Cumulative Evidence for Associations Between Genetic Variants and Risk of Esophageal Cancer

Author Manuscript Published OnlineFirst on January 22, 2020; DOI: 10.1158/1055-9965.EPI-19-1281 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.

Cumulative Evidence for Associations Between Genetic Variants and

Risk of Esophageal Cancer

Gaoming Li 1, Qiuyue Song 1, Yuxing Jiang 2, Angsong Cai 1, Yong Tang 1, Ning Tang 1, Dali Yi 1,

Rui Zhang 1, Zeliang Wei 1, Dingxin Liu 3, Jia Chen 1, Yanqi Zhang 1, Ling Liu 1, Yazhou Wu 1,

Ben Zhang 4 and Dong Yi 1

1Department of Health Statistics, Army Medical University, Chongqing, China

2Medical Department, The 305 Hospital of Chinese People's Liberation Army, Beijing, China

3Department of Statistics, Chongqing Technology and Business University, Chongqing, China

4Department of Epidemiology and Biostatistics, First Affiliated Hospital, Army Medical

University, Chongqing, China

Corresponding Authors: Dong Yi, Department of Health Statistics, School of Public Health,

Army Medical University, Chongqing 400038, China. Phone: +86 23 6877 1562; Email:

[email protected]; Ben Zhang, Department of Epidemiology and Biostatistics, First Affiliated

Hospital, Army Medical University, Chongqing 400038, China. Phone: +86 23 6875 4311; Email:

[email protected]; and Yazhou Wu, Department of Health Statistics, School of Public

Health, Army Medical University, Chongqing 400038, China. Phone number: +86 23 6877 1563;

Email: [email protected]

Running Title: Genetic Associations with Esophageal Cancer

Abbreviations: CIs, confidence intervals; DNase I, Deoxyribonuclease I; EAC, esophageal

adenocarcinoma; eQTL, expression quantitative trait loci; ESCC, esophageal squamous cell

carcinoma; MAF: minor allele frequency; FPRP, false-positive report probability; GWAS,

genome-wide association studies; HWE, Hardy-Weinberg equilibrium; MAF, minor allele

frequency; ORs, odds ratios.

Keywords: esophageal cancer; genetic variants; epidemiological evidence; meta-analysis

Disclosure of Potential Conflicts of Interest: No potential conflicts of interest were declared. 1

Downloaded from cebp.aacrjournals.org on September 29, 2021. © 2020 American Association for Cancer Research. Author Manuscript Published OnlineFirst on January 22, 2020; DOI: 10.1158/1055-9965.EPI-19-1281 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.

Abstract

Background: A large number of studies have been conducted to investigate associations between

genetic variants and esophageal cancer risk in the past several decades. However, findings from

these studies have been generally inconsistent. We aimed to provide a summary of the current

understanding of the genetic architecture of esophageal cancer susceptibility.

Methods: We performed a comprehensive field synopsis and meta-analysis to evaluate

associations between 95 variants in 70 genes or loci and esophageal cancer risk using data from

304 eligible publications, including 104,904 cases and 159,797 controls, through screening a total

of 21,328 citations. We graded levels of cumulative epidemiological evidence of a significant

association with esophageal cancer using the Venice criteria and false-positive report probability

tests. We constructed functional annotations for these variants using data from the Encyclopedia of

DNA Elements Project and other databases.

Results: Thirty variants were nominally significantly associated with esophageal cancer risk.

Cumulative epidemiological evidence of a significant association with overall esophageal cancer,

esophageal squamous cell carcinoma, or esophageal adenocarcinoma was strong for 13 variants in

or near 13 genes (ADH1B, BARX1, CDKN1A, CHEK2, CLPTM1L, CRTC1, CYP1A1, EGF,

LTA, MIR34BC, PLCE1, PTEN and PTGS2). Bioinformatics analysis suggested that these

variants and others correlated with them might fall in putative functional regions.

Conclusion: Our study summarizes current literature on the genetic architecture of esophageal

cancer susceptibility and identifies several potential polymorphisms that could be involved in

esophageal cancer susceptibility.

Impact: These findings provide direction for future studies to identify new genetic factors for

esophageal cancer.

Introduction

Esophageal cancer, the sixth-leading cause of cancer-related deaths, is one of the most

aggressive diseases worldwide. Both genetic components and environmental factors play a role in

the pathogenesis of this malignancy. Epidemiological studies indicate that cigarette smoking,

heavy alcohol consumption, high intake of nitrosamine-rich or pickled vegetables, nutritional

deficiencies, and low socioeconomic status may contribute to esophageal carcinogenesis (1,2).

However, only some of the exposed population develop cancer, suggesting that the genetic

makeup of the individual may also play a crucial role in the development of esophageal cancer.

Recently, genome-wide association studies (GWAS) identified several susceptibility loci for

esophageal cancer (3-12), but only a few overlap across studies. These genes and loci explain

approximately 7.0% of the heritability of the disease (13).

Despite efforts using high-throughput genotyping technologies, candidate gene approaches

that are cost-effective and convenient remain the mainstay of investigations to identify genetic

susceptibility factors for esophageal cancer. In the past two decades, over 200 candidate genes,

involving more than 800 genetic variants, have been investigated in candidate gene studies for

predisposition to esophageal cancer. With accumulating information and conflicting conclusions, it

is difficult to identify, explain, and interpret genetic associations between common variants and

esophageal cancer risk. To address this issue, a comprehensive meta-analysis of the research

synopsis of genetic associations is a useful tool that has been utilized in several diseases (14,15).

In the present study, we sought to systematically collect and summarize all candidate gene studies

in the field of esophageal cancer and to perform a meta-analysis of articles for all polymorphisms

from at least three independent data sources. Furthermore, we applied the Venice criteria

developed by the Human Genome Epidemiology Network (HuGENet) to assess the cumulative

epidemiological evidence of significant associations (16). Our study is the first attempt to evaluate

the genetic susceptibility factors of esophageal cancer with all available genetic association data.

The identification of genetic variants may provide new insights into the causes of esophageal

cancer.

Materials and Methods

Selection criteria and search strategy

The included literature for this study needed to satisfy the following criteria: (1) the study

must have been published in English in a peer-reviewed journal before December 31, 2018; (2) the

research design had to be a human-related case-control, cohort or cross-sectional study; (3)

patients with esophageal cancer must have been diagnosed by pathological and/or histological

examination; and (4) publications must have provided sufficient information for the genotype data

to allow for the calculation of odds ratios (ORs) and corresponding 95% confidence intervals (CIs).

The exclusion criteria were as follows: (1) articles with a family-based association design; or (2)

articles for which only the abstracts were available.

