and clinical stage (advanced vs. early: OR = 2.56; 95% CI, 1.33–4.92; P < 0.01). However, there were no associations between other clinicopathological characteristics and CDKN2A promoter methylation in EC.

Table 4: The association of CDKN2A promoter methylation with clinicopathological features of esophageal cancer patients

Characteristic

No.

Case type/control type

Cases/controls

OR (95% CI)

P

Heterogeneity

I2 (%)

P

Age

8

Older/younger

259/230

0.98 (0.65–1.47)

0.91

0

0.45

Gender

12

Male/female

568/259

0.97 (0.68–1.38)

0.84

0

0.83

Smoking behavior

8

Yes/no

409/354

1.10 (0.54–2.28)

0.79

71.2

0

Alcohol consumption

5

Yes/no

282/322

0.92 (0.47–1.82)

0.82

59.1

0.04

Differentiation grade

11

Poor/well and moderate

144/614

1.79 (1.20–2.67)

0

25.2

0.2

T stage

6

T3+4/T1+2

409/230

0.88 (0.42–1.82)

0.73

68.8

0.01

Clinical stage

9

III + IV/I+II

307/288

2.56 (1.33–4.92)

0.01

55.9

0.02

Lymph node metastasis

8

Yes/no

379/358

1.69 (0.76–3.75)

0.2

77.4

< 0.01

Diameter

4

> 5 cm/< 5 cm

100/98

1.37 (0.74–2.52)

0.32

0

0.91

Location

7

Up and middle/down

456/237

0.79 (0.54–1.14)

0.2

0

0.6

No., number of studies.

The accuracy of testing for CDKN2A methylation for EC diagnosis

In the current analysis, the diagnostic value of methylated CDKN2A for EC diagnosis was assessed using data from 41 eligible case-control studies. The pooled sensitivity and specificity values were 0.52 (95% CI, 0.44–0.59) and 0.96 (95% CI, 0.93–0.98), respectively. The area under the summary receiver operating characteristic (SROC) curve (AUC) based on the specificity and sensitivity was 0.83 (95% CI, 0.79–0.86) (Figure 5A). The AUCs based on subgroups where methylation was tested in tissue and blood samples were 0.82 (95% CI, 0.79–0.85) (Figure 5B) and 0.90 (95% CI, 0.87–0.92) (Figure 5C), respectively. As shown in the Figure 6, Fagan plot analysis demonstrated that the probabilities of a patient being diagnosed with EC were 82%, 93%, and 98% following a positive result for methylation of CDKN2A, where the pretest probabilities of being diagnosed with EC were set to 25%, 50%, and 75%, respectively; when the test was negative, the probabilities of diagnosis with EC reduced to 14%, 33%, and 60%, respectively.

Summary of receiver operating characteristic (SROC) plots of methylated CDKN2A for the diagnosis of esophageal cancer based on all samples, tissue samples, and blood samples.

Figure 5: Summary of receiver operating characteristic (SROC) plots of methylated CDKN2A for the diagnosis of esophageal cancer based on all samples, tissue samples, and blood samples. (A) All samples: sensitivity, 0.52 (95% CI, 0.44–0.59); specificity, 0.96 (95% CI, 0.93–0.98); AUC, 0.83 (95% CI, 0.79–0.86). (B) Tissue samples: sensitivity, 0.53 (95% CI, 0.45–0.61); specificity, 0.95 (95% CI, 0.91–0.98); AUC, 0.82 (95% CI, 0.79–0.85). (C) Blood samples: sensitivity, 0.42 (95% CI, 0.23–0.63); specificity, 1.00 (95% CI, 0.46–1.00); AUC, 0.90 (95% CI, 0.87–0.92).

Fagan plot analysis to evaluate the clinical applicability of screening for methylated CDKN2A in esophageal cancer diagnosis.

Figure 6: Fagan plot analysis to evaluate the clinical applicability of screening for methylated CDKN2A in esophageal cancer diagnosis. (A) The post-test probability was 82% at a pretest probability of 25%. (B) The post-test probability was 93% at a pretest probability of 50%. (C) The post-test probability was 98% at a pretest probability of 75%.

DISCUSSION

EC is a complex, progressive disease with multiple stages. Barrett’s esophagus (BE), a metaplastic condition where the squamous-lined esophageal mucosa is replaced by specialized intestinal mucosa, and dysplasia, are precursor lesions of EC [2, 22].

Epigenetic modifications, including DNA methylation, contribute significantly to the pathogenesis of EC. DNA methylation is a relatively early molecular change that is a candidate biomarker in several carcinomas [23]. Increasing evidence demonstrates that methylation of the promoter of CDKN2A, an important negative regulator of cell growth and proliferation [24], is involved in the tumorigenesis and progression of various tumors, including pancreatic [25], gastric [26], and prostate [27] cancers. Although there have been several reports that the frequency of CDKN2A promoter methylation is higher in EC samples than those from cancer-free controls, the association and the role of CDKN2A promoter methylation in EC remain controversial. We performed this meta-analysis to address these issues, achieve insights into the role of CDKN2A promoter methylation in EC carcinogenesis, and evaluate its diagnostic value in screening for early stage EC.

