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Cancer Biomarkers 13 (2013) 215–226 DOI 10.3233/CBM-130355 IOS Press
A systematic review of hypermethylation of p16 gene in esophageal cancer Ruobing Xua,1 , Fengliang Wangb,1, Liang Wua , Jianming Wanga,∗ and Cheng Lub,∗ a
Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China b Department of Breast, Nanjing Maternity and Child Health Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
Abstract. BACKGROUND: Inactivation of cell-cycle regulating gene p16, resulting from epigenetic alteration, is common in the carcinogenesis of human cancers. The aim of this study is to offer a systematic review on the aberrant methylation of p16 gene in esophageal cancer. METHODS: We performed a meta-analysis referring to the guidelines of PRISMA. We searched for articles published from 1996 to 31 May 2012 using PubMed and China National Knowledge Infrastructure (CNKI) database. Additional database including Web of Science and EMBASE were also searched for related articles. The random or fixed effect model was applied to estimate the pooled frequency of DNA methylation based on the heterogeneity analysis. Subgroup analyses were performed according to the histological type, study area, and tumor grade. RESULTS: This meta-analysis included 39 articles related to the methylation studies on p16 gene in cancer tissues and 7 articles using blood samples. The summarized frequency of DNA methylation detected in cancer tissues was 0.53 (95% CI: 0.44–0.61). With the increase of tumor differentiation grades, the frequency of DNA methylation increased accordingly (well differentiated: 0.37; moderately differentiated: 0.61; poorly differentiated: 0.63). We further summarized the methylation of p16 gene detected in patient’s peripheral blood samples. The pooled frequency was 0.33 (95% CI: 0.17–0.49), which was lower than that detected in cancer tissues. CONCLUSION: This meta-analysis revealed the elevated frequency of DNA methylation of p16 gene in esophageal cancer, which indicated future potential application of this biomarker in early detection as well as the prognosis of the disease. Keywords: Genes, p16, methylation, esophageal neoplasms, meta-analysis
1. Introduction Esophageal cancer has been ranked as one of the most common cause of cancer-related deaths. An estimated 482,300 new cases and 406,800 deaths due to esophageal cancer has been reported in 2008 world1 These
authors contributed equally to this work. authors: Jianming Wang, Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, 211166, Jiangsu, China. E-mail: jmwang@ njmu.edu.cn; Cheng Lu. Department of Breast, Nanjing Maternity and Child Health Hospital of Nanjing Medical University, Nanjing, 210004, Jiangsu, China. E-mail: [email protected]. ∗ Corresponding
wide [1]. Incidence rates of esophageal cancer vary internationally by nearly 16-fold, with the highest rates found in Southern and Eastern Africa and Eastern Asia, while the lowest rates are observed in Western and Middle Africa and Central America [2]. In contrast to the most common type of esophageal adenocarcinoma (EAC) in Western countries, the major phenotype in the Asia – Pacific region is esophageal squamous cell carcinoma (ESCC) [3,4]. Patients with ESCC have a relatively poor prognosis, mostly because it is usually diagnosed at a late stage [5]. Previous efforts for early detection of esophageal cancer have concentrated on cytological or endoscopic screening, but with low sensitivity and potential uncommon but serious side
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effects. With the development of molecular biology, application of genetic and epigenetic biomarkers has gained much more focus due to their higher sensitivity [6,7]. One of the early epigenetic events during the tumourigenesis is DNA aberrant methylation. In normal cells, DNA methylation assures the proper regulation of gene expression and stable gene silencing. Aberrant methylation includes global hypomethylation in genomic DNA as well as the hypermethylation in specific genes [8–10]. It is commonly known that inactivation of certain tumor-suppressor genes occurs as a consequence of hypermethylation within the promoter region. Previous studies have demonstrated a broad range of genes silenced by DNA methylation in different cancer types [11]. Among tumor-suppressor genes, cyclin-dependent kinase inhibitor (CDKN2A) has gained much more focus. CDKN2A (Gene ID: 1029), also known as p16, CDKN2, p16INK4, p16INK4A, or MTS1, is located on 9p21. This gene is frequently mutated or deleted in a wide variety of tumors, and is known to be an important tumor suppressor gene. The tumor-suppressive activity of p16 is ascribed to its ability to bind both CDK4 and CDK6. This in turn inhibits the catalytic activity of the CDK4/CDK6-cyclin D complex, and blocks retinoblastoma phosphorylation, which ultimately prevents cell-cycle progression [12]. Methylation of p16 in esophageal cancer has been identified in many settings, but with varied results due to the different study subjects with different disease stages and pathological types [13,14]. To systematically summarize the methylation status of p16 gene in esophageal cancer, we performed a meta-analysis by combining results from different studies, in the hope of identifying patterns of methylation of p16 gene for the future clinical application.
tional Knowledge Infrastructure (CNKI) database. Additional database including Web of Science and EMBASE were also searched for relevant articles. We used the keywords ‘methylation’ and ‘esophagus cancer’ or “esophageal cancer” or ‘esophageal carcinoma’, in conjunction with any of the following terms: ‘p16’, ‘CDKN2A’, ‘INK4A’, or ‘MTS1’. For publications reporting the same data, only the most recently published article with complete data was involved in the analysis to avoid overlap between studies. The literature search was performed with language restriction to English or Chinese. 2.2. Inclusion and exclusion criteria Inclusion criteria: (1) Specimens used for detecting DNA methylation were surgically resected, microdissected, formalin fixed, or paraffin wax embedded tissues, or peripheral blood (including serum and plasma); (2) Studies examining methylation status of multiple genes were included if individual data of p16 was given. Exclusion criteria: (1) Manmade methylation or demethylation on cell lines; (2) Samples obtained from patients following chemo-radiation therapy; (3) Lack of access to the full article or unable to extract necessary data from the abstract; (4) Carcinoma in situ and high-grade dysplasia were not involved in the analysis. 2.3. Data extraction
2. Method
Two researchers independently performed a systematic search for relevant articles. We developed a data extraction sheet to collect data. For each eligible article, we collected information including the author(s), publication year, sample size, age, gender, histological type of cancer, study area, detection method and the frequency of methylation. Extracted items also included the location of the tumor in the esophagus (upper, middle, lower), and histological grade if available.
