Abstract
Background
Growing evidence suggested that B‐ and T‐lymphocyte attenuator (BTLA) polymorphisms raised the susceptibility to a wide range of cancers. This study aimed to evaluate whether BTLA variants were related to the risk of esophageal squamous cell carcinoma (ESCC).
Methods
A total of 721 ESCC patients and 1208 matched non‐cancer controls were included in this research, and four tagging BTLA polymorphisms (rs2171513 G > A, rs3112270 A > G, rs1982809 G > A, and rs16859629 T > C) were selected and genotyped using SNPscan™ Assays.
Results
In the present study, no significant relationship between BTLA polymorphisms and ESCC was observed. However, stratified analyses suggested that the variant of BTLA rs3112270 A > G reduced the risk of ESCC in the male subgroup (AG vs AA: adjusted OR = 0.78, 95% CI = 0.61‐0.99, P = .042), BMI < 24 kg/m2 subgroup (AG vs AA: adjusted OR = 0.72, 95% CI = 0.55‐0.93, P = .012; AG/GG vs AA: adjusted OR = 0.77, 95% CI = 0.60‐0.98, P = .032), and ever drinking subgroup (AG vs AA: adjusted OR = 0.61, 95% CI = 0.38‐0.97, P = .037). But when stratified by BMI ≥ 24 kg/m2, the rs3112270 A > G polymorphism increased the susceptibility to ESCC (GG vs AA: adjusted OR = 1.91, 95% CI = 1.02‐3.59, P = .045). Besides, we demonstrated that BTLA rs2171513 G > A polymorphism was protective of ESCC in the ever drinking subgroup (GA/AA vs GG: adjusted OR = 0.62, 95% CI = 0.39‐0.97, P = .037).
Conclusion
Taken together, our initial investigation postulated that the rs3112270 A > G and rs2171513 G > A variants in the BTLA gene are candidates for the risk of ESCC, which might be helpful for the early diagnosis and treatment of ESCC.
Keywords: BTLA, ESCC, polymorphisms, susceptibility
1. INTRODUCTION
As stated by the global epidemiological data, esophageal cancer (EC) ranks the sixth primary cause of cancer‐related death, with an approximated 477 900 new occurrences and 375 000 deaths per year in China.1, 2 Different from the fact that esophagogastric junction adenocarcinoma (EGJA) is the dominant subtype of EC for the western nations, in China, esophageal squamous cell carcinoma (ESCC) makes up more than 90% of the total cases.3 And despite rapid progress in surgical technique and adjuvant treatment, the prognosis for patients with ESCC is extremely poor, with a 5‐year overall survival rate <30%.4 Thus, it is essential to explore new risk factors for further understanding the potential mechanism of ESCC progression.
Nowadays, the immune system plays an increasingly important role in anti‐tumor therapy.5 Cytotoxic T lymphocyte–associated antigen 4 (CTLA‐4) and programmed cell death 1 (PD‐1) are the prominent representative of this field. Similar to CTLA‐4 and PD‐1, as a co‐inhibitory regulator of the immune system, BTLA contains an extracellular domain, a transmembrane region, and a cytoplasmic region.6 When combined with its ligand named herpesvirus entry mediator (HVEM),7 tyrosine phosphorylation of the cytoplasmic region in BTLA gene can suppress T‐cell activation by recruiting Src homology phosphatase‐1 and Src homology phosphatase‐2,8 which could significantly inhibit the secretion of IL‐1, IFN‐γ, and IL‐10. 9 And the role of BTLA‐HVEM pathway has also been identified in BTLA‐deficient mouse models,10 where the absence of BTLA gene could enhance sensitivity to antigen‐specific immune response and therefore develop autoimmune diseases, as well as HVEM‐deficient mice,11 which, on another level, showed the negative effect of BTLA‐HVEM pathway on the immune microenvironment.
In recent years, accumulating studies have focused on the genetic polymorphisms of immune molecules with susceptibility to the various tumors, including BTLA.12, 13, 14, 15 Fu et al12 genotyped five SNPs and found that BTLA rs1844089, rs2705535, and rs2633562 polymorphisms were associated with the pathological features of breast cancer. Partyka et al13 chose seven variants and revealed the rs1982809G allele contributed to a higher‐grade stage of renal cell carcinoma. Recently, there was a basic study demonstrating that the change from T to C in the BTLA rs1982809 variant could interfere with the activity of BTLA 3'UTR and regulate BTLA expression in peripheral blood T lymphocytes, which might be considered as a potential biomarker in predicting the process of sepsis and multiple organ dysfunction syndrome.16 In addition, Karabon et al14 enrolled a total of ten polymorphisms and demonstrated that the presence of BTLA rs1982809 polymorphism was related to a lower level of BTLA mRNA, and the variant might be deemed as a low‐risk factor for the development of chronic lymphocytic leukemia. Subsequently, Tang et al15 reported that the BTLA rs1982809 SNP was found to be conferred to an increased risk of EGJA in smoking patients.