We adopted a two-stage literature search to identify all relevant publications (Figure 1). First,

using the terms "(esophageal cancer OR oesophageal cancer) AND association", we retrieved

articles published in PubMed between the establishment of the database and May 25, 2018. This

process retrieved 18,541 related articles and identified 261 articles satisfying the inclusion criteria

after the title, abstract, or full text (if necessary) were screened. The articles retrieved included 208

candidate genes or chromosomal regions. Second, targeted monthly searches on PubMed between

May 25, 2018 and December 31, 2018 were performed using the 208 retrieved candidate genes or

chromosomal regions (e.g., "XRCC1" or "rs1799782") and "esophageal cancer OR oesophageal

cancer" as search terms. Meanwhile, the references of the included literature and previous

meta-analyses or reviews on esophageal genetic association studies were screened. A total of 2787

relevant publications were retrieved through the second-stage search strategy, of which 19

additional candidate genes or chromosomal regions in 43 articles met the inclusion criteria.

Therefore, 21,328 articles were retrieved after a two-phase process, and 304 articles were

ultimately identified that reported 836 variants in 227 candidate genes or chromosomal regions.

Data extraction and management

Data were extracted independently by two researchers (Gaoming Li, Yuxing Jiang) using a

unified data table. Any inconsistencies in data extraction results were resolved by consensus. The

major extracted information included the following: first author, publication year, country,

ethnicity, histological type, study design, sample source, mean ages of cases and controls, genes,

variants, sample size, major and minor alleles, genotype distribution in cases and controls, and

Hardy-Weinberg equilibrium (HWE) of the controls. Ethnicity comprised four categories: African,

Asian, Caucasian, or other (including mixed), based on the criterion that at least 80% of the study

populations belonged to one of these groups. If the ethnicity of the source population was not clear,

we considered the ethnicity using the geographic location in which the study was performed. The

histological type was subdivided into three subtypes: esophageal squamous cell carcinoma

(ESCC), primary esophageal adenocarcinoma (EAC) or mixed, based on the criterion that at least

80% of the histological types of the subjects belonged to one group (17). For studies with

redundant information, only the studies with the highest quality, largest sample size and most

detailed information were selected. In addition, data were extracted separately if the study

included several research sources or study populations.

Statistical analysis

R3.3.2 and Stata 13.1 was used for statistical analysis. All tests were bilateral, and P<0.05

was considered statistically significant unless otherwise stated. The meta-analysis of each

variation had to contain at least three data sets to allow the investigators to obtain stable

heterogeneity test statistics and to ensure sensitivity analysis. Fisher’s exact probability method

was used to evaluate whether each study in the control group conformed to HWE by comparing

the observed and expected gene frequencies (18). The DerSimonian-Laird random-effects model

was used to estimate the association between genetic variants and cancer risk by ORs and

corresponding 95% CIs (19). Allelic, dominant and recessive models were adopted for common

variants that had a minor allele frequency (MAF) greater than 0.05, while for rare (MAF<0.05) or

phenotype traits, only appropriate dominant or recessive models were adopted. Since the major

and minor alleles may be reversed in populations of diverse ancestry, the average MAF in the

study may be greater than 50%. If present, the minor alleles of Caucasians were designated as the

minor alleles in all analyses. Stratified analysis was then performed using ethnicity or histological

type.

Cochran’s Q test was used to analyze the heterogeneity between studies, and P<0.10 was

considered significant evidence of heterogeneity. Small studies are often included in

meta-analyses, and Cochran's Q test is poor at detecting true heterogeneity in such circumstances.

The use of 0.1 as the significance level ameliorated the problem of a low statistical power but

increased the risk of drawing a false-positive conclusion (type I error). Thus, the I2 statistic was

also used to quantitatively evaluate the heterogeneity and ranged from 0% to 100%; the higher the

value of I2 was, the greater the heterogeneity. In general, I2 statistics less than 25% indicated mild

heterogeneity; I2 statistics between 25% and 50% indicated moderate heterogeneity; and I2

statistics greater than 50% indicated high heterogeneity (20,21). Compared with Q, the I2 statistic

did not vary with the number of publications, and its value could be compared among

meta-analyses with different numbers of studies. Sensitivity analysis was performed to assess the

robustness of the results and to evaluate the stability of the conclusions through the performance

of a new meta-analysis via changes in some key factors affecting the results. These factors

included elimination of the first published report, elimination of all small studies (n<300), and

elimination of all studies in which the control group did not conform to the HWE. A funnel plot

was drawn using the logORs versus standard errors to analyze the presence of publication bias in

the included studies, and Begg’s rank correlation test was used to test the asymmetry of the funnel

plot (22). The modified linear regression method proposed by Harbord was adopted to evaluate

the potential bias of small studies, with P<0.10 indicating statistically significant differences (23).

This method was based on the correction of the Z-statistic of the test score and its variance in the

traditional Egger linear regression method, which can avoid the risk of type I error. Finally, the

power of the meta-analysis was calculated to detect a statistically significant association at a

significance level α=0.05 and a disease prevalence of 0.5% (24) for certain allele frequencies

(estimated using Genetic Association Study Power Calculator) (25).

Assessment of epidemiological credibility

We evaluated the strength of the epidemiological evidence using the Venice criteria proposed

by the Human Genome Epidemiology Network (HuGENet) Working Group (16). The grading was

independently completed by two investigators. Briefly, each significant association identified by

meta-analysis was graded according to three criteria: amount of evidence, replication of 6

association, and protection from bias. For amount of evidence, a grade of A was assigned for a

sum of minor alleles among cases and controls of more than 1000 in the meta-analysis, B for a

sum of between 100 and 1000, and C for a sum of less than 100. It should be noted that this

criterion did not apply to rare variants (MAF≤1%) since grade A is virtually unobtainable. For

replication of association, a grade of A was assigned for heterogeneity statistic I2 values of less

than 25%, B for I2 values between 25% and 50%, and C for I2 values of more than 50%. When

associations with moderate or high heterogeneity on the criterion of replication had been

replicated widely by large collaborative studies, such as GWAS or GWAS meta-analysis, grade A

is suitable to be graded to them (26). The cumulative evidence and major conclusions may be

affected by errors in phenotypes, genotypes and biases at the meta-analysis level (selection

reporting and publication biases). Thus, adequate protection from bias is needed. We assigned a

grade of A if there was no observable bias that could affect the result of the genetic association, a

grade of B if no strong bias was visible but there was considerable missing information for its

appraisal, and a grade of C if there was clear bias that could affect the presence of the association.

Finally, the epidemiological credibility for significant genetic association was rated as strong if all

criteria grades were A, moderate if at least one criterion grade was A but no grades were C, and

weak if any criteria grades were C (16).

The false-positive report probability (FPRP) of each significant result was calculated to

determine whether an association can be taken as a false-positive finding. A prior probability of

0.05 was set to detect an OR of 1.5 based on the methods developed by Wacholder et al (27). The

strong, moderate, and weak evidence of a true association were assigned for FPRP<0.05,

0.05≤FPRP≤0.20 and FPRP≥0.20, respectively. Epidemiological credibility was upgraded from

weak to moderate or from moderate to strong for results with strong evidence of FPRP values,

whereas epidemiological credibility was downgraded from strong to moderate and from moderate

to weak for results with weak evidence of FPRP values. Regarding findings with moderate

evidence of FPRP, the overall epidemiological credibility was consistent with the result of the

Venice criteria.