2.1. Literature search
2.4. Statistical analysis
We performed a systematic review referring to the guidelines of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) set by the PRISMA Group (2009) [15]. We registered this meta-analysis (No: CRD42012002498) in the PROSPERO website (http://www.crd.york.ac.uk/prospero/). We systematically searched for articles published from 1996 to 31 May 2012 using PubMed and China Na-
We used STATA 11.0 (College Station, TX, USA) to analyze data. Heterogeneity between studies was tested using Q-test (P < 0.05 indicated significant heterogeneity) and I2 ( 50%, strong heterogeneity) [16]. Potential sources of heterogeneity were also detected by meta-regression. To test the robustness of association and characterize possible sources of
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Fig. 1. Flow chart of the meta-analysis.
statistical heterogeneity, sensitivity analysis was carried out by excluding studies one by one and analyzing the homogeneity and the frequency of DNA methylation for the rest studies. Fixed or random effect model was applied to estimate the pooled frequency of DNA methylation based on the heterogeneity analysis. Subgroup analyses were performed according to the histological type of esophageal cancer (ESCC and EAC), study area (China, Japan, USA, Germany and other countries), and histological grade (grade 1, well differentiated; grade 2, moderately differentiated; grade 3, poorly differentiated). Publication bias was assessed by using funnel plots and Begg’s test [17,18].
3. Results 3.1. Study characteristics A flow chart of the literature selection procedure is showed in Fig. 1. Briefly, 115 relevant articles searched from PubMed, EMBASE and Web of Science were included in the first stage. From CNKI database, we
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also searched 946 articles published in Chinese. Then we removed 30 articles due to duplicated publication and dropped 980 irrelevant articles after reading the titles and abstracts. We further carefully read the full texts of the remaining 51 articles and excluded 10 irrelevant articles. Finally, we involved 41 articles in this meta-analysis, including 39 reporting DNA methylation in tissue samples and 7 reporting DNA methylation in blood samples. If the article reported the frequency of DNA methylation using different samples (tissue, serum, or whole blood) or methods among the same group of patients, we defined it as separated studies. For those involved for summarizing DNA methylation in cancer tissues, the sample size of each study varied from 13 to 125. Eleven studies focused on EAC; 29 studies investigated ESCC; and 1 article evaluated patients with EAC and ESCC together. Methylation-specific PCR (MSP) or quantitative methylation-specific PCR (qMSP) was the commonly used method to detect DNA methylation of p16 gene. Other methods included methylation sensitive dot blot assay (MS-DBA), combined bisulphate restriction analysis (COBRA), and the methylationsensitive single-strand conformation analysis. We also summarized 7 articles (9 studies) using blood samples to detect DNA methylation. The sample size varied from 30 to 76. Among them, 5 articles also reported DNA methylation in cancer tissues. All samples were collected prior to the surgery, radiotherapy or chemotherapy. The characteristics of eligible studies are listed in Tables 1 and 2. 3.2. Frequency of DNA methylation of p16 gene in cancer tissues There was a significant heterogeneity between studies (I2 = 93.9%, P < 0.001). Thus, we used the random effect model to pool the data. The summarized frequency of DNA methylation of p16 gene was 0.53(95% CI: 0.44–0.61) in esophageal cancer tissues. The forest plot of involved studies is showed in Fig. 2. We further performed subgroup analyses on selected studies (Table 3). The frequency of DNA methylation of p16 gene varied with the tumor differentiation grades (well differentiated: 0.37; moderately differentiated: 0.61; poorly differentiated: 0.63) (Fig. 3). No significant difference in the frequency of DNA methylation was observed between EAC and ESCC (0.55 vs. 0.52) (Fig. 4).
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R. Xu et al. / p16 methylation in esophageal cancer Table 1 Baseline characteristics of eligible studies evaluating p16 hypermethylation
Study Maesawa [36] Wong [37] Roncalli [38] Xing [39] Xing [40] Eads [41] Kempster [42] Hibi [43] Bian [44] Nie [14] Smeds [45] Zhang [46] Sarbia [47] Vieth [48] Zhang [49] Abbaszadegan [50] Hardie [51] Schildhaus [52] Schulmann [53] Clement [54] Fukuoka [55] Guo [56] Roth [13] Yu [57] Guo [58] Ishii [28] Ito [59] Song [60] Song [60] Fujiwara [19] Wang [27] Yang [61] Miao [62] Salam [29] Wang [63] Mohammad Ganji [64] Taghavi [65] Lu [66] Wang [20] Zhao [67] Zhao [67]
Year 1996 1997 1998 1999 1999 2000 2000 2001 2002 2002 2002 2002 2004 2004 2004 2005 2005 2005 2005 2006 2006 2006 2006 2006 2007 2007 2007 2007 2007 2008 2008 2008 2009 2009 2009 2010 2010 2011 2011 2011 2011
Cases 31 14 72 40 34 4 16 38 22 21 21 30 50 15 34 30 21 10 76 27 35 69 6 45 37 56 38 75 65 60 125 44 105 69 41 44 50 120 76 36 35
Type ESCC EAC EAC ESCC ESCC EAC EAC/ESCC ESCC EAC ESCC ESCC ESCC EAC EAC ESCC ESCC EAC EAC EAC EAC ESCC ESCC ESCC ESCC ESCC ESCC ESCC ESCC ESCC ESCC ESCC ESCC ESCC ESCC EAC ESCC ESCC ESCC ESCC ESCC ESCC
Age (years) NA NA NA NA NA Median:69 Median:68 NA NA NA Median:55 NA Median:62 NA NA Median: 58(M); 61(F) NA NA Mean:61.5 NA Mean:62(ME); 66(UM) NA Mean:61 NA Median:61 Mean:65.8 Mean:62.4(ME);63.4(UM) Median:59 Median:64 Mean:65 Mean:61.8 Mean:55 NA Mean:56.8(M);58.4(F) Mean:69.2 Mean:60.8 Mean:59.0 Mean:61.8 Median:60 Median:65 Median:59
