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Diagnostic accuracy of DNA methylation for head and neck cancer varies by sample type and number of markers tested

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Oncotarget. 2016; 7:80019-80032. https://doi.org/10.18632/oncotarget.12219

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Xu Ji, Chao Guan, Xuejun Jiang _ and Hong Li

Abstract

Xu Ji1, Chao Guan1, Xuejun Jiang1, Hong Li2

1Department of Otolaryngology, The First Affiliated Hospital of China Medical University, Shenyang, 110001, China

2Department of Otorhinolaryngology Head and Neck Surgery, The Fourth Affiliated Hospital of China Medical University, Shenyang, 110032, China

Correspondence to:

Xuejun Jiang, email: djiangxj@163.com.

Hong Li, email: zhaohuan_2000@163.com.

Keywords: head and neck cancer, DNA methylation, diagnostic accuracy, biopsy type, meta-analysis

Received: March 27, 2016     Accepted: September 02, 2016     Published: September 23, 2016

ABSTRACT

Abnormal methylation of certain cancer related genes strongly predicts a diagnosis of head and neck cancer (HNC), while the predictive power of methylation of other DNA markers for HNC remains unclear. To systemically assess the diagnostic value of DNA methylation patterns for HNC and the effect of methylation platform techniques and sample types, we performed a PubMed search for studies of the correlation between DNA methylation and HNC completed before July 2016, and extracted the sensitivity and specificity for methylated biomarkers. Across these studies, DNA methylation showed high sensitivity for diagnosing HNC in solid tissue (0.57), and high specificity in saliva (0.89). Area under the curve (AUC) from summary receiver operating characteristic (SROC) curves revealed that DNA methylation had more diagnostic power in solid tissue (AUC = 0.82) than saliva (AUC = 0.80) or blood (AUC = 0.77). Combinations of multiple methylated genes were more sensitive diagnostic markers than single methylated genes. Our results suggest that the diagnostic accuracy of methylated biomarkers for HNC varied by sample type and were most accurate when results from multiple sample types were considered.


Diagnostic accuracy of DNA methylation for head and neck cancer varies by sample type and number of markers tested | Ji | Oncotarget

INTRODUCTION

For this study, we selected from the literature reports of common squamous-cell carcinomas of the oral cavity, pharynx, and larynx (HSCC), which account for 90% of HNC [1]. Approximately 650,000 cases of HNC are identified per year [2] and the disease has high recurrence rates and poor prognoses due to distant metastasis [3]. Late diagnosis results in poorer prognosis [4]. Improved diagnostic accuracy for HNC could lead to earlier diagnosis, increasing patient survival rates.

Variations in the epigenetic modifications, such as DNA methylation in gene promoters, often inhibit gene transcription and protein translation, important factors in human carcinogenesis. A number of genes are frequently methylated in HNC, including p16, DAPK1, and RASSF1A [5, 6], or hypermethylated in CpG islands, such as hMLH1 [7], KIF1A, and EDNRB [8]. Many groups have identified abnormally methylated genes as HNC diagnostic biomarkers but their predictive accuracies fluctuated among different sample types. Moreover, there are no systematic diagnostic accuracy studies or meta-analyses regarding the various sample types in HNC. We performed a systematic review and stratified meta-analysis of previous HNC studies based on sample types and diagnostic markers. We aim to provide more reliable evidence to clarify the diagnostic accuracy of DNA methylation markers, according to published reports that computed sensitivity and specificity.