Results

Characteristics of the included studies

A total of 304 relevant articles involving 836 genetic variants in 227 different genes were

ultimately retrieved in our meta-analysis (Figure 1). Approximately half of those reports were

published after 2010. For all polymorphisms examined in the analyses, 95 variants in 70 candidate

genes or chromosomal regions had at least three data sources to warrant analysis (Supplementary

Table S1). The median number of independent studies and pooled sample sizes in each

meta-analysis were 5 (interquartile range (IQR) = 4-8) and 4280 (IQR = 2355-7451), respectively.

Main meta-analyses

For the 95 polymorphisms (except three phenotype traits: GSTM1 present/null, GSTT1

present/null, and NAT2 fast/slow), we carried out meta-analyses using allelic contrasts. Twenty

variants within 19 genes (ADH1B, BARX1, CCND1, CHEK2, CLPTM1L, CYP1A1, CYP2E1,

ECRG1, EGF, ERCC2, GSTM1, GSTT1, MIR124-1, MTHFR, PLCE1, PSCA, PTEN, SULT1A1

and VEGF) could significantly increase or decrease the risk of developing esophageal cancers.

Details of the meta-analyzed variants showing nominally significant findings are summarized in

Table 1. These meta-analyses were based on a median of 5 independent studies (IQR = 3-19) and

6664 subjects (IQR = 2339-12553). Specifically, the most significant association with risk of

esophageal cancer was found for PLCE1 rs2274223 (OR, 1.28; 95% CI, 1.21-1.35, P=3.47×10-20;

Figure 2), which was previously identified in three independent ESCC GWAS in Chinese

populations (4,5,10), and showed significant association in all genetic effect models. Across the

twenty meta-analyses that showed significant allelic summary ORs, 18 of these polymorphisms

had sufficient data to conduct analyses via dominant and recessive models. Significant findings

were no longer observed for two polymorphisms in the dominant model and eight polymorphisms

in the recessive model. Although nonsignificant results were produced in allelic contrasts, 10

additional variants yield a nominally statistically significant effect on the meta-analysis results via

either dominant or recessive models (Table 2). A total of 65 genetic variants in 51 genes showed

no significant summary ORs in the meta-analyses in any genetic-effect models (allelic, dominant,

and recessive). These meta-analyses were based on an average of 5 independent studies (IQR =

4-7) and 3696 subjects (IQR = 2396-6607). Eighteen genetic variants, described in Table 3,

showed no association with esophageal cancer risk in any model tested with a minimum of 2000

cases and 2000 controls.

Heterogeneity, sensitivity analysis and publication bias

Between-study heterogeneity tests were conducted for the 95 variants in main meta-analyses.

Mild heterogeneity was observed for 25 (26%) variants, moderate heterogeneity was observed for

21 (22%) variants, and high heterogeneity was observed for 49 (52%) variants. For the 92 (except

three phenotype traits) variants in dominant models, mild heterogeneity was observed for 28 (31%)

variants, moderate heterogeneity was observed for 15 (16%) variants, and high heterogeneity was

observed for 49 (53%) variants. For the 92 variants in recessive models, mild heterogeneity was

observed for 41 (45%) variants, moderate heterogeneity was observed for 16 (17%) variants, and

high heterogeneity was observed for 35 (38%) variants (Supplementary Table S1). These results

should be interpreted with caution since they were limited in the number of studies. Because of the

limited number of studies and the high uncertainty of heterogeneity estimates, these results should

be interpreted with caution.

Sensitivity analyses and bias assessments were performed to evaluate the stability of the

associations and the potential publication bias for all 30 variants significantly associated with

esophageal cancer risk (Tables 1 and 2). After the exclusion of the first published report, small

studies. and HWE-violating studies, six (EPHX1 rs1051740, MGMT rs12917, MIR124-1

rs531564, MMP1 rs1799750, PTGS2 rs5275, and VEGF rs3025039), seven (CCND1 rs9344,

EPHX1 rs1051740, GSTM1 present/null, GSTT1 present/null, MGMT rs12917, PSCA rs2294008,

and SULT1A1 rs9282861), and six (CYP2E1 rs2031920, EPHX1 rs1051740, MIR124-1 rs531564,

MMP1 rs1799750, MTHFR rs1801133, and SOD2 rs4880) variants were no longer significant,

respectively. Findings from the analysis of publication bias showed evidence of publication bias

for four associations (CCND1 rs9344, GSTM1 present/null, GSTT1 present/null, and SULT1A1

rs9282861), and eight variants (CCND1 rs9344, CYP2E1 rs2031920, CYP2E1 rs3813867,

ERCC2 rs1799793, GSTT1 present/null, IL10 rs1800896, MMP1 rs1799750, and SULT1A1

rs9282861) had significantly larger effects in the small studies than in larger studies. As a result,

13 variants were considered to have reliable associations with esophageal cancer after completion

of sensitivity analyses and bias assessments (allelic associations for ADH1B rs1229984, BARX1

rs11789015, CHEK2 rs738722, CLPTM1L rs401681, CYP1A1 rs1048943, ECRG1 rs12422149,

EGF rs4444903, ERCC2 rs13181, PLCE1 rs2274223, and PTEN rs701848; dominant association

for XPA rs1800975; and recessive associations for CDKN1A rs2395655 and MIR34BC

rs4938723).

Epidemiological evidence of significant associations

Tables 1 and 2 show the assessment of epidemiological credibility for all 30 meta-analyses

with nominally statistical significance. For the amount of evidence, 27 variants were graded as A

and 3 variants as B; for the replication of association, 17 variants were graded as A, 3 variants as

B, and 10 variants as C; and for protection from bias, 12 variants were graded as A and 18 variants

as C. The grades for the protection from bias are low mainly due to the following factors:

significant modified Egger’s tests, indicating larger effects in the small studies than in the larger

studies (n=8); nonsignificant findings when the small study was excluded (n=7); and/or

nonsignificant results after the exclusion of studies showing violation of HWE in controls (n=6).