Nevertheless, whether the variation in the BTLA gene associates with ESCC risk remains unknown. Concerning the tremendous value of co‐signaling molecules in anti‐tumor therapy, and to better understand this issue, we conducted this case‐control study to clarify the detailed relationship of four tagging BTLA polymorphisms with the risk of ESCC in the eastern Chinese Han population.
2. MATERIALS AND METHODS
2.1. Ethics statement
All procedures of this research were administered in line with the Declaration of Helsinki and approved by the Institutional Review Board of Jiangsu University (NO. K‐20160036‐Y). Each participant provided the written informed consent to this study and was willing to donate 2 mL of peripheral blood.
2.2. Participants
From February 2014 to April 2018, patients with pathologically confirmed ESCC were continuously recruited from Fujian Medical University Union Hospital and the Affiliated People's Hospital of Jiangsu University. The major exclusion criteria for ESCC subjects were as follows: (a) suffering from autoimmune diseases, (b) prior exposure to anti‐cancer treatment, (c) history of any other malignancy, and (d) patients with incomplete clinical records. Ultimately, 721 ESCC cases were enrolled in this study. During the parallel period, 1208 healthy controls were also recruited from the department of physical examination in the same hospitals and matched with the ESCC patients in terms of age and sex. And the control individuals should meet the major inclusion criteria: (a) non‐cancer samples, (b) without any infectious/immunological disorders, and (c) ethnicity of the eastern Chinese Han population. The detailed data on personal characteristics and environmental factors, including smoking status and alcohol consumption, were obtained by questionnaires and patients' clinical records. We defined the “ever drinkers” as the subjects with drinking no <3 times a week for longer than half a year, and the individuals who smoked at least one cigarette per day over 1 year were deemed as “ever smokers.” Besides, we used the body mass index (BMI) value of 24 kg/m2 as a threshold for distinguishing individuals at obesity.17
2.3. SNP selection
BTLA tagging SNPs were ascertained based on the Genome Variation Server data (http://gvs.gs.washington.edu/GVS147/), with the extent covering all the gene regions together with the upstream and downstream extending 5 Kb, respectively. And the following criteria were applied: minor allele frequency (MAF) ≥0.05 and minimum linkage disequilibrium (LD) of r 2 < .8. Overall, four candidate BTLA SNPs, including rs2171513 G > A, rs3112270 A > G, rs1982809 G > A, and rs16859629 T > C, were enrolled in this research to evaluate the effect of BTLA polymorphisms on the susceptibility to ESCC.
2.4. DNA genotyping
The whole blood sample was stored in an anti‐coagulated tube that contained EDTA. We extracted the genomic DNA by using a Promega DNA Mini Kit (Promega) under the instruction of the manufacturer's procedure,18 and then, the four SNPs were genotyped using the SNPscan™ Assays (Genesky Biotechnologies Inc).19 For qualitative tests, 4% of the total DNA samples were selected at random and genotyped again by different laboratory staff, and the final results of the four BTLA genotypes were in concord with the primary findings.
2.5. Statistical analysis
In this study, all data analyses were conducted with software SAS version 9.4 (SAS Institute). The value of continuous variable, including age, was reported as means ± standard deviation (SD) and evaluated by Student's t test. The comparison of categorical variables between ESCC cases and controls, such as BTLA genotypes, was conducted with the chi‐square test. The deviation of HWE for each SNP distribution in the controls was assessed via the online software (http://ihg.gsf.de/cgi-bin/hw/hwa1.pl). After adjusting for age, gender, smoking status, and alcohol consumption, the potential associations between BTLA variants and the risk of ESCC were examined by the multivariate logistic regression analyses and described by calculating the adjusted odds ratio (OR) with 95% confidence intervals (CIs). A two‐sided P value < .05 was deemed as statistically significant.
3. RESULTS
3.1. Basic characteristics
Basic information regarding BTLA polymorphisms is revealed in Table 1. Results showed that the MAF of each BTLA SNP was in accord with the database of the Chinese population. In the control group, frequencies of the four BTLA genotypes were all reached HWE (all P > .05), and the failed genotype data for each polymorphism were <1%. Table 2 summarizes the basic features of 721 ESCC cases and 1208 controls, and the mean age of the case and control groups was 62.59 ± 8.18 and 62.92 ± 8.94 years, respectively. The ESCC group composed of 551 males (76.42%) and 170 females (23.58%), and there involved 899 males (74.42%) and 309 females (25.58%) among the controls. There was no difference in age and sex between the study groups (both P > .05), meaning that the two above factors were well matched. However, compared with the controls, the degree of BMI and the proportion of drinking and smoking were significantly higher in those of ESCC group (all P < .05).