RESULTS

Study characteristics

We identified 108 papers in a search of the PubMed database. Seventy-nine were excluded based on screening the title and abstract, including twenty-eight papers that did not involve HNC, thirty-six papers that did not investigate the cancer diagnoses, eleven papers that did not include a diagnosis based on DNA methylation, and four reviews. We obtained the full texts of twenty-nine papers; of these five further papers were excluded, including two studies that did not show the sensitivity and specificity of the methylation biomarkers in a HNC diagnosis and three studies that only investigated the diagnosis of recurrence. We identified 183 studies from the remaining twenty-four articles [831] (Figure 1). In addition, we added 20 articles including 25 studies to our analysis from a review [3251]. These studies were conducted in fifteen countries or regions (including the USA, Brazil, China, Hong Kong, Japan, Australia, Sweden, Egypt, Thailand, India, Taiwan, Hungary, Turkish, French and Italy) and were published between 2002 and 2016. The sample sizes of these studies ranged from 31–597 patients, with a mean of 115.

Flow chart showing the study retrieval process.

Figure 1: Flow chart showing the study retrieval process.

The diagnostic accuracy of selected methylated genes was extracted from the included papers and grouped by sample type tested. Ten papers used saliva [9, 10, 12, 14, 17, 27, 29, 32, 50], sixteen papers used solid tissue [11, 15, 16, 18, 19, 23-26, 30, 31, 33-38, 40-46, 49], four papers used blood [21, 28, 31, 39], five papers used both solid tissue and saliva [8, 13, 22, 47, 51], and two papers used both solid tissue and blood (Table 1) [20, 33]. The studies evaluated the diagnostic power of methylation biomarkers as follows: thirty-five studies were based on a single gene [8, 12-14, 16-19, 21, 23, 25-28, 30, 32-51], two papers were based on multiple genes [9, 22] and seven papers were based on both single and multiple genes [10, 11, 15, 20, 24, 29, 31]. The details of methylated biomarkers and their diagnostic powers are shown in Supplementary Table 1.

Table 1: The included studies investigating the diagnosis of DNA methylation biomarkers in head and neck cancer