Nine variants (ADH1B rs1229984, BARX1 rs11789015, CDKN1A rs2395655, CHEK2 rs738722,

CLPTM1L rs401681, EGF rs4444903, MIR rs4938723, PLCE1 rs2274223 and PTEN rs701848)

were given a grade of A for all three items and reached the category of strong cumulative

epidemiological evidence. One variant (CYP1A1 rs1048943) was graded either A or B, which

could be characterized as moderate evidence. The remaining 20 variants were rated as weak

evidence. The probability of true association with esophageal cancer risk was then evaluated by

FPRP value at the prior probability of 0.05. Analysis of FPRP for significant associations showed

that the values were lower than 0.05 for 9 variants, between 0.05 and 0.2 for 5 variants and higher

than 0.2 for 16 variants. On the basis of strong FPRP value, cumulative epidemiological evidence

of two variants in allelic contrasts was upgraded from weak to moderate for CYP2E1 rs3813867

and from moderate to strong for CYP1A1 rs1048943. Overall, strong, moderate and weak

epidemiological credibilities of significant associations with esophageal cancer risk were assigned

to 10, 1 and 19 variants, respectively, based on the Venice guidelines and FPRP tests. Note that

four variants with strong cumulative epidemiological evidence have never been examined in 10

previous meta-analyses, namely, CDKN1A rs2395655, CHEK2 rs738722, CLPTM1L rs401681,

and PTEN rs701848.

Stratified meta-analyses

Ethnicity

Stratified meta-analyses by ethnicity were performed for variants that had at least three data

sets under different genetic-effect models (allelic, dominant, and recessive models; Table 4). In the

Asian group, significant results were found for 23 variants, including nine variants (ADH1B

rs1229984, CDKN1A rs2395655, CHEK2 rs738722, CLPTM1L rs401681, CYP1A1 rs1048943,

MIR34BC rs4938723, PLCE1 rs2274223, PTEN rs701848, and PTGS2 rs689466) that showed

strong epidemiological evidence and seven variants (ALDH2 rs671, CHEK2 rs738722, CYP2E1

rs3813867, ECRG1 rs12422149, IL1B rs16944, PTEN rs701848, and PTGS2 rs689466) that

showed moderate epidemiological evidence. Note that three (ADH1B rs1229984, PTGS2

rs689466, and PLCE1 rs2274223) of these variants were significantly associated with esophageal

cancer risk in any genetic model. In the Caucasian group, significant associations were found for

nine variants, of which three variants (BARX1 rs11789015, CRTC1 rs10419226, and XPA

rs1800975) showed strong and one (ERCC2 rs1799793) showed moderate evidence. Two

(BARX1 rs11789015 and CRTC1 rs10419226) of these variants have not been previously

subjected to meta-analysis.

Histological types of esophageal cancer

In addition, stratified analyses were conducted for different subtypes of esophageal cancer

under each genetic model (Table 4). We found that 18 and 4 variants showed significant

associations with esophageal cancer risk in the ESCC and EAC groups, respectively. In the ESCC

group, eight variants (ADH1B rs1229984, CDKN1A rs2395655, CHEK2 rs738722, CYP1A1

rs1048943, LTA rs909253, MIR34BC rs4938723, PLCE1 rs2274223, and PTEN rs701848) were

considered to have strong cumulative evidence, and six variants (CHEK2 rs738722, CYP2E1

rs3813867, ECRG1 rs12422149, MTHFR rs1801133, PLCE1 rs2274223, and PTEN rs701848)

were considered to have moderate cumulative evidence. Note that two (ADH1B rs1229984 and

PLCE1 rs2274223) of these variants remained significant in all genetic models, and one

meta-variant, LTA rs909253, which had never been previously analyzed, was identified. In the

EAC subgroup, two variants (BARX1 rs11789015 and CRTC1 rs10419226) were rated as having

strong epidemiological evidence.

Functional annotation

For the 13 variants that showed significant effects with strong epidemiological evidence in

main and stratified meta-analyses, we evaluated the potential functional roles using the

Encyclopedia of DNA Elements tool HaploReg v4.1 (Table 5) (28). In the functional annotations,

three variants mapped to exons, and the remaining 10 variants mapped to non-coding regions (one

3'-UTR, two 5'-UTRs, and one intergenic and six intronic regions). Most genetic variants are

identified as expression quantitative trait loci (eQTLs) for many genes in various tissue types.

Functional annotation using data from the Encyclopedia of DNA Elements Project showed that

these variants might be located within the histone modification regions of promoters and

enhancers and sites exhibiting DNase I hypersensitivity. Data in Table 5 indicated that some

variants can cause changes in transcription factor-binding motifs and may affect transcriptional

regulatory element activity in this region. Additionally, the potential functions for three

non-synonymous variants were evaluated using the PolyPhen-2 web server (29). All variants were

qualitatively predicted to be "benign" with a naïve Bayes posterior probability of less than 0.15.

Discussion

To the best of our knowledge, this study is the most comprehensive and recently updated

assessment of the literature regarding candidate gene association studies in esophageal cancer

following guidelines proposed by the HuGENet. We investigated the associations between

putative esophageal cancer variants and affected status for 836 polymorphisms in 227 different

genes and conducted meta-analyses for a total of 95 variants across 70 different genes from 304

reports, including 104,904 cases and 159,797 controls. Nominally statistically significant findings

for the risk of esophageal cancer were identified in 30 variants. The most notable findings based

on Venice criteria and FPRP values were observed for 13 variants in main and stratified

meta-analyses. In addition, we constructed functional annotations for the 13 significant variants

with strong epidemiological credibility and found that these variants might fall in several putative

regulatory regions.

A non-synonymous SNP occurring in a coding region may result in amino acid transition in a

protein sequence, subsequently altering the phenotype of the host organism. The alcohol

metabolism-related variant (His48Arg) in the ADH2 gene causes an amino acid substitution from

arginine to histidine at codon 48 in exon 3. The encoded ADH2 subunit His allele results in

superactive metabolization of ethanol, and the fast His/His genotype of ADH2 exhibits

approximately 40 times greater maximum velocity than the less active ADH2 Arg/Arg form (30).

The SNP rs1048943 (Ile462Val) is a common functional locus in exon 7 of CYP1A1. The

substitution of isoleucine to valine in the heme-binding region of CYP1A1 results in a 2-fold

increase in microsomal enzyme activity. CYP1A1 is a phase I enzyme that is responsible for the

aryl-hydrocarbon hydroxylase activity and is involved in the metabolic activation of several

classes of tobacco procarcinogens (31). These carcinogenic substances can induce the CYP1A1

enzyme through high-affinity binding of the Ah receptor, increasing the formation of DNA adducts

(32,33). A non-synonymous SNP (His1927Arg) in exon 26 of the PCLE1 gene could result in a

histidine-to-arginine substitution in the calcium-dependent lipid-binding C2 domain of the PLCE1

protein. The PLCE1 protein catalyzes the hydrolysis of polyphosphoinositides to generate

diacylglycerol (DAG) and inositol 1,4,5-trisphosphate (IP3). These two intracellular secondary

structures are involved in protein kinase C activation and Ca2+ immobilization, respectively 13

(34,35). Studies have shown that PLCE1 is a major factor in the progression of various cancers

through its coaction with the Ras family (36-38).