Table 1.
Primary information for BTLA tagging SNPs
| Genotyped polymorphisms | rs2171513 G > A | rs3112270 A > G | rs1982809 G > A | rs16859629 T > C |
|---|---|---|---|---|
| Chr | 3 | 3 | 3 | 3 |
| Position_38 | 112466080 | 112461797 | 112463893 | 112471533 |
| Region | 3'UTR | Promoter | 3'UTR | intron_variant |
| MAF in database (1000 genomes‐ Chinese Han populations) | 0.188 | 0.269 | 0.216 | 0.067 |
| MAF in our controls (n = 1208) | 0.197 | 0.281 | 0.260 | 0.081 |
| P value for HWE test in our controls | .551 | .026 | .108 | .958 |
| % Genotyping value | 99.27% | 99.12% | 99.22% | 99.29% |
Abbreviations: HWE, Hardy‐Weinberg equilibrium; MAF, minor allele frequency.
Table 2.
Distribution of selected demographic variables and risk factors in ESCC cases and controls
| Variable | Cases (n = 721) | Controls (n = 1208) | P |
|---|---|---|---|
| n (%) | n (%) | ||
| Age (years) | 62.59 ± 8.18 | 62.92 ± 8.94 | .413 |
| Age (years) | |||
| <63 | 337 (46.74) | 579 (47.93) | .613 |
| ≥63 | 384 (53.26) | 629 (52.07) | |
| Sex | |||
| Male | 551 (76.42) | 899 (74.42) | .325 |
| Female | 170 (23.58) | 309 (25.58) | |
| Tobacco use | |||
| Never | 342 (47.43) | 881 (72.93) | <.001 |
| Ever | 379 (52.57) | 327 (27.07) | |
| Alcohol use | |||
| Never | 502 (69.63) | 1046 (86.59) | <.001 |
| Ever | 219 (30.37) | 162 (13.41) | |
| BMI (kg/m2) | |||
| <24 | 527 (73.09) | 651 (53.89) | <.001 |
| ≥24 | 194 (26.01) | 557 (46.11) | |
Bold values are statistically significant (P < .05).
Abbreviation: BMI, body mass index.
3.2. BTLA polymorphisms and ESCC risk in the overall population
The detailed frequencies of BTLA genotypes and the results about the association between each selected polymorphism with the risk of ESCC are presented in Table 3. We found that BTLA rs2171513 G > A, rs3112270 A > G, and rs1982809 G > A SNPs were not correlated with the susceptibility to the entire cohorts (all P > .05). Nevertheless, we showed that the BTLA rs16859629 T > C variant significantly decreased the risk of ESCC (TC vs TT: adjusted OR = 0.75, 95% CI = 0.57‐0.99, P = .044; TC/CC vs TT: adjusted OR = 0.75, 95% CI = 0.57‐0.98, P = .035). But the significant statistical distribution of BTLA rs16859629 T > C SNP disappeared after adjusting for the confounding factors, including age, sex, smoking, and alcohol status (P > .05).
Table 3.