Study

Country

Case#

Control#

Sample

Biomarker

Technique

Methylated genes

Liu et al, 2016

China

246

246

Tissue

S

BeadChip

S100A8

Nawaz et al, 2015

Sweden

44

18

Tissue

S, M

MSP

EBNA1, LMP1, RASSF1A, DAPK, ITGA9, P16, WNT7A, CHFR, CYB5R2, WIF1, RIZ1, FSTL1

Arantes et al, 2015

Brazil

40

40

Saliva

S, M

qMSP

TIMP3, DCC, DAPK, CCNA1, AIM1, MGMT, CDH1, HIC1

Kis et al, 2014

Hungary

60

68

Saliva

S

MSP

P16

Bhatia et al, 2014

India

76

70

Tissue, Blood

S

MSP

P16

Dang et al, 2013

China

12

30

Tissue

S

MSP

P16

Puttipanyalears 2013

Thailand

88

161

Saliva

S

COBRA

ALU

Tian et al, 2013

China

40

41

Blood

S

MSP

RASSF1A, CDKN2A, DLEC1, DAPK1, UCHL1

Rettoriet al, 2013

Brazil

68

60

Tissue

S

BS

CCNA1, DAPK, MGMT, SFRP1, TIMP3

You et al, 2013

China

40

40

Blood

S

MSP, BS

CDK10

Schussel et al, 2013

USA

48

113

Saliva

S, M

qMSP

DCC, EDNRB

Ovchinnikov et al, 2012

Australia

143

31

Saliva

M

MSP

RASSF1A, p16, DAPK1

Minor et al, 2012

USA

59

48

Tissue

S, M

MSP

miR-9-1, miR-9-3

Nagata et al, 2012

Japan

34

24

Saliva

S

MSP

ECAD, TMEFF2, RARβ, MGMT, FHIT, WIF-1, DAPK, p16, HIN-1, TIMP3, p15, APC, SPARC

Zhange et al, 2012

Sweden

49

20

Tissue

S

MSP

EBNA1, LMP1, RASSF1A, DAPK

Demokan et al, 2011

Turkish

60

77

Tissue

S

MSP

P16

Li et al, 2011

China

47

15

Tissue

S, M

MSP

P16, DAPK, RARb, CDH1, RASSF1A

Weiss et al, 2011

Germany

51

31

Tissue

S

MSP

P16

Gyobu et al, 2011

Japan

40

8

Tissue

S

qMSP

PAX6, ENST00000363328

Loyo et al, 2011

Hong Kong

50

28

Tissue

S, M

qMSP

AIM1, APC, CALCA, DCC, DLEC, DLC1, ESR, FHIT, KIF1A, PGP9.5, TIG1

Guerrero-Preston et al, 2011

USA

24

12

Tissue, Saliva

S

BeadChip, qMSP

HOXA9, NID2, GATA4, KIF1A, EDNRB, DCC, MCAM, CALCA

Laytragoon et al, 2010

Sweden

41

18

Tissue

S

MSP

P16

Pattani et al, 2010

USA

48

113

Saliva

S

qMSP

EDNRB

Kaur et al, 2010

India

92

48

Tissue, blood

S

qMSP

P16

Tawfik et al, 2010

Egypt

34

15

Tissue

S

MSP

hMLH1

Su et al, 2010

Taiwan

30

30

Tissue

S

qMSP

P16

Cao et al, 2009

China

22

56

Tissue

S

MSP

P16

Steinmann et al, 2009

Germany

54

23

Tissue

S

MSP

P16

Ghosh et al, 2009

India

63

40

Tissue

S

MSRA

India

Viet et al, 2008

USA

13

23

Tissue, saliva

M

BeadChip

GABRB3, IL11, INSR, NOTCH3, NTRK3, PXN, ERBB4, PTCH2, TMEFF1, TNFSF10, TWIST1, ADCYAP1, CEBPA, EPHA5, FGF3, HLF, AGTR1, BMP3, FGF8, NTRK3, FLT, IRAK3, KDR, NTRK, RASGRF1, WT1, ESR1, ETV1, GAS7, PKD2, WNT2, EPHA5, GALR1, KDR, p16, AGTR1, EYA4, IHH, NTRK3, NTRK3, TFPI2

Adams et al, 2008

USA

51

50

Tissue, blood

S, M

qMSP

AHRR, p16, CBRP, CLDN3, MT1G, MGMT, RARβ, PGP9.5

Carvalho et al, 2008

USA

135

462

Tissue, saliva

S

qMSP

DCC, DAPK, ESR, CCNA1, CCND2, MINT1, MINT31, CDH1, AIM1, MGMT, p16, PGP9.5, RARβ, HIC1, RASSF1A, CALCA, TGFBR2, S100A2, RIZ1, RBM6

Righimi et al, 2007

French

90

30

Tissue, saliva

S

MSP

P16

Franzmann et al, 2007

USA

102

69

Saliva

S

MSP

CD44

Martone et al, 2007

Italy

20

11

Tissue

S

MSP

P16

Shaw et al, 2006

UK

80

26

Tissue

S

Pyro

P16

Maruya et al, 2004

USA

14

32

Tissue

S

MSP

P16

Kulkarni et al, 2004

India

60

60

Tissue, saliva

S

MSP

P16

Weber et al, 2003

Germany

50

42

Tissue

S

MSP

P16

Wong et al, 2003

China

73

29

Tissue, blood

S

MSP

P16

Tong et al, 2002

Hong Kong

28

26

Tissue

S

MSP

EBV

Nakahara et al, 2001

Japan

32

32

Tissue

S

MSP

P16

Rosas et al, 2001

USA

30

30

Saliva

S

MSP

P16

Sanchez et al, 2000

USA

95

26

Blood

S

MSP

P16

S represented single methylated gene as diagnostic marker, and M represented combination of multiple methylated genes as diagnostic marker.

Exploration of heterogeneity analysis

To determine the effect model of diagnostic accuracy, we conducted heterogeneity tests for PLR, NLR, and DOR and found a significant heterogeneity of NLR in the solid tissue and saliva studies (Table 2). DOR showed no heterogeneity in the solid tissue or blood studies. PLR showed low heterogeneity in the solid tissue studies and no heterogeneity in the saliva or blood studies. The heterogeneity of NLR varied among the sample types.