Variants in non-coding regions of a gene may affect its function through changes in

transcriptional activity, mRNA stability or translation, or alterations in miRNA-binding sites. The

common SNP rs11789015 is located in the intron of the BARX1 gene. In vitro functional

experiments indicated that compared with the A allele, the G allele of the variant rs11789015

significantly reduced the promoter activity of the reporter gene, which suggested that the variant

rs11789015 might be associated with esophageal cancer risk mediated through the regulation of

BARX1 gene expression (39). CHEK2 maps to 22q12 and encodes a CHK2 checkpoint homolog.

A non-coding SNP rs738722 in the intron of CHEK2 has been identified by GWAS in the first

phase, but a statistically significant association has not been confirmed in the second phase (4).

The mechanisms of functions of this associated SNP have not been described previously, and

further validation is warranted. The intronic SNP rs401681 of CLPTM1L is located approximately

27 kb away from the TERT gene. The rs401681 C allele has been found to be associated with

shorter telomeres (40). Telomere dysfunction or shortening may cause genomic instability and can

drive early esophageal carcinogenesis (41,42). A functional SNP rs4444903 (+61A>G) in the

5'-UTR of EGF showed strong evidence of a moderate increase in the risk of esophageal cancer.

The presence of the variant rs4444903 G allele results in higher EGF levels by affecting DNA

folding or processing of the mRNA transcript and has also been associated with susceptibility to

multiple human malignancies (43-45). The PTEN variant rs701848 in the 3'-UTR is also

associated with increased esophageal cancer risk. The stability, localization, and translation of

mRNA are largely determined by sequences in the 3'-UTR (46). Thus, the functional SNP may

have effects on cancer susceptibility through altering the PTEN expression pattern and the PTEN

mRNA stability.

Although the allelic contrasts are a primary analysis tool for candidate gene association

studies, other test statistics, such as analyses performed under a dominant or recessive model, are

used as secondary analyses to explore the potential mode of inheritance of an associated SNP

(47,48). Our meta-analyses revealed that two additional variants (CDKN1A rs2395655 and

MIR34BC rs4938723) in the recessive model showed strong cumulative evidence of associations 14

with esophageal cancer risk. The lack of replication across many association studies may be due to

differences in population or disease subtype, Therefore, stratified meta-analyses were performed

within each subgroup to investigate the associations between genetic variation and disease status

(47,48). Three additional variants were identified to be associated with esophageal cancer in the

stratification analysis, including an intronic polymorphism CRTC1 rs10419226 in both the

Caucasian and EAC groups, another intronic SNP LTA rs909253 in the ESCC group, and a

intergenic variant PTGS2 rs689466 in the Asian group. The number of significant associations

that were identified among Asians was higher than that among other ethnicities, mainly because

most genetic studies were conducted in Asians. In the meta-analysis of population stratification,

the sample size of each subgroup was reduced, which may have resulted in an insufficient

statistical power to assess the effects of these variants in subgroups. Thus, further validations of

these associations in a large epidemiological study are warranted.

Despite the scientific design and strict implementation, several inevitable limitations need to

be considered in this study when these genetic associations are interpreted. First, although a

two-stage search strategy was used to identify eligible publications, we cannot rule out the

possibility that some publications were missed. Only English reports that can be retrieved through

PubMed were included, and results from genetic association studies in the form of abstracts were

excluded. This approach might have resulted in a potential publication bias, although the evidence

for such a bias was not detected in most meta-analyses with significant findings. Second, there is

considerable heterogeneity in the 95 meta-analyses in this study. Stratification analyses based on

ethnicity and histology were performed to detect the heterogeneity. However, other sources of

heterogeneity, such as sample source or genotyping platforms, likely exist and are difficult to

detect because of a lack of sufficient information. Third, the meta-analyses had insufficient power

to detect significant findings that may genuinely exist. If most small effect variants with

ORs<1.15 are linked to a genetic predisposition for esophageal cancer, the sample sizes of both

cases and controls would need to be increased significantly to detect such effect sizes with a

power of 80% (Supplementary Table S2). Fourth, there was no raw data to change genotypes into

haplotypes and to use these as the unit of haplotype-based analysis. This situation may lead to the

missing heritability of esophageal cancer. The meta-analyzed variants were not from the same

study group; therefore, these data cannot be used for multivariate analyses such as logistic

regression. Fifth, the gene-gene or gene-environment relationships and interactions were not

evaluated. Future investigations with specific designs are needed to identify these interactions. In

addition, although multiple sources of potential bias for variants associated with esophageal

cancer risk were evaluated by the Venice criteria, the unreasonable data, such as errors in

phenotype and genotype, could not be assessed, and confounding factors, such as age, gender, diet,

smoking and alcohol consumption, might be present. Thus, all genetic associations in this study

should be interpreted with caution until further studies are conducted and the molecular

characteristics have been confirmed and clarified.

Recent GWAS, most of them focused on individuals of Asian descent, have revealed several

key esophageal cancer susceptibility variants. Only a few polymorphisms show consistent findings

across studies specifically designed to re-examine associations from one population to another

(49). Further large GWAS in other high-risk populations likely contribute to the missing risk

effects of esophageal cancer. Although the potential throughput from GWAS is rapidly growing

and unit costs are dramatically decreasing, candidate gene-based association studies remain the

most prevalent hypothesis-driven approaches to detect associations between esophageal cancer

risk and potential candidate genes with prior knowledge.

In this comprehensive and systematic meta-analysis, a total of 13 polymorphisms identified

from 95 variants reached the category of strong cumulative epidemiological evidence of

associations with esophageal cancer risk. The identification of potential loci of susceptibility to

esophageal cancer provides a basis for further understanding of the genetic architecture of this

disorder. Our findings could inspire further studies to elucidate the cause of esophageal cancer and

may lead to the development of screening and prevention strategies for clinical management.

Authors’ Contributions: Conception and design: Yazhou Wu, Ben Zhang and Dong Yi;

Development of methodology: Gaoming Li and Ben Zhang; Acquisition of data: Gaoming Li,

Qiuyue Song, Yuxing Jiang, Angsong Cai, Yong Tang, Ning Tang, Dali Yi, Rui Zhang, Zeliang

Wei and Jia Chen; Analysis and interpretation of data: Gaoming Li, Ben Zhang and Dingxin Liu;

Writing, review and/or revision of the manuscript: Gaoming Li, Yazhou Wu and Ben Zhang;

Study supervision: Ben Zhang, Yanqi Zhang and Ling Liu.

Acknowledgments: This project was supported by the National Natural Science Foundation of

China (81473068, 81573254, 81872716, 81673255, and 81874283), Recruitment Program for

Young Professionals of China, Army Medical University (WX2015-013), and Army Medical

University First Affiliated Hospital (SWH2015LC03, SWH2016ZDCX1012, SWH2016JQFY-02,

and 2018XLC1004).