The current study incorporated a total of 41 case-control studies, including 2487 ECs, 618 precancerosis (including dysplasia and BE), and 2367 control samples. Our results indicate that the frequency of methylation of the CDKN2A promoter was significantly higher in EC than in healthy controls and precancerous lesions. Moreover, the level of CDKN2A methylation was also remarkably higher in precancerous lesions than in healthy controls. This evidence supports the association of methylated CDKN2A with EC carcinogenesis. Recent research has revealed that ESCC and EAC have different molecular features [28]. In our results, the OR of the association with CDKN2A methylation for the EAC subgroup was greater than that for the ESCC subgroup, indicating that this molecular feature may be more relevant in EAC. The subgroup analysis by ethnicity showed that CDKN2A promoter methylation was associated with an increased risk of EC in Asian and Caucasian, as well as African population, with the Caucasian population showing a higher OR than the Asian and African population, suggesting that the Caucasian population may be more susceptible to CDKN2A promoter methylation. The subgroup analysis of sample source presented a significant correlation between hypermethylated CDKN2A and EC in both tissue and blood samples, and the pooled OR of blood samples was remarkably higher than that of tissue samples, which suggested that hypermethylated CDKN2A may be a useful noninvasive biomarker for blood detection.

Additionally, we evaluated the relationship between CDKN2A methylation and the clinicopathological characteristics of EC. A previous systematic review reported that there was a trend towards increased frequency of CDKN2A methylation according to EC differentiation grade; however, the association was not statistically significant [29]. In contrast, our results indicate that methylation of the CDKN2A promoter is significantly more common among patients with poorly differentiated tumors and advanced clinical stage, compared with those with well/moderately differentiated tumors and early stage disease. Together, these results support the hypothesis that hypermethylation of the CDKN2A promoter is involved in the progression of EC.

We further assessed the diagnostic potential of CDKN2A promoter methylation for EC, based on data from 41 eligible case-control studies. The pooled sensitivity and specificity for all included studies were 0.52 and 0.96, respectively, indicating that CDKN2A promoter methylation could be a useful biomarker for diagnosis of EC. In the current study, we also analyzed SROC and AUC statistics. SROC is a comprehensive index synthesizing sensitivity and specificity, which can be used to evaluate the accuracy of diagnostic tests. The SROC curve and AUC can also be used to evaluate diagnostic power, where an AUC closer to 1.0 signifies that the test has better discrimination. The AUC calculated from all studies included in the current analysis was 0.83, indicating that CDKN2A promoter methylation is an extremely useful biomarker for EC diagnosis. In addition, there is increasing evidence that abnormal DNA methylation in body fluid samples is a promising biomarker for cancer screening and diagnosis [30, 31]. As a relatively noninvasive test, blood sample collection is generally acceptable to patients and is a promising route for clinical application of methylation screening. Previous studies have reported that abnormal DNA methylation markers in blood samples can be used to diagnose various cancers [32, 33]. Our subgroup analysis showed that the AUC value for the blood sample group was 0.9, higher than that for the tissue group (0.82), which are in accordance with numerous previous studies [34]. The existence of cell-free circulating tumor DNA (ctDNA) in blood sample could be responsible for this phenomenon. This form of circulating DNA is presumably shed from tumors, either through necrosis or apoptosis [35]. It has been reported that tumor-derived somatic alterations in DNA can be detected in ctDNA with broad clinical application, high sensitivity, and specificity [3638]. All these above indicated that testing for CDKN2A methylation in blood samples has potential as a noninvasive tool for the diagnosis of EC. Future rigorous clinical research studies with larger sample sizes will be essential to validate our findings.

The clinical utility of CDKN2A methylation was also evaluated by Fagan plot analysis [39, 40]. The results demonstrated that, when the pre-test probabilities were assumed to be 25%, 50%, and 75%, a corresponding 82%, 93%, and 98% of patients would be correctly diagnosed with EC following positive CDKN2A methylation tests; moreover, a diagnosis of EC could be ruled out for 86%, 67%, and 40% of patients following negative results. These analyses suggest that methylated CDKN2A has good diagnostic power to discriminate patients with EC from healthy individuals.

The present meta-analysis had several limitations. First, only articles published in English and Chinese were included in the study. Second, significant heterogeneity was observed in the current analysis, and its source was not definitively identified. Third, we did not specifically investigate the association of CDKN2A methylation and different EC histology subtypes; therefore, an updated meta-analysis, including more rigorous studies with large sample sizes in the future would support, and could add to the findings of the present study.

In conclusion, our findings provide strong evidence that CDKN2A methylation is involved in the carcinogenesis and progression of EC and that it is a promising biomarker for the diagnosis of EC, especially by screening of blood samples. Future large-scale studies are necessary to support or add to our findings, especially regarding the role of CDKN2A methylation in the prediction of prognosis, and to support the clinical application of our findings for the benefit of patients with EC.