Table 2: Heterogeneity analysis of diagnostic effects

Sample

Effects

Estimate[95% CI]

Log(Estimate) [95% CI]

df

Q

P-value

I2

All

PLR

3.45[3.07-3.88]

1.24[1.12-1.35]

207

257.16

0.01

19.51%

NLR

0.62[0.59-0.64]

-0.48[-0.52 to -0.44]

207

578.57

<0.01

64.22%

DOR

7.84[6.56-9.35]

2.06[1.88-2.24]

207

242.98

0.044

14.81%

Saliva

PLR

3.60[2.97-4.37]

1.28[1.09-1.47]

75

63.44

0.827

0%

NLR

0.71[0.67-0.74]

-0.35[-0.40 to -0.30]

75

290.26

<0.01

74.16%

DOR

6.84[5.45-8.59]

1.92[1.70-2.75]

75

106.90

0.01

29.84%

Tissue

PLR

3.85[3.08-4.83]

1.35[1.13-1.57]

70

81.91

0.156

14.54%

NLR

0.52[0.47-0.57]

-0.66[-0.76 to -0.56]

70

117.57

<0.01

40.46%

DOR

10.96[7.57-15.89]

2.40[2.02-2.77]

70

68.33

0.534

0%

Blood

PLR

2.76[1.89-4.03]

1.01[0.63-1.39]

10

10.26

0.418

2.57%

NLR

0.65[0.54-0.78]

-0.43[-0.61 to -0.25]

10

15.19

0.125

34.17%

DOR

5.42[2.98-9.86]

1.69[1.09-2.29]

10

8.28

0.60

0%

PLR: positive likelihood ratio. NLR: negative likelihood ratio. DOR: diagnostics odd ratio. Estimate [95% CI]: the pooled effect measure with the corresponding 95% confidence interval. Log (Estimate) [95% CI]: logarithmic transformation of the pooled effect measure with the corresponding 95% confidence interval. df: degrees of freedom. Q and P-value were the Q value and p value of Cochran's Q test.

We used meta-regression analysis to assess whether publication year, sample type, DNA methylation detection technique, or the methylation panel corresponding to single or multiple methylated biomarkers affected the diagnostic accuracy for HNC. The true and false positive rates were used as the responses in meta-regression analyses. As shown in Table 3, the p values of sensitivity and false positive rates were not significant, suggesting that publication year, biomarker technique, and sample types did not affect the false positive rate.

Table 3: Meta-regression analysis

Factor

Sensitivity

False positive rate

Coefficient

p value

Coefficient

p value

Year

0.132

0.070

0.018

0.559

Marker type

0.15

0.489

-0.193

0.379

Technique

-0.185

0.051

-0.023

0.824

Sample

0.068

0.345

0.098

0.186

Meta-analysis and diagnostic accuracy

The pooled sensitivity and specificity of meta-analysis was 0.52 (95% CI 0.47-0.57) and 0.87 (95% CI: 0.85-0.89), respectively. Meta-analysis was performed separately for the saliva, solid tissue, and blood samples. DNA methylation detected from saliva samples had an overall sensitivity and specificity for HNC diagnosis of 0.47 (95% CI: 0.39-0.55) and 0.89 (95% CI: 0.85-0.91), respectively (Figure 2A). In solid tissue samples the overall sensitivity and specificity were 0.57 (95% CI: 0.50-0.63) and 0.88 (95% CI: 0.84-0.9), respectively (Figure 2B). Blood samples provided the lowest overall sensitivity at 0.46 (95% CI: 0.32-0.61), and overall specificity of 0.85 (95% CI: 0.77-0.91, Figure 2C).

Forest plot of estimate of diagnostic accuracy using methylated biomarkers.