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Table 1. Genetic variants showing significant summary ORs for esophageal cancer risk in main meta-analyses

Number evaluated Esophageal cancer risk Heterogeneity Venice False-positive Cumulative Frequency Gene Variant Allelesa Group criteria report evidence of (%)b 2 Studies Cases Controls OR (95% CI) P value P value I (%) gradec probability associationd ADH1B rs1229984 G vs A 32.52 All ancestries 29 10487 20242 1.58 (1.40-1.78) 1.33×10-13 0.000 88 AAA <0.001 Strong BARX1 rs11789015 G vs A 24.81 All ancestries 5 6228 19895 0.84 (0.79-0.88) 2.95×10-10 0.506 0 AAA <0.001 Strong CCND1 rs9344 G vs A 47.95 All ancestries 13 3034 4321 0.84 (0.72-0.97) 0.019 0.000 76 ACC 0.250 Weak CHEK2 rs738722 T vs C 25.38 Asian 5 3153 4298 1.27 (1.17-1.37) 1.33×10-09 0.467 0 AAA <0.001 Strong CLPTM1L rs401681 T vs C 33.86 Asian 3 1716 1834 0.86 (0.77-0.96) 0.009 0.303 16 AAA 0.120 Strong CYP1A1 rs1048943 G vs A 19.41 All ancestries 16 2248 3724 1.34 (1.18-1.52) 7.71×10-6 0.098 33 ABA <0.001 Strong CYP2E1 rs3813867 C vs G 24.44 Asian 3 708 712 0.69 (0.58-0.83) 5.99×10-5 0.409 0 BAC 0.002 Moderate ECRG1 rs12422149 A vs G 20.63 Asian 3 1473 1762 1.38 (1.12-1.71) 0.003 0.082 60 ACA 0.073 Weak EGF rs4444903 G vs A 45.34 All ancestries 3 779 934 1.38 (1.20-1.59) 6.54×10-6 0.997 0 AAA <0.001 Strong ERCC2 rs13181 C vs A 25.36 All ancestries 18 4807 7886 1.14 (1.02-1.28) 0.019 0.000 62 ACC 0.336 Weak ERCC2 rs1799793 A vs G 23.58 All ancestries 12 2828 4686 1.12 (1.03-1.23) 0.011 0.990 0 AAC 0.252 Weak Null vs GSTM1 Deletion 44.30 All ancestries 37 4884 9317 1.18 (1.01-1.37) 0.031 0.000 72 ACC 0.362 Weak present Null vs GSTT1 Deletion 26.22 All ancestries 32 4336 7795 1.18 (1.00-1.40) 0.044 0.000 65 ACC 0.524 Weak present MIR124-1 rs531564 G vs C 16.16 Asian 3 1959 2159 0.86 (0.76-0.97) 0.014 0.467 0 AAC 0.211 Weak MTHFR rs1801133 T vs C 43.04 All ancestries 19 4244 5665 1.21 (1.04-1.41) 0.012 0.000 82 ACC 0.218 Weak PLCE1 rs2274223 G vs A 29.97 All ancestries 27 25954 35498 1.28 (1.21-1.35) 3.47×10-20 0.000 64 AAA <0.001 Strong PSCA rs2294008 T vs C 29.46 All ancestries 4 2357 2741 0.86 (0.79-0.95) 0.002 0.435 0 AAC 0.054 Weak PTEN rs701848 C vs T 31.11 Asian 3 955 1085 1.37 (1.21-1.57) 1.96×10-6 0.778 0 AAA <0.001 Strong SULT1A1 rs9282861 A vs G 16.87 All ancestries 4 827 978 1.53 (1.07-2.19) 0.019 0.008 75 BCC 0.455 Weak VEGF rs3025039 T vs C 12.73 All ancestries 3 955 1076 1.30 (1.07-1.58) 0.008 0.320 12 BAC 0.147 Weak Abbreviations: ESCC, esophageal squamous cell carcinoma; OR, odds ratio; A, adenine; T, thymine; G, guanine; C, cytosine.

aMinor alleles vs major alleles (reference).

bFrequency of minor allele in controls.

cStrength of epidemiological evidence based on the Venice criteria (A: strong; B: modest; C: weak).

dDegree of epidemiological credibility based on the combination of results from Venice guidelines and FPRP tests.

Table 2. Additional genetic variants showing significant summary ORs for esophageal cancer risk in meta-analyses using dominant or recessive models

Venice False-positive Cumulative Frequency Number evaluated Allelic contrasts Best genetic model Gene Variant Allelesa criteria report evidence of (%)b 2 2 Studies Cases Controls OR (95% CI) P value I (%) Model OR (95% CI) P value I (%) gradec probability associationd CYP2E1 rs2031920 T vs C 18.54 20 2908 5488 0.79 (0.61-1.03) 0.078 85 DOM 0.69 (0.49-0.97) 0.035 88 ACC 0.518 Weak PTGS2 rs5275 C vs T 33.41 3 542 1317 1.12 (0.92-1.37) 0.270 39 DOM 1.27 (1.02-1.58) 0.032 0 AAC 0.394 Weak SOD2 rs4880 C vs T 42.10 4 517 1114 1.37 (0.94-1.98) 0.097 80 DOM 1.55 (1.06-2.26) 0.024 46 ABC 0.500 Weak XPA rs1800975 A vs G 46.95 5 1328 2952 0.72 (0.51-1.03) 0.069 91 DOM 0.57 (0.36-0.91) 0.018 87 ACA 0.579 Weak CDKN1A rs2395655 A vs G 46.18 3 1444 1610 0.92 (0.76-1.11) 0.391 72 REC 0.75 (0.61-0.92) 0.006 23 AAA 0.112 Strong EPHX1 rs1051740 C vs T 36.27 10 1640 2931 1.25 (0.95-1.66) 0.108 87 REC 1.46 (1.02-2.09) 0.040 76 ACC 0.568 Weak IL10 rs1800896 G vs A 47.58 6 904 2110 0.86 (0.73-1.02) 0.081 33 REC 0.78 (0.62-0.97) 0.024 0 AAC 0.345 Weak MGMT rs12917 T vs C 16.24 6 2338 2436 1.08 (0.92-1.27) 0.354 36 REC 1.60 (1.01-2.53) 0.046 15 AAC 0.683 Weak MIR34BC rs4938723 C vs T 34.14 4 2121 2408 0.92 (0.84-1.00) 0.051 0 REC 0.72 (0.59-0.87) 7.55×10-4 0 AAA 0.016 Strong 1G vs MMP1 rs1799750 45.07 4 988 1216 0.85 (0.68-1.05) 0.135 64 REC 0.73 (0.55-0.96) 0.023 31 ABC 0.384 Weak 2G Abbreviations: OR, odds ratio; CI, confidence interval; A, adenine; T, thymine; G, guanine; C, cytosine; REC, recessive; DOM, dominant.

aMinor alleles vs major alleles (reference).

bFrequency of minor allele in controls.

cStrength of epidemiological evidence based on the Venice criteria (A: strong; B: modest; C: weak).

dDegree of epidemiological credibility based on the combination of results from Venice guidelines and FPRP tests.