Male N (%) Methylation N (%) NA 6(19) NA 8(57) NA 17(24) NA 16(40) NA 17(50) 3(50) 2(50) 12(75) 9(56) NA 31(82) NA 18(82) NA 7(33) NA 4(19) NA 4(13) 45(90) 27(54) NA 8(53) NA 5(15) 16(53) 22(73) 46(85) 18(86) NA 5(50) NA 34(45) NA 13(48) 30(86) 28(80) NA 36(52) 5(83) 3(50) 31(69) 33(73) 58(55) 24(65) 47(84) 22(39) NA 29(76) 58(77) 31(41) 44(68) 34(52) 49(82) 12(20) 81(65) 110(88) 28(64) 23(52) NA 53(50) 40(58) 36(52) NA 22(54) 29(66) 12(27) 25(50) 31(62) 79(66) 106(88) 61(80) 66(87) 19(53) 16(44) 19(54) 15(43)
Area Japan USA Italy China China USA Australia Japan Switzerland China China China Germany Germany China Iran UK Germany USA Switzerland Japan China China China China Japan Japan China China Japan China China China India USA Italy Iran China China China China
Detection Method MSP MSP MSP BSP MSP Methylight MSP MSP MS-SSCA MSP MSP MSP Real-time qMSP MSP MSP MSP MSP MSP Real-time qMSP MS-DBA MSP Nest-MSP MSP MSP MSP COBRA MSP MSP MSP MSP MSP MSP MSP MSP MSP MSP MSP MSP Real-time MSP MSP MSP
M: male; F: female; ME: methylated; UM: unmethylated; NA: not available; MSP: methylation specific PCR; COBRA: combined bisulphite restriction analysis; MS-SSCA: methylation-sensitive single-strand conformation analysis; MS-DBA: methylation-sensitive dot-blot assay; BSP: bisulfite sequencing PCR; ESCC: esophageal squamous cell carcinoma; EAC: esophageal adenocarcinoma. Table 2 Methylation of p16 gene detected in peripheral blood samples of esophageal cancer Author Hibi [43] Abbazadegan [50] Abbazadegan [50] Yao [68] Yao [68] Guo [58] Ikoma [69] Fujiwara [19] Wang [20]
Year Methylation in blood samples N (%) Cancer type Methylation in tumor tissues N (%) 2001 7/31(23) ESCC 31/38(82) 2005 13/30(43) ESCC 22/30(73) 2005 8/30(27) ESCC 22/30(73) 2005 15/56(27) ESCC − 2005 34/56(61) ESCC − 2006 14/51(27) ESCC 35/51(68) 2007 6/44(14) ESCC − 2008 2/38(5) ESCC 12/60(20) 2011 54/76(72) ESCC 66/76(87)
Area Method Japan MSP Iran MSP Iran MSP China MSP China Nest-MSP China MSP Japan MSP Japan MSP China Real-time MSP
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Table 3 Subgroup analysis of DNA methylation of p16 gene in cancer tissues Study Histological type ESCC EAC Areas China Japan USA Germany Other countries Grade Grade 1 Grade 2 Grade 3
Methylation (%)
95% CI
P value (heterogeneity test)
52 55
42–61 42–68
< 0.001 < 0.001
51 53 49 53 56
39–63 28–77 41–57 42–65 41–72
< 0.001 < 0.001 0.730 0.974 < 0.001
37 61 63
28–45 46–75 49–78
0.052 < 0.001 0.006
Grade 1: well differentiated; grade 2: moderately differentiated; grade 3: poorly differentiated.
Fig. 2. Forest plot of p16 methylation in esophageal cancer tissues.
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Fig. 3. Subgroup meta-analysis of p16 gene methylation in esophageal cancer tissues stratified by tumor histological grade.
3.3. Frequency of DNA methylation of p16 gene in blood samples The forest plot of involved studies on the frequency of DNA methylation of p16 gene in blood samples is showed in Fig. 5. The summarized frequency of DNA methylation of p16 gene was 0.33(95% CI: 0.17– 0.49), which was lower than that detected in cancer tissues. The frequency varied significantly between studies. For example, in the study conducted by Fujiwara in Japan, the proportion of p16 methylation was only 5% [19]; but in another study performed by Wang in China, it was 71% [20]. We further compared the results in three articles using tissue samples and blood samples from the same group of patients. The frequency was significantly lower in blood samples as compared to that detected in cancer tissue samples (Table 2).
3.4. Assessment of publication bias We used the funnel plot and Begg’s test to evaluate the publication bias of selected articles (Fig. 6). No significant publication bias was found for selected articles. The p value of Begg’s test was 0.472 for studies using cancer tissues and 0.251 for studies using blood samples, respectively.
4. Discussion In the past decade, the role of aberrant DNA methylation in human cancers has been increasingly understood. This meta-analysis demonstrated DNA methylation of p16 gene as a common event in esophageal cancer. Aberrant DNA methylation of p16 gene can also be detected from patient’s peripheral blood samples,
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Fig. 4. Subgroup meta-analysis of p16 gene methylation in esophageal cancer tissues stratified by histological type.
but with lower frequency as compared with that from cancer tissues. The importance of somatic epigenetic alterations in tissues targeted for carcinogenesis is considered a key molecular step in the development of tumors [21]. The term ‘epigenetics’ refers to stable alterations in gene expression with no underlying modifications in the genetic sequences [22]. Epigenetic mechanisms regulating gene expression include DNA methylation, histone
protein modification, as well as functional non-coding RNA. The most widely studied epigenetic modification is DNA methylation, the covalent post-replicative addition of a –CH3 onto the 5-C of the cytosine ring within CpG dinucleotide [22]. Aberrant CpG island methylation is common in the development of cancers and plays an important role in the carcinogenic process [8]. Global hypomethylation increases mutation rates and chromosomal instability whereas pro-
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Fig. 5. Forest plot of p16 gene methylation in blood samples.