Figure 2: Forest plot of estimate of diagnostic accuracy using methylated biomarkers. A. Forest plot of estimate of sensitivity and specificity of methylated biomarkers in saliva. B. Forest plot of estimate of sensitivity and specificity of methylated biomarkers in tissue. C. Forest plot of estimate of sensitivity and specificity of methylated biomarkers in blood.

In addition, we evaluated the diagnostic power based on the types of methylation biomarkers. The single methylation markers had overall sensitivity and specificity of 0.51 (95% CI: 0.45-0.57) and 0.87 (95% CI: 0.84-0.90), respectively. The diagnostic sensitivity of multiple methylation markers was 0.55 (95% CI: 0.47-0.63) and the specificity was 0.88 (95% CI: 0.85-0.90). In general, methylated biomarkers showed differential diagnostic accuracy in all three sample types, and the diagnostic power of integrating multiple methylated genes was better than with a single gene.

According to the sensitivity and specificity results from each trial, the regression coefficients of the SROC curves were near 0 for the three sample types (Table 4). The AUC curve indicated that samples from solid tissue had the highest diagnostic accuracy, with an AUC value of 0.82 (Figure 3) and a Q* metric of 0.81 (95% CI: 0.77–0.85), whereas the sensitivity was identical to the specificity (Table 4, Figure 3). In addition, the panel of multiple methylated genes showed higher AUC value than a single methylated gene (0.85 vs. 0.77). These results suggest that the combination of multiple methylation biomarkers in solid tissue has better diagnostic accuracy, with higher sensitivity in saliva, which could be useful for HNC screening.

Table 4: The main analysis results of SROC

Sample

Sensitivity (95% CI)

Specificity (95% CI)

a (95% CI)

b (95% CI)

AUC

Q* (95% CI)

Saliva

0.47 (0.39 - 0.55)

0.89 (0.85 - 0.91)

2.14 (1.71-2.56)

-0.02 (-0.14-0.09)

0.80

0.74 (0.70-0.78)

Tissue

0.57(0.5 - 0.63)

0.88(0.84 - 0.90)

2.91 (2.37 - 3.44)

0.07 (-0.11 - 0.25)

0.82

0.81 (0.77-0.85)

Blood

0.46 (0.32 - 0.61)

0.85 (0.77 -.91)

1.75 (0.,74 - 2.77)

-0.09 (-0.42 - 0.24)

0.77

0.71 (0.59 - 0.80)

Sensitivity and Specificity represent the independent pooled sensitivity (Se) and specificity (Sp) using fixed effect model. a and b represent the intercept and slope of SROC curve. AUC represent the area under SROC curve. Q* represents the diagnostic threshold at which the probability of a correct diagnosis is constant for all subjects and calculated as exp(a/2)/[1+exp(a/2)].

SROC curves of studies relating to the detection of HNC in different biopsy types.

Figure 3: SROC curves of studies relating to the detection of HNC in different biopsy types.

Publish bias and sensitivity analysis

The risk of bias for each study was first assessed (Supplementary Figure 1). As shown in Figure 4, 92% of studies showed a low or unclear risk of bias for many bias items and only 5 ~ 18% of studies clearly reported a non-random sequence generation, no blinding, or incomplete blinding. More than 50% of the studies had independent data collection, assessment of DNA methylation, and interpretation of outcomes. In total, 75% of the studies show the sensitivities and specificities for all of the evaluated methylation biomarkers, which suggested no selective reporting. Ten studies were reported to be free of other sources of bias. Based on these metrics we deemed the quality of the studies included in the following meta-analysis to be acceptable.

Risk of bias graph.

Figure 4: Risk of bias graph. Two authors independently evaluated the items of bias. If the study reported all of the sensitivities and specificities of genes that were measured DNA methylation status, selective reporting was defined as low risk.