Table 3. Genetic variants showing no significant summary ORs for esophageal cancer risk in a meta-analysis with at least 2000 cases and 2000 controls

a Frequency Number evaluated Esophageal cancer risk Heterogeneity Gene Variant Comparison b (%) Studies Cases Controls OR (95% CI) P value P for Q I2 (%) ADH1C rs698 G vs A 26.18 10 2022 4076 1.20 (0.95-1.52) 0.118 0.000 79 FOXF1 rs2687201 A vs C 33.60 5 6214 19894 1.11 (0.99-1.25) 0.065 0.000 85 FOXF1 rs9936833 G vs A 31.82 5 5018 13050 1.08 (1.00-1.16) 0.049c 0.184 36 GSTP1 rs1695 G vs A 31.76 28 3319 6835 1.05 (0.96-1.15) 0.264 0.031 36 MIR146A rs2910164 G vs C 47.68 6 3120 4036 1.06 (0.95-1.19) 0.268 0.073 50 MIR196A-2 rs11614913 C vs T 49.53 7 3799 4714 1.01 (0.87-1.18) 0.887 0.000 81 MIR26A1 rs7372209 T vs C 21.30 4 2044 2653 0.81 (0.60-1.10) 0.173 0.004 78 MIR423 rs6505162 A vs C 34.91 7 3269 4717 0.94 (0.82-1.06) 0.295 0.026 58 MIR499 rs3746444 C vs T 15.05 3 2047 2870 0.91 (0.71-1.17) 0.483 0.030 71 MUC1 rs4072037 G vs A 20.99 5 4654 5326 0.97 (0.85-1.12) 0.698 0.055 57 NAA25 rs4767364 G vs A 23.92 5 2607 3254 1.00 (0.88-1.13) 0.966 0.814 0 OGG1 rs1052133 G vs C 26.86 11 2406 4013 1.05 (0.94-1.17) 0.361 0.124 34 PLCE1 rs3765524 T vs C 20.57 5 3093 4361 1.24 (1.05-1.46) 0.009c 0.029 63 PTGS2 rs20417 C vs G 17.44 7 2099 3337 1.26 (0.95-1.67) 0.111 0.000 82 S100A14 rs11548103 A vs G 38.58 3 2237 2431 1.04 (0.89-1.22) 0.598 0.047 67 SLC52A3 rs13042395 T vs C 21.17 10 3595 6680 0.94 (0.87-1.03) 0.178 0.590 0 XRCC1 rs1799782 T vs C 28.48 9 2168 3376 1.03 (0.95-1.13) 0.459 0.627 0 XRCC1 rs25487 A vs G 31.86 19 4056 7697 1.04 (0.96-1.13) 0.356 0.019 45 NOTE: Only the allelic summary ORs are presented here.

Abbreviations: OR, odds ratio; CI, confidence interval; A, adenine; T, thymine; G, guanine; C, cytosine.

aMinor alleles vs major alleles (reference).

bFrequency of minor allele in controls.

cVariants fail to reach genome-wide statistical significance.

Table 4. Genetic variants showing significant summary ORs for esophageal cancer risk in stratified meta-analyses with strong or moderate cumulative evidence

Number evaluated Esophageal cancer risk Heterogeneity Venice False-positive Cumulative Gene Subgroupa Variant Allelesb criteria report evidence of Studies Cases Controls OR (95% CI) P value P value I2 (%) gradec probability associationd Associations identified from allelic model ADH1B Asian rs1229984 G vs A 24 9203 17589 1.69 (1.52-1.89) 1.15×10-20 0.000 85 AAA <0.001 Strong CHEK2 Asian/ESCC rs738722 T vs C 5 3153 4298 1.27 (1.17-1.37) 1.33×10-09 0.467 0 AAA <0.001 Strong CLPTM1L Asian rs401681 T vs C 3 1716 1834 0.86 (0.77-0.96) 0.009 0.303 16 AAA 0.120 Strong CYP1A1 Asian rs1048943 G vs A 11 1947 2765 1.36 (1.20-1.54) 1.10×10-6 0.161 30 ABA <0.001 Strong CYP2E1 Asian/ESCC rs3813867 C vs G 3 708 712 0.69 (0.58-0.83) 5.59×10-5 0.409 0 BAC 0.002 Moderate PLCE1 Asian rs2274223 G vs A 21 24779 32211 1.33 (1.27-1.40) 4.68×10-30 0.001 55 AAA <0.001 Strong PTEN Asian/ESCC rs701848 C vs T 3 955 1085 1.37 (1.21-1.57) 1.96×10-6 0.778 0 AAA <0.001 Strong PTGS2 Asian rs689466 G vs A 3 1380 1680 0.77 (0.69-0.86) 1.26×10-6 0.842 0 AAA <0.001 Strong BARX1 Caucasian rs11789015 G vs A 3 3365 16326 0.83 (0.78-0.89) 7.44×10-9 0.775 0 AAA <0.001 Strong CRTC1 Caucasian rs10419226 T vs G 3 3365 16326 1.17 (1.11-1.24) 4.98×10-9 0.861 0 AAA <0.001 Strong ADH1B ESCC rs1229984 G vs A 26 9918 19517 1.60 (1.41-1.81) 4.98×10-13 0.000 89 AAA <0.001 Strong CYP1A1 ESCC rs1048943 G vs A 12 1988 3132 1.28 (1.12-1.46) 3.27×10-4 0.125 33 ABA 0.004 Strong LTA ESCC rs909253 G vs A 3 4029 5133 0.83 (0.78-0.88) 4.33×10-9 0.519 0 AAA <0.001 Strong MTHFR ESCC rs1801133 T vs C 13 3309 4028 1.30 (1.12-1.51) 4.37×10-4 0.000 76 ACA 0.012 Moderate PLCE1 ESCC rs2274223 G vs A 22 24987 34022 1.30 (1.23-1.38) 5.03×10-21 0.000 66 AAA <0.001 Strong BARX1 EAC rs11789015 G vs A 4 4109 17464 0.84 (0.79-0.89) 2.17×10-8 0.458 0 AAA <0.001 Strong CRTC1 EAC rs10419226 T vs G 4 4109 16979 1.17 (1.11-1.23) 3.22×10-9 0.844 0 AAA <0.001 Strong ERCC2 EAC rs1799793 A vs G 4 932 1694 1.16 (1.03-1.31) 0.015 0.778 0 AAA 0.241 Moderate Associations identified from dominant model ADH1B Asian rs1229984 G vs A 24 9203 17589 1.55 (1.44-1.68) 1.27×10-28 0.033 38 AAA <0.001 Strong ALDH2 Asian rs671 A vs G 36 14606 24069 2.00 (1.57-2.56) 3.13×10-8 0.000 96 AAC <0.001 Moderate