Fig. 6. Begg’s funnel plot for visual assessment of the presence of publication bias.
moter hypermethylation usually results in transcriptional gene inactivation [9]. As DNA methylation is reversible, development of relevant demethylating agents is making the field of DNA methylation wider and more exciting [23,24]. P 16 is a cyclin-dependent kinase inhibitor that functions upstream of the Rb gene, negatively regulating cell cycle progression by preventing the phosphorylation of Rb protein during the G1 phase of the cell cycle [22]. Its suppressive role in human cancers has been demonstrated in various studies [3]. During the past decade, studies have revealed the molecular mechanisms underlying the inactivation of p16 gene includ-
ing hypermethylation of the gene promoter, intragenic mutation coupled with the loss of the second allele, and homozygous deletion [25]. Hypermethylation of CpG island, caused by the action of DNA methyltransferases (DNMTs), is a major form of epigenetic inactivation of p16 gene [26]. Findings from our metaanalysis further demonstrated methylation of p16 as a frequent event in esophageal cancer tissues, with the proportion varying from 13% to 88% in different settings. The development of human esophageal cancer is a multistep, progressive process. Previous studies have reported that the aberrant hypermethylation of cancer
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related genes was associated with the clinical characteristics of esophageal cancer [27]. Previous studies have shown that the frequency of CpG island methylation increased from a baseline level in the background of non-neoplastic epithelium, through intraepithelial neoplasia (IEN), to advanced esophageal cancer [14, 28]. As DNA methylation is an early event during the carcinogenesis and can be detected in many kinds of body fluids, it is supposed to be one of a potential alternative biomarkers [24]. In accordance with previous findings, our meta-analysis indicated that promoter methylation gradually increased with the increasing severity of histological grades of esophageal cancer [29]. This characteristic showed a promising potential to apply this biomarker for the prognosis of esophageal cancer patients. Studies of the potential of DNA methylation as a cancer biomarker for esophageal cancer mainly used esophagus tissues. The invasive nature of this procedure and the likely existence of tissue heterogeneity limited the application of tissue based DNA methylation detection. Therefore, it is desirable to develop less invasive and more accessible ways which can substitute or complement tissue DNA for methylation studies [30]. Blood-based specimens such as cell-free circulating nucleic acid and DNA extracted from leukocytes in peripheral blood may be a potential source of noninvasive biomarkers [21]. Since the discovery of circulating nucleic acids in plasma in 1948, many diagnostic applications have emerged [31]. It is clear that cancers do not develop as an isolated phenomenon in their target tissue, but instead result from altered processes affecting the surrounding tissues and cells. Thus, alterations of DNA methylation profiles detected in peripheral blood may be useful not only in understanding the carcinogenic process, but also provide critical insights in a systematic biological view of carcinogenesis [32]. We observed that when using blood (plasma/serum) samples, the pooled frequency of DNA methylation in p16 gene was relatively lower than that in the tumor tissues. The reason might be the lower concentration of cell free DNA and the lack of sensitive detection method. Furthermore, the stage of esophageal cancer and the timing of blood collection are factors influencing whether methylation of p16 gene can be detected in peripheral blood samples. This might partly explain the heterogeneity of the studies. Despite this, the results from our study still give us an exciting clue to apply the aberrant hypermethylation of cancer related genes as the biomarkers for the detection and diagnosis of esophageal cancer.
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The methods used to detect DNA methylation may influence the results of this analysis. With the progress of modern molecular technology, more and more sensitive and specific methods have been developed. The common used methods include MSP, methylation sensitive restriction endonuclease enzyme PCR/southern blot, and bisulfate sequencing, etc. In brief, the quantitative methods seem to be more sensitive than the qualitative methods. However, in this meta-analysis, a large part of studies used traditional MSP method (qualitative), which restricted us to perform the subgroup analysis by the detection methods. In this meta-analysis, we only summarized the frequency of methylation in one gene. Some other limitations of this meta-analysis should be discussed. First, the possibility of selection bias and information bias cannot be completely excluded. Insufficient information such as demographic and some clinical factors may act as potential confounders. For example, tobacco smoking, alcohol consumption and gastroesohageal reflux are important factors for esophageal cancer. However, very few articles reported the proportion of p16 methylation by considering these factors, which restricted us to perform subgroup meta-analysis of these factors. Second, we restricted articles to those published in English or Chinese. Articles with potentially high-quality data that published in other languages were not included. Third, assays that measure DNA methylation at gene promoters need to be standardized, simplified, and evaluated with external quality assurance programmer [33]. Fourth, the sensitivity and specificity of applying one gene as the biomarker may not be satisfying for clinical application. Thus, to construct an epigenetic profiling by involving multiple markers might be of clinical value in human cancers and may in the future be extended to other diseases [34]. Remarkably, in spite of significant molecular and translational progress, there are currently no epigenetic biomarkers in widespread clinical use [35]. Large systematic and unbiased prospective studies that consider biological plausibility and data analysis issues will be needed in order to develop a clinically feasible assay.
5. Conclusion This meta-analysis revealed elevated frequency of DNA methylation of p16 gene either in cancer tissues or in blood samples of esophageal cancer patients. It may have potential application in detection of
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esophageal cancer as well as the prognosis of the disease. However, methodological and validation issues remain to be addressed to provide the data that will enable this information to be considered for the clinical use.
[7] [8] [9]
[10]
Authors’ contributions
[11]
RX and JW conceived the idea. RX, FW and LW were involved in data collection. RX, FW, JW, and CL participated in the statistical analysis and drafted the manuscript. All authors read and approved the final manuscript.
[12]
[13]
[14]
Acknowledgements This study is supported by the National Natural Science Foundation of China (81172268, 81172501), Key University Science Research Project of Jiangsu Province (12KJA330001), and Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD).
[15]
[16] [17] [18]
[19]
Competing interests The authors declare that they have no competing interests.