MATERIALS AND METHODS

Literature search

Five electronic databases (including PubMed, Google Scholar, Web of Science, Embase, and China National Knowledge Infrastructure) were searched for eligible studies until October 20, 2016. The following search terms, and various combinations of them, were used: “p16,” “p16INK4a,” “p14arf”, “p14”,CDKN2A (p16 and p14),” “cyclin-dependent kinase inhibitor 2A,” “methylation,” “DNA methylation,” “promoter methylation,” “esophageal carcinoma,” “esophagus cancer,” “esophageal tumor,” and “esophageal malignancy.” In addition, we performed a manual search of references to find potentially relevant articles.

Selection criteria

We collected all eligible articles that addressed the association between CDKN2A methylation and EC. Articles with data meeting the following criteria were included: (1) all samples confirmed by pathology, including ECs, esophageal precancerous lesions (dysplasia or Barrett’s esophagus), and healthy controls; (2) case-control or cohort studies of CDKN2A promoter methylation in EC; (3) included sufficient data regarding the methylation frequency of the CDKN2A promoter to enable the calculation of odds ratios and 95% confidence intervals; and (4) where authors published several reports using the same population or overlapping data, only the most complete study with the most information was selected.

Data quality assessment

The quality of studies was assessed according to Newcastle–Ottawa Scale (NOS) criteria [41]. The NOS evaluation system includes three aspects: (1) subject selection: 0–4 points; (2) comparability of subjects: 0–2 points; and (3) clinical outcome: 0–3 points. NOS scores range from 0 to 9; and a score ≥ 7 indicates good quality. Only studies with scores ≥ 7 were included in the analysis.

Data extraction

Three authors (QL, JL, and CZ) independently reviewed all the available articles and extracted relevant data from eligible articles. The following information was recorded: the first author’s name; the country, ethnicity of subjects, year of publication, sample type, number of cases, control type, and detection method of methylation of the study; and the methylation frequency of CDKN2A, and clinicopathological parameters (including age, gender, smoking behavior, alcohol consumption, tumor location, tumor diameter, differentiation grade, tumor stage, clinical stage and lymph node metastasis) of the subjects. Inconsistent information was discussed by all authors and a consensus reached on its use.

Statistical analyses

Data were analyzed using Stata statistical software, version 12.0 (Stata Corporation, College Station, TX, USA). Overall odds ORs and corresponding 95% CIs were calculated to evaluate the strengths of the associations between CDKN2A methylation and different EC pathological processes (cancer vs. healthy control, cancer vs. precancerous lesion, precancerous lesion vs. healthy control), as well as clinicopathological features. Potential heterogeneity was quantified using Cochran’s Q statistic and I2 tests [42]. When significant heterogeneity was observed among studies, a random-effects model (DerSimonian-Laird method) [43] was applied to calculate a pooled OR (I2 > 50% or P < 0.05), otherwise, a fixed-effects model (Mantel–Haenszel method) was used [44]. Meta-regression and subgroup analyses were performed to explore the sources of heterogeneity. Sensitivity analyses were conducted to determine the influence of individual studies on the pooled results, through the omission of single studies [45]. Publication bias was quantitatively assessed using Begg’s linear regression tests [46]. Pooled sensitivity and specificity were used to estimate the diagnostic efficacy of testing for CDKN2A promoter methylation. Two indicators, the SROC and AUC, were also calculated to evaluate the stability and accuracy of the diagnostic capacity of CDKN2A promoter methylation for EC [47]. Fagan plot analysis was performed with 25%, 50%, and 75% pre-test probability to assess the diagnostic power of CDKN2A promoter methylation in clinical practice [39]. All P values are two-sided and P values < 0.05 were considered statistically significant.

Abbreviations

EC: esophageal cancer

EAC: esophageal adenocarcinoma

ESCC: esophageal squamous cell carcinoma

BE: Barrett’s esophagus

CDKN2A: cyclin-dependent kinase inhibitor 2A

SROC: summary receiver operating characteristic

AUC: the area under the SROC

NOS: the Newcastle–Ottawa Scale

OR: odds ratio

CI: confidence interval

Author contributions

Conceived and designed the experiments: JL and QL. Performed the experiments: JL and CZ. Analyzed the data: JL, CZ, and QL. Contributed analysis tools: JL and CZ. Wrote the manuscript: JL and CZ.

ACKNOWLEDGMENTS

This research was supported by grants from the Medical and Health Research Project of Zhejiang Province (No. 2013KYB231; No. 2014KYB229), Medical Association Clinical Research Project Funding of Zhejiang Province (No. 2015ZYC-A49), and Ningbo Natural Science Foundation (No. 2015A610221).

CONFLICTS OF INTEREST

None of the authors has any commercial or other associations that might pose a conflicts of interest.

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