By testing the relationship between the DOR and its standard error, we assessed the publication bias effects of the sample size for each diagnostic consequence. The potential publication bias was ascertained in these studies using symmetrical funnel plots for the saliva, solid tissue, and blood samples. We found that some studies corresponding to saliva (Figure 5A) or solid tissue (Figure 5B) were not inside the funnel. Begg’s testing demonstrated that there was no significant publication bias in the three sample types from HNC patients. The studies with smaller sample sizes did not tend towards higher levels of accuracy.

Funnel plot to assess bias in estimates of diagnostic odds ratio caused by small-study effects.

Figure 5: Funnel plot to assess bias in estimates of diagnostic odds ratio caused by small-study effects. A. Saliva. B. Solid tissue. C. Blood.

A one-way sensitivity analysis was performed to evaluate the robustness of the results of this meta-analysis with respect to study and biomarker. The pooled specificity was not influenced when removing one study or diagnostic biomarker (Supplementary Table 2, 3). The sensitivity was increased when the studies by Carvalho et al. and Adams et al. were excluded, and was decreased when the study by Arantes et al. was excluded (Supplementary Table 2). The exclusion of individual methylated markers had no effect on diagnostic sensitivity.

DISCUSSION

DNA methylation has previously been demonstrated to be a potentially useful marker for multiple cancers [52]. Abnormally methylated regions in cancer-related genes such as RASSF1A [24], p16[53], RAR-β[24], and MGMT [54], provide adequate sensitivity and specificity for the detection of HNC. Other abnormally methylated genes have shown inconsistent diagnostic accuracy for HNC. For example, the sensitivity of p16 for diagnosing HNC varied from 44.6% to 100% [19, 55].

In this study, we analyzed the accuracy of methylated genes for diagnosing HNC based on previously published studies. Overall, the sensitivity of the DNA methylation was 0.47 in saliva, 0.57 in solid tissue and 0.46 in blood, and the specificity was 0.89, 0.88 and 0.85, respectively. We found that DNA methylation had low sensitivity but high specificity in the diagnosis of HNC. Different samples showed similar specificity but differential sensitivity. Seven papers corresponding to eleven studies were used to estimate the diagnostic accuracy of DNA methylation in blood. The small number of studies may provide misleading conclusions on the diagnostic power in blood that should be further evaluated. Moreover, testing for multiple methylated genes showed higher sensitivity than single methylated genes. Ideally, we should assess the overall diagnostic accuracy of the same combinations of methylated genes or single genes in three different sample types, but were limited by the number of studies available in the literature. We provide detailed information on the combinations of multiple methylated genes in Figure 2. Behind each author's name is the applicable DNA methylation marker information for the specific study. The evidence from this study suggests that DNA methylation biomarkers might be effective tools for detecting HNC. It should also be noted that the diagnostic accuracy of DNA methylation depends on the sample type and diagnostic markers studied.

Many of the studies in our analysis detected DNA methylation based on methylation-specific PCR (MSP), one of the principle methods of investigating DNA methylation. MSP typically overestimates the extent of methylation, which would affect the diagnosis of HNC. We studied whether the assay method of DNA methylation affects the HNC diagnosis, but found no significant differences among these methods.

MATERIALS AND METHODS

Data sources and search strategy

We searched for diagnostic studies in PubMed published before July 2016. The search strategy for PubMed was (“head and neck neoplasms” [MeSH Terms] OR “head and neck cancer” [All Fields]) AND (“sensitivity and specificity”[MeSH Terms] OR “sensitivity and specificity” [All Fields] AND (“DNA methylation” [MeSH Terms] OR “DNA methylation” [All Fields]) to find appropriate studies published in English prior to July 25, 2016. We searched for published trials that evaluated the diagnostic accuracy of one or more methylated biomarkers. In addition, we added all studies included in a previous meta-analysis into our analysis [56].