CHEK2 Asian/ESCC rs738722 T vs C 3 1038 996 1.23 (1.03-1.47) 0.020 0.672 0 AAA 0.306 Moderate CYP1A1 Asian rs1048943 G vs A 11 1947 2765 1.42 (1.24-1.62) 5.77×10-7 0.327 12 AAA <0.001 Strong IL1B Asian rs16944 T vs C 3 602 700 0.73 (0.56-0.95) 0.019 0.598 0 AAA 0.327 Moderate PLCE1 Asian rs2274223 G vs A 10 3868 5526 1.34 (1.18-1.53) 6.27×10-6 0.038 49 AAA <0.001 Strong PTEN Asian/ESCC rs701848 C vs T 3 955 1085 1.61 (1.24-2.10) 3.63×10-4 0.129 51 ACA 0.027 Moderate PTGS2 Asian rs689466 G vs A 3 1380 1680 0.69 (0.59-0.82) 3.02×10-5 0.368 0 AAA 0.001 Strong ERCC2 Caucasian rs1799793 A vs G 7 1242 2847 1.19 (1.03-1.37) 0.017 0.949 0 AAA 0.228 Moderate ADH1B ESCC rs1229984 G vs A 25 9918 19517 1.46 (1.31-1.63) 7.02×10-12 0.000 70 AAA <0.001 Strong CYP1A1 ESCC rs1048943 G vs A 12 1988 3132 1.35 (1.15-1.59) 1.95×10-4 0.138 32 ABA 0.007 Strong PLCE1 ESCC rs2274223 G vs A 11 4076 7337 1.27 (1.10-1.47) 0.001 0.002 64 AAA 0.025 Strong ERCC2 EAC rs1799793 A vs G 4 932 1694 1.24 (1.05-1.47) 0.012 0.871 0 AAA 0.203 Moderate Associations identified from recessive model ADH1B Asian rs1229984 G vs A 24 9203 17589 2.88 (2.23-3.73) 5.79×10-16 0.000 90 AAA <0.001 Strong CDKN1A Asian/ESCC rs2395655 A vs G 3 1444 1610 0.75 (0.61-0.92) 0.006 0.273 23 AAA 0.112 Strong ECRG1 Asian/ESCC rs12422149 A vs G 3 1473 1762 1.60 (1.13-2.26) 0.008 0.294 18 AAA 0.289 Moderate MIR34BC Asian/ESCC rs4938723 C vs T 4 2121 2408 0.72 (0.59-0.87) 7.55×10-4 0.741 0 AAA 0.016 Strong PLCE1 Asian rs2274223 G vs A 10 3868 5526 1.49 (1.19-1.86) 4.68×10-4 0.195 27 AAA 0.015 Strong PTGS2 Asian rs689466 G vs A 3 1380 1680 0.71 (0.59-0.85) 1.41×10-4 0.818 0 AAC 0.005 Moderate XPA Caucasian rs1800975 A vs G 3 551 1890 0.50 (0.36-0.69) 2.70×10-5 0.468 0 AAA 0.012 Strong ADH1B ESCC rs1229984 G vs A 26 9918 19517 2.54 (1.96-3.31) 3.03×10-12 0.000 90 AAA <0.001 Strong PLCE1 ESCC rs2274223 G vs A 11 4076 7337 1.31 (1.07-1.62) 0.011 0.071 42 AAA 0.213 Moderate Abbreviations: OR, odds ratio; CI, confidence interval; EAC, esophageal adenocarcinoma; ESCC, esophageal squamous cell carcinoma; A, adenine; T, thymine; G, guanine; C, cytosine. aStratified by ethnicity or subtype. bMinor alleles vs major alleles (reference). cStrength of epidemiological evidence based on the Venice criteria (A: strong; B: modest; C: weak).

dDegree of epidemiological credibility based on the combination of results from Venice guidelines and FPRP tests.

Table 5. Summary of functional annotations for 13 esophageal cancer risk variants with strong epidemiological credibility

Variant Gene Positiona Annotation Promoter histone marksb Enhancer histone marksc DNAsed Proteins bounde Motifs changedf rs1229984 ADH1B 100239319 missense GI, HRT OVRY rs11789015 BARX1 96716028 intronic GI 15 tissues 6 tissues CTBP2 7 altered motifs rs2395655 CDKN1A 36645696 5'-UTR 14 tissues 16 tissues 21 tissues 12 bound proteins 4 altered motifs rs738722 CHEK2 29130012 intronic ESDR, ESC, BRN 5 tissues ATF3,E2F,Pou2f2 rs401681 CLPTM1L 1322087 intronic LIV, THYM, BLD Egr-1,HNF4 rs10419226 CRTC1 18803172 intronic 6 altered motifs rs1048943 CYP1A1 75012985 missense ESDR rs4444903 EGF 110834110 5'-UTR 15 tissues 12 tissues 9 tissues CJUN,MAFK,POL2 rs909253 LTA 31540313 intronic BLD, GI, THYM BLD, SKIN 6 tissues 5 bound proteins EWSR1-FLI1,GR rs4938723 MIR34BC 111382565 intronic ESC, BRST 6 tissues IPSC,BRST Mrg1::Hoxa9,RORalpha1,Rhox11 rs2274223 PLCE1 96066341 missense 5 altered motifs rs701848 PTEN 89726745 3'-UTR FAT, MUS Irf rs689466 PTGS2 186650751 17 tissues IPSC, BLD, KID 4 tissues 8 altered motifs aThe chromosome position is based on NCBI Build 37.

bHistone modification of H3K4me1 and H3K27ac (tissue types: if >3, only the number is included).

cHistone modification of H3K4me3 (tissue types: if >3, only the number is included).

dLevels of DNase I hypersensitivity (tissue types: if >3, only the number is included).

eAlteration in transcription factor binding (disruptions: if >3, only the number is included).

fAlteration in regulatory motif (disruptions: if >3, only the number is included).

Figure legends

Figure 1. Flow chart of the selection of studies for meta-analyses.

Figure 2. Forest plot for the association between rs2274223 and esophageal cancer risk (allelic

contrast: G vs A). The study-specific ORs are represented as squares. The size of the square indicates

the weight of each study. The horizontal lines represent 95 CIs. Diamonds show the overall estimate

or pooled ORs in subgroups with their corresponding 95 CIs.

Cumulative Evidence for Associations Between Genetic Variants and Risk of Esophageal Cancer

Gaoming Li, Qiuyue Song, Yuxing Jiang, et al.

Cancer Epidemiol Biomarkers Prev Published OnlineFirst January 22, 2020.

Updated version Access the most recent version of this article at: doi:10.1158/1055-9965.EPI-19-1281

Supplementary Access the most recent supplemental material at: Material http://cebp.aacrjournals.org/content/suppl/2020/01/22/1055-9965.EPI-19-1281.DC1

Author Author manuscripts have been peer reviewed and accepted for publication but have not yet Manuscript been edited.

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