[20]
[21]
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Cancer Biomarkers 13 (2013) 215–226 DOI 10.3233/CBM-130355 IOS Press
A systematic review of hypermethylation of p16 gene in esophageal cancer Ruobing Xua,1 , Fengliang Wangb,1, Liang Wua , Jianming Wanga,∗ and Cheng Lub,∗ a
Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China b Department of Breast, Nanjing Maternity and Child Health Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
Abstract. BACKGROUND: Inactivation of cell-cycle regulating gene p16, resulting from epigenetic alteration, is common in the carcinogenesis of human cancers. The aim of this study is to offer a systematic review on the aberrant methylation of p16 gene in esophageal cancer. METHODS: We performed a meta-analysis referring to the guidelines of PRISMA. We searched for articles published from 1996 to 31 May 2012 using PubMed and China National Knowledge Infrastructure (CNKI) database. Additional database including Web of Science and EMBASE were also searched for related articles. The random or fixed effect model was applied to estimate the pooled frequency of DNA methylation based on the heterogeneity analysis. Subgroup analyses were performed according to the histological type, study area, and tumor grade. RESULTS: This meta-analysis included 39 articles related to the methylation studies on p16 gene in cancer tissues and 7 articles using blood samples. The summarized frequency of DNA methylation detected in cancer tissues was 0.53 (95% CI: 0.44–0.61). With the increase of tumor differentiation grades, the frequency of DNA methylation increased accordingly (well differentiated: 0.37; moderately differentiated: 0.61; poorly differentiated: 0.63). We further summarized the methylation of p16 gene detected in patient’s peripheral blood samples. The pooled frequency was 0.33 (95% CI: 0.17–0.49), which was lower than that detected in cancer tissues. CONCLUSION: This meta-analysis revealed the elevated frequency of DNA methylation of p16 gene in esophageal cancer, which indicated future potential application of this biomarker in early detection as well as the prognosis of the disease. Keywords: Genes, p16, methylation, esophageal neoplasms, meta-analysis
1. Introduction Esophageal cancer has been ranked as one of the most common cause of cancer-related deaths. An estimated 482,300 new cases and 406,800 deaths due to esophageal cancer has been reported in 2008 world1 These
authors contributed equally to this work. authors: Jianming Wang, Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, 211166, Jiangsu, China. E-mail: jmwang@ njmu.edu.cn; Cheng Lu. Department of Breast, Nanjing Maternity and Child Health Hospital of Nanjing Medical University, Nanjing, 210004, Jiangsu, China. E-mail: [email protected]. ∗ Corresponding
wide [1]. Incidence rates of esophageal cancer vary internationally by nearly 16-fold, with the highest rates found in Southern and Eastern Africa and Eastern Asia, while the lowest rates are observed in Western and Middle Africa and Central America [2]. In contrast to the most common type of esophageal adenocarcinoma (EAC) in Western countries, the major phenotype in the Asia – Pacific region is esophageal squamous cell carcinoma (ESCC) [3,4]. Patients with ESCC have a relatively poor prognosis, mostly because it is usually diagnosed at a late stage [5]. Previous efforts for early detection of esophageal cancer have concentrated on cytological or endoscopic screening, but with low sensitivity and potential uncommon but serious side
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effects. With the development of molecular biology, application of genetic and epigenetic biomarkers has gained much more focus due to their higher sensitivity [6,7]. One of the early epigenetic events during the tumourigenesis is DNA aberrant methylation. In normal cells, DNA methylation assures the proper regulation of gene expression and stable gene silencing. Aberrant methylation includes global hypomethylation in genomic DNA as well as the hypermethylation in specific genes [8–10]. It is commonly known that inactivation of certain tumor-suppressor genes occurs as a consequence of hypermethylation within the promoter region. Previous studies have demonstrated a broad range of genes silenced by DNA methylation in different cancer types [11]. Among tumor-suppressor genes, cyclin-dependent kinase inhibitor (CDKN2A) has gained much more focus. CDKN2A (Gene ID: 1029), also known as p16, CDKN2, p16INK4, p16INK4A, or MTS1, is located on 9p21. This gene is frequently mutated or deleted in a wide variety of tumors, and is known to be an important tumor suppressor gene. The tumor-suppressive activity of p16 is ascribed to its ability to bind both CDK4 and CDK6. This in turn inhibits the catalytic activity of the CDK4/CDK6-cyclin D complex, and blocks retinoblastoma phosphorylation, which ultimately prevents cell-cycle progression [12]. Methylation of p16 in esophageal cancer has been identified in many settings, but with varied results due to the different study subjects with different disease stages and pathological types [13,14]. To systematically summarize the methylation status of p16 gene in esophageal cancer, we performed a meta-analysis by combining results from different studies, in the hope of identifying patterns of methylation of p16 gene for the future clinical application.
tional Knowledge Infrastructure (CNKI) database. Additional database including Web of Science and EMBASE were also searched for relevant articles. We used the keywords ‘methylation’ and ‘esophagus cancer’ or “esophageal cancer” or ‘esophageal carcinoma’, in conjunction with any of the following terms: ‘p16’, ‘CDKN2A’, ‘INK4A’, or ‘MTS1’. For publications reporting the same data, only the most recently published article with complete data was involved in the analysis to avoid overlap between studies. The literature search was performed with language restriction to English or Chinese. 2.2. Inclusion and exclusion criteria Inclusion criteria: (1) Specimens used for detecting DNA methylation were surgically resected, microdissected, formalin fixed, or paraffin wax embedded tissues, or peripheral blood (including serum and plasma); (2) Studies examining methylation status of multiple genes were included if individual data of p16 was given. Exclusion criteria: (1) Manmade methylation or demethylation on cell lines; (2) Samples obtained from patients following chemo-radiation therapy; (3) Lack of access to the full article or unable to extract necessary data from the abstract; (4) Carcinoma in situ and high-grade dysplasia were not involved in the analysis. 2.3. Data extraction
2. Method
Two researchers independently performed a systematic search for relevant articles. We developed a data extraction sheet to collect data. For each eligible article, we collected information including the author(s), publication year, sample size, age, gender, histological type of cancer, study area, detection method and the frequency of methylation. Extracted items also included the location of the tumor in the esophagus (upper, middle, lower), and histological grade if available.