Study selection

Two reviewers independently filtered the search results by the title and abstract. Studies were excluded if they did not pertain to DNA methylation, were not related to HNC, were not diagnostic studies, or were reviews. Two authors obtained the full text of each paper and further filtered out the studies that did not supply sensitivity or specificity data for HNC diagnosis or that concerned the diagnosis of recurrence. We collected the authors’ names; institutions; publication dates; sample types, including saliva, solid tissue, and blood; methylated biomarkers; and techniques of DNA methylation detection for all of the studies. All studies were evaluated independently and discussed by the authors until any inconsistencies were resolved.

Data extraction and quality assessment

A standardized data extraction form was used to extract the information from each paper including the first author, year of publication, country in which the study was performed, number of cases and controls, sample types, methylated gene names, DNA methylation detection techniques, number of methylated genes used as diagnostic biomarkers, and records of true positive, false positive, true negative and false negative results in head and neck cancer. Simultaneously, we evaluated the risk of bias according to pre-specified criteria from the Cochrane Collaboration’s tool for assessing the risk of bias [57]. The two authors independently checked the risk of bias assessment for each trial using standardized methods including the following (Supplementary Table 4): whether a study showed selection bias including sequencing generation and allocation concealment; whether the performance was biased regarding the blinding of patients and study personnel; whether the detection was biased according to the assessment of the blinding of the outcome; and whether the attrition and reporting were biased by being based on incomplete outcome data and selective reporting, respectively.

Data synthesis and statistical analysis

We extracted the number of true positive (TP), false positive (FP), true negative (TN) and false negative (FN) results based on the remaining studies. The summary effects of the positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and 95% confidence intervals (CI) were further computed to estimate the statistical heterogeneities through Cochran’s Q test, that approximately follows a χ2 distribution with k-1 degrees of freedom (where k is the number of included studies) [58]. We assessed I2 = ((Q-(k-1))/Q) ×100%, which ranged from 0–100%. I2 represents different degrees of heterogeneity, including low (0–25%), moderate (25–50%), high (50–75%) and very high (75–100%) [58]. The p value of the heterogeneity test determined whether a fixed- or random-effects model was used to estimate the diagnostic effects, and the significance level of heterogeneity was considered to be 0.05. The overall sensitivity and specificity were estimated to represent the diagnostic power of DNA methylation for the detection of head and neck cancer. For the overall diagnostic accuracy, an SROC curve was generated based on the sensitivity and specificity of each study using the equation D=a+b×S, where D = logit(Se) – logit(1-Sp) = log(OR) and S = logit(Se) +logit(1-Sp) [59]. In the regression equation, D represents the diagnostic power of the methylated biomarkers, and S represents the threshold of the classification between positive and negative. Because the parameters D and S are from different studies, the values of the regression coefficient closer to 0 suggested less significant heterogeneity in various studies, which corresponds to diagnostic accuracy. The area under the SROC curve (AUC) value was estimated to measure the overall diagnostic power of DNA methylation in individual studies. In addition, Q* = Se = 1-Sp was computed according to the regression equation of SROC, where Se = exp(a/2)/[1+exp(a/2)] and 1-Sp = 1/[1+exp(a/2)], which suggested that the diagnostic threshold for a correct diagnosis was constant for all of the subjects. Publication bias was evaluated using funnel plot analyses and Begg’s and Egger's tests, with a significance level defined as 0.01. We used the mada and metafor package in R to performed the statistical analysis [60].

CONFLICTS OF INTEREST

DTR: none; SO: Consulting: Novartis, BMS, MSD; KK: none; TYS: Honoraria: Astra Zeneca, Amgen, BMS, Innate, Jounce, SD; FK: none; IT: none; UK: Honoraria: AmGen, Astra Zeneca, BMS, Glycotope, GSK, Merck Serono, Merck/MSD, Pfizer, Research Support: Innate Pharma, Sirtec, Pfizer.

DISCLOSURES

The authors declare no conflicts of interest.

Authors contribution

XJ and HL conceived the hypothesis and revised the manuscript. CG and XJ searched the literature and extracted the data. XJ performed the meta-analysis and wrote the manuscript. HL and XJ filtered the literature.

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