2.1. Literature search
2.4. Statistical analysis
We performed a systematic review referring to the guidelines of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) set by the PRISMA Group (2009) [15]. We registered this meta-analysis (No: CRD42012002498) in the PROSPERO website (http://www.crd.york.ac.uk/prospero/). We systematically searched for articles published from 1996 to 31 May 2012 using PubMed and China Na-
We used STATA 11.0 (College Station, TX, USA) to analyze data. Heterogeneity between studies was tested using Q-test (P < 0.05 indicated significant heterogeneity) and I2 ( 50%, strong heterogeneity) [16]. Potential sources of heterogeneity were also detected by meta-regression. To test the robustness of association and characterize possible sources of
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Fig. 1. Flow chart of the meta-analysis.
statistical heterogeneity, sensitivity analysis was carried out by excluding studies one by one and analyzing the homogeneity and the frequency of DNA methylation for the rest studies. Fixed or random effect model was applied to estimate the pooled frequency of DNA methylation based on the heterogeneity analysis. Subgroup analyses were performed according to the histological type of esophageal cancer (ESCC and EAC), study area (China, Japan, USA, Germany and other countries), and histological grade (grade 1, well differentiated; grade 2, moderately differentiated; grade 3, poorly differentiated). Publication bias was assessed by using funnel plots and Begg’s test [17,18].
3. Results 3.1. Study characteristics A flow chart of the literature selection procedure is showed in Fig. 1. Briefly, 115 relevant articles searched from PubMed, EMBASE and Web of Science were included in the first stage. From CNKI database, we
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also searched 946 articles published in Chinese. Then we removed 30 articles due to duplicated publication and dropped 980 irrelevant articles after reading the titles and abstracts. We further carefully read the full texts of the remaining 51 articles and excluded 10 irrelevant articles. Finally, we involved 41 articles in this meta-analysis, including 39 reporting DNA methylation in tissue samples and 7 reporting DNA methylation in blood samples. If the article reported the frequency of DNA methylation using different samples (tissue, serum, or whole blood) or methods among the same group of patients, we defined it as separated studies. For those involved for summarizing DNA methylation in cancer tissues, the sample size of each study varied from 13 to 125. Eleven studies focused on EAC; 29 studies investigated ESCC; and 1 article evaluated patients with EAC and ESCC together. Methylation-specific PCR (MSP) or quantitative methylation-specific PCR (qMSP) was the commonly used method to detect DNA methylation of p16 gene. Other methods included methylation sensitive dot blot assay (MS-DBA), combined bisulphate restriction analysis (COBRA), and the methylationsensitive single-strand conformation analysis. We also summarized 7 articles (9 studies) using blood samples to detect DNA methylation. The sample size varied from 30 to 76. Among them, 5 articles also reported DNA methylation in cancer tissues. All samples were collected prior to the surgery, radiotherapy or chemotherapy. The characteristics of eligible studies are listed in Tables 1 and 2. 3.2. Frequency of DNA methylation of p16 gene in cancer tissues There was a significant heterogeneity between studies (I2 = 93.9%, P < 0.001). Thus, we used the random effect model to pool the data. The summarized frequency of DNA methylation of p16 gene was 0.53(95% CI: 0.44–0.61) in esophageal cancer tissues. The forest plot of involved studies is showed in Fig. 2. We further performed subgroup analyses on selected studies (Table 3). The frequency of DNA methylation of p16 gene varied with the tumor differentiation grades (well differentiated: 0.37; moderately differentiated: 0.61; poorly differentiated: 0.63) (Fig. 3). No significant difference in the frequency of DNA methylation was observed between EAC and ESCC (0.55 vs. 0.52) (Fig. 4).
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R. Xu et al. / p16 methylation in esophageal cancer Table 1 Baseline characteristics of eligible studies evaluating p16 hypermethylation
Study Maesawa [36] Wong [37] Roncalli [38] Xing [39] Xing [40] Eads [41] Kempster [42] Hibi [43] Bian [44] Nie [14] Smeds [45] Zhang [46] Sarbia [47] Vieth [48] Zhang [49] Abbaszadegan [50] Hardie [51] Schildhaus [52] Schulmann [53] Clement [54] Fukuoka [55] Guo [56] Roth [13] Yu [57] Guo [58] Ishii [28] Ito [59] Song [60] Song [60] Fujiwara [19] Wang [27] Yang [61] Miao [62] Salam [29] Wang [63] Mohammad Ganji [64] Taghavi [65] Lu [66] Wang [20] Zhao [67] Zhao [67]
Year 1996 1997 1998 1999 1999 2000 2000 2001 2002 2002 2002 2002 2004 2004 2004 2005 2005 2005 2005 2006 2006 2006 2006 2006 2007 2007 2007 2007 2007 2008 2008 2008 2009 2009 2009 2010 2010 2011 2011 2011 2011
Cases 31 14 72 40 34 4 16 38 22 21 21 30 50 15 34 30 21 10 76 27 35 69 6 45 37 56 38 75 65 60 125 44 105 69 41 44 50 120 76 36 35
Type ESCC EAC EAC ESCC ESCC EAC EAC/ESCC ESCC EAC ESCC ESCC ESCC EAC EAC ESCC ESCC EAC EAC EAC EAC ESCC ESCC ESCC ESCC ESCC ESCC ESCC ESCC ESCC ESCC ESCC ESCC ESCC ESCC EAC ESCC ESCC ESCC ESCC ESCC ESCC
Age (years) NA NA NA NA NA Median:69 Median:68 NA NA NA Median:55 NA Median:62 NA NA Median: 58(M); 61(F) NA NA Mean:61.5 NA Mean:62(ME); 66(UM) NA Mean:61 NA Median:61 Mean:65.8 Mean:62.4(ME);63.4(UM) Median:59 Median:64 Mean:65 Mean:61.8 Mean:55 NA Mean:56.8(M);58.4(F) Mean:69.2 Mean:60.8 Mean:59.0 Mean:61.8 Median:60 Median:65 Median:59
Male N (%) Methylation N (%) NA 6(19) NA 8(57) NA 17(24) NA 16(40) NA 17(50) 3(50) 2(50) 12(75) 9(56) NA 31(82) NA 18(82) NA 7(33) NA 4(19) NA 4(13) 45(90) 27(54) NA 8(53) NA 5(15) 16(53) 22(73) 46(85) 18(86) NA 5(50) NA 34(45) NA 13(48) 30(86) 28(80) NA 36(52) 5(83) 3(50) 31(69) 33(73) 58(55) 24(65) 47(84) 22(39) NA 29(76) 58(77) 31(41) 44(68) 34(52) 49(82) 12(20) 81(65) 110(88) 28(64) 23(52) NA 53(50) 40(58) 36(52) NA 22(54) 29(66) 12(27) 25(50) 31(62) 79(66) 106(88) 61(80) 66(87) 19(53) 16(44) 19(54) 15(43)
Area Japan USA Italy China China USA Australia Japan Switzerland China China China Germany Germany China Iran UK Germany USA Switzerland Japan China China China China Japan Japan China China Japan China China China India USA Italy Iran China China China China
Detection Method MSP MSP MSP BSP MSP Methylight MSP MSP MS-SSCA MSP MSP MSP Real-time qMSP MSP MSP MSP MSP MSP Real-time qMSP MS-DBA MSP Nest-MSP MSP MSP MSP COBRA MSP MSP MSP MSP MSP MSP MSP MSP MSP MSP MSP MSP Real-time MSP MSP MSP
M: male; F: female; ME: methylated; UM: unmethylated; NA: not available; MSP: methylation specific PCR; COBRA: combined bisulphite restriction analysis; MS-SSCA: methylation-sensitive single-strand conformation analysis; MS-DBA: methylation-sensitive dot-blot assay; BSP: bisulfite sequencing PCR; ESCC: esophageal squamous cell carcinoma; EAC: esophageal adenocarcinoma. Table 2 Methylation of p16 gene detected in peripheral blood samples of esophageal cancer Author Hibi [43] Abbazadegan [50] Abbazadegan [50] Yao [68] Yao [68] Guo [58] Ikoma [69] Fujiwara [19] Wang [20]
Year Methylation in blood samples N (%) Cancer type Methylation in tumor tissues N (%) 2001 7/31(23) ESCC 31/38(82) 2005 13/30(43) ESCC 22/30(73) 2005 8/30(27) ESCC 22/30(73) 2005 15/56(27) ESCC − 2005 34/56(61) ESCC − 2006 14/51(27) ESCC 35/51(68) 2007 6/44(14) ESCC − 2008 2/38(5) ESCC 12/60(20) 2011 54/76(72) ESCC 66/76(87)
Area Method Japan MSP Iran MSP Iran MSP China MSP China Nest-MSP China MSP Japan MSP Japan MSP China Real-time MSP
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Table 3 Subgroup analysis of DNA methylation of p16 gene in cancer tissues Study Histological type ESCC EAC Areas China Japan USA Germany Other countries Grade Grade 1 Grade 2 Grade 3
Methylation (%)
95% CI
P value (heterogeneity test)
52 55
42–61 42–68
< 0.001 < 0.001
51 53 49 53 56
39–63 28–77 41–57 42–65 41–72
< 0.001 < 0.001 0.730 0.974 < 0.001
37 61 63
28–45 46–75 49–78
0.052 < 0.001 0.006
Grade 1: well differentiated; grade 2: moderately differentiated; grade 3: poorly differentiated.
Fig. 2. Forest plot of p16 methylation in esophageal cancer tissues.
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Fig. 3. Subgroup meta-analysis of p16 gene methylation in esophageal cancer tissues stratified by tumor histological grade.
3.3. Frequency of DNA methylation of p16 gene in blood samples The forest plot of involved studies on the frequency of DNA methylation of p16 gene in blood samples is showed in Fig. 5. The summarized frequency of DNA methylation of p16 gene was 0.33(95% CI: 0.17– 0.49), which was lower than that detected in cancer tissues. The frequency varied significantly between studies. For example, in the study conducted by Fujiwara in Japan, the proportion of p16 methylation was only 5% [19]; but in another study performed by Wang in China, it was 71% [20]. We further compared the results in three articles using tissue samples and blood samples from the same group of patients. The frequency was significantly lower in blood samples as compared to that detected in cancer tissue samples (Table 2).
3.4. Assessment of publication bias We used the funnel plot and Begg’s test to evaluate the publication bias of selected articles (Fig. 6). No significant publication bias was found for selected articles. The p value of Begg’s test was 0.472 for studies using cancer tissues and 0.251 for studies using blood samples, respectively.
4. Discussion In the past decade, the role of aberrant DNA methylation in human cancers has been increasingly understood. This meta-analysis demonstrated DNA methylation of p16 gene as a common event in esophageal cancer. Aberrant DNA methylation of p16 gene can also be detected from patient’s peripheral blood samples,
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Fig. 4. Subgroup meta-analysis of p16 gene methylation in esophageal cancer tissues stratified by histological type.
but with lower frequency as compared with that from cancer tissues. The importance of somatic epigenetic alterations in tissues targeted for carcinogenesis is considered a key molecular step in the development of tumors [21]. The term ‘epigenetics’ refers to stable alterations in gene expression with no underlying modifications in the genetic sequences [22]. Epigenetic mechanisms regulating gene expression include DNA methylation, histone
protein modification, as well as functional non-coding RNA. The most widely studied epigenetic modification is DNA methylation, the covalent post-replicative addition of a –CH3 onto the 5-C of the cytosine ring within CpG dinucleotide [22]. Aberrant CpG island methylation is common in the development of cancers and plays an important role in the carcinogenic process [8]. Global hypomethylation increases mutation rates and chromosomal instability whereas pro-
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Fig. 5. Forest plot of p16 gene methylation in blood samples.
Fig. 6. Begg’s funnel plot for visual assessment of the presence of publication bias.
moter hypermethylation usually results in transcriptional gene inactivation [9]. As DNA methylation is reversible, development of relevant demethylating agents is making the field of DNA methylation wider and more exciting [23,24]. P 16 is a cyclin-dependent kinase inhibitor that functions upstream of the Rb gene, negatively regulating cell cycle progression by preventing the phosphorylation of Rb protein during the G1 phase of the cell cycle [22]. Its suppressive role in human cancers has been demonstrated in various studies [3]. During the past decade, studies have revealed the molecular mechanisms underlying the inactivation of p16 gene includ-
ing hypermethylation of the gene promoter, intragenic mutation coupled with the loss of the second allele, and homozygous deletion [25]. Hypermethylation of CpG island, caused by the action of DNA methyltransferases (DNMTs), is a major form of epigenetic inactivation of p16 gene [26]. Findings from our metaanalysis further demonstrated methylation of p16 as a frequent event in esophageal cancer tissues, with the proportion varying from 13% to 88% in different settings. The development of human esophageal cancer is a multistep, progressive process. Previous studies have reported that the aberrant hypermethylation of cancer
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related genes was associated with the clinical characteristics of esophageal cancer [27]. Previous studies have shown that the frequency of CpG island methylation increased from a baseline level in the background of non-neoplastic epithelium, through intraepithelial neoplasia (IEN), to advanced esophageal cancer [14, 28]. As DNA methylation is an early event during the carcinogenesis and can be detected in many kinds of body fluids, it is supposed to be one of a potential alternative biomarkers [24]. In accordance with previous findings, our meta-analysis indicated that promoter methylation gradually increased with the increasing severity of histological grades of esophageal cancer [29]. This characteristic showed a promising potential to apply this biomarker for the prognosis of esophageal cancer patients. Studies of the potential of DNA methylation as a cancer biomarker for esophageal cancer mainly used esophagus tissues. The invasive nature of this procedure and the likely existence of tissue heterogeneity limited the application of tissue based DNA methylation detection. Therefore, it is desirable to develop less invasive and more accessible ways which can substitute or complement tissue DNA for methylation studies [30]. Blood-based specimens such as cell-free circulating nucleic acid and DNA extracted from leukocytes in peripheral blood may be a potential source of noninvasive biomarkers [21]. Since the discovery of circulating nucleic acids in plasma in 1948, many diagnostic applications have emerged [31]. It is clear that cancers do not develop as an isolated phenomenon in their target tissue, but instead result from altered processes affecting the surrounding tissues and cells. Thus, alterations of DNA methylation profiles detected in peripheral blood may be useful not only in understanding the carcinogenic process, but also provide critical insights in a systematic biological view of carcinogenesis [32]. We observed that when using blood (plasma/serum) samples, the pooled frequency of DNA methylation in p16 gene was relatively lower than that in the tumor tissues. The reason might be the lower concentration of cell free DNA and the lack of sensitive detection method. Furthermore, the stage of esophageal cancer and the timing of blood collection are factors influencing whether methylation of p16 gene can be detected in peripheral blood samples. This might partly explain the heterogeneity of the studies. Despite this, the results from our study still give us an exciting clue to apply the aberrant hypermethylation of cancer related genes as the biomarkers for the detection and diagnosis of esophageal cancer.
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The methods used to detect DNA methylation may influence the results of this analysis. With the progress of modern molecular technology, more and more sensitive and specific methods have been developed. The common used methods include MSP, methylation sensitive restriction endonuclease enzyme PCR/southern blot, and bisulfate sequencing, etc. In brief, the quantitative methods seem to be more sensitive than the qualitative methods. However, in this meta-analysis, a large part of studies used traditional MSP method (qualitative), which restricted us to perform the subgroup analysis by the detection methods. In this meta-analysis, we only summarized the frequency of methylation in one gene. Some other limitations of this meta-analysis should be discussed. First, the possibility of selection bias and information bias cannot be completely excluded. Insufficient information such as demographic and some clinical factors may act as potential confounders. For example, tobacco smoking, alcohol consumption and gastroesohageal reflux are important factors for esophageal cancer. However, very few articles reported the proportion of p16 methylation by considering these factors, which restricted us to perform subgroup meta-analysis of these factors. Second, we restricted articles to those published in English or Chinese. Articles with potentially high-quality data that published in other languages were not included. Third, assays that measure DNA methylation at gene promoters need to be standardized, simplified, and evaluated with external quality assurance programmer [33]. Fourth, the sensitivity and specificity of applying one gene as the biomarker may not be satisfying for clinical application. Thus, to construct an epigenetic profiling by involving multiple markers might be of clinical value in human cancers and may in the future be extended to other diseases [34]. Remarkably, in spite of significant molecular and translational progress, there are currently no epigenetic biomarkers in widespread clinical use [35]. Large systematic and unbiased prospective studies that consider biological plausibility and data analysis issues will be needed in order to develop a clinically feasible assay.
5. Conclusion This meta-analysis revealed elevated frequency of DNA methylation of p16 gene either in cancer tissues or in blood samples of esophageal cancer patients. It may have potential application in detection of
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esophageal cancer as well as the prognosis of the disease. However, methodological and validation issues remain to be addressed to provide the data that will enable this information to be considered for the clinical use.
[7] [8] [9]
[10]
Authors’ contributions
[11]
RX and JW conceived the idea. RX, FW and LW were involved in data collection. RX, FW, JW, and CL participated in the statistical analysis and drafted the manuscript. All authors read and approved the final manuscript.
[12]
[13]
[14]
Acknowledgements This study is supported by the National Natural Science Foundation of China (81172268, 81172501), Key University Science Research Project of Jiangsu Province (12KJA330001), and Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD).
[15]
[16] [17] [18]
[19]
Competing interests The authors declare that they have no competing interests.
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[21]
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