Genotype frequencies of BTLA tagging SNPs and ESCC risk
| Genotype | ESCC cases (n = 721) | Controls (n = 1208) | Crude OR (95% CI) | P | Adjusted OR (95% CI)a | P | ||
|---|---|---|---|---|---|---|---|---|
| n | % | N | % | |||||
| rs2171513 G > A | ||||||||
| GG | 463 | 64.85 | 774 | 64.45 | 1.00 | 1.00 | ||
| GA | 227 | 31.79 | 380 | 31.64 | 1.00 (0.82‐1.22) | .989 | 0.99 (0.80‐1.22) | .888 |
| AA | 24 | 3.36 | 47 | 3.91 | 0.85 (0.52‐1.42) | .539 | 0.80 (0.47‐1.37) | .424 |
| GA + AA | 251 | 35.15 | 427 | 35.55 | 0.98 (0.81‐1.19) | .860 | 0.96 (0.79‐1.19) | .730 |
| GG + GA | 690 | 96.64 | 1154 | 96.09 | 0.85 (0.52‐1.41) | .537 | 0.81 (0.47‐1.37) | .430 |
| A allele | 275 | 19.26 | 474 | 19.73 | ||||
| rs3112270 A > G | ||||||||
| AA | 387 | 54.43 | 614 | 51.12 | 1.00 | 1.00 | ||
| AG | 259 | 36.43 | 500 | 41.64 | 0.82 (0.68‐1.00) | .051 | 0.84 (0.68‐1.04) | .109 |
| GG | 65 | 9.14 | 87 | 7.24 | 1.19 (0.84‐1.68) | .335 | 1.27 (0.88‐1.83) | .204 |
| AG + GG | 324 | 45.57 | 587 | 48.88 | 0.88 (0.73‐1.06) | .162 | 0.91 (0.74‐1.10) | .324 |
| AA + AG | 646 | 90.86 | 1114 | 92.76 | 1.29 (0.92‐1.80) | .139 | 1.36 (0.96‐1.95) | .087 |
| G allele | 389 | 27.36 | 674 | 28.06 | ||||
| rs1982809 G > A | ||||||||
| GG | 408 | 57.22 | 657 | 54.70 | 1.00 | 1.00 | ||
| GA | 252 | 35.35 | 464 | 38.64 | 0.88 (0.72‐1.07) | .182 | 0.93 (0.75‐1.15) | .488 |
| AA | 53 | 7.43 | 80 | 6.66 | 1.07 (0.74‐1.54) | .731 | 1.30 (0.88‐1.91) | .189 |
| GA + AA | 305 | 42.78 | 544 | 45.30 | 0.90 (0.75‐1.09) | .284 | 0.98 (0.80‐1.20) | .841 |
| GG + GA | 660 | 92.57 | 1121 | 93.34 | 1.13 (0.79‐1.61) | .521 | 1.34 (0.92‐1.95) | .134 |
| A allele | 358 | 25.11 | 624 | 25.98 | ||||
| rs16859629 T > C | ||||||||
| TT | 622 | 87.61 | 997 | 84.06 | 1.00 | 1.00 | ||
| TC | 85 | 11.97 | 181 | 15.27 | 0.75 (0.57‐0.99) | .044 | 0.82 (0.61‐1.10) | .191 |
| CC | 3 | 0.42 | 8 | 0.67 | 0.60 (0.16‐2.28) | .454 | 0.82 (0.21‐3.23) | .777 |
| CT + CC | 88 | 12.39 | 189 | 15.94 | 0.75 (0.57‐0.98) | .035 | 0.82 (0.62‐1.10) | .184 |
| TT + CT | 707 | 99.58 | 1178 | 99.33 | 0.63 (0.17‐2.36) | .489 | 0.85 (0.22‐3.33) | .809 |
| C allele | 91 | 6.31 | 197 | 8.15 | ||||
Bold values are statistically significant (P < .05).
Abbreviations: CI, confidence interval; OR, odds ratio.
Adjusted for age, sex, smoking status, and alcohol use in a logistic regression model.
3.3. BTLA polymorphisms and ESCC risk in stratification groups
Furthermore, we conducted a stratified analysis mainly relied on the enrolled parameters, including age, sex, BMI, smoking status, and alcohol consumption. As presented in Table 4, when stratified by alcoholic use, in the ever drinking subgroup, we found that the rs2171513 G > A variant in BTLA gene might be a protective variable against the progression of ESCC (GA/AA vs GG: adjusted OR = 0.62, 95% CI = 0.39‐0.97, P = .037).
Table 4.
Stratified analyses between BTLA rs2171513 G > A polymorphism and ESCC risk by sex, age, smoking status, and alcohol consumption
| Variable | BTLA rs2171513 G > A (case/control)a | Adjusted OR (95% CI)b; P | ||||||
|---|---|---|---|---|---|---|---|---|
| GG | GA | AA | GG | GA vs GG | AA vs GG | GA/AA vs GG | AA vs (GG/GA) | |
| Sex | ||||||||
| Male | 352/579 | 175/278 | 18/38 | 1.00 |
1.01 (0.78‐1.29); P: .966 |
0.73 (0.40‐1.36); P: .322 |
0.97 (0.77‐1.24); P: .816 |
0.73 (0.40‐1.35); P: .315 |
| Female | 111/195 | 52/102 | 6/9 | 1.00 |
0.91 (0.60‐1.38); P: .653 |
1.04 (0.34‐3.17); P: .945 |
0.92 (0.61‐1.38); P: .685 |
1.07 (0.36‐3.24); P: .899 |
| Age | ||||||||
| <63 | 219/578 | 103/177 | 11/22 | 1.00 |
0.97 (0.70‐1.34); P: .860 |
0.76 (0.34‐1.71); P: .510 |
0.95 (0.69‐1.29); P: .731 |
0.77 (0.35‐1.72); P: .522 |
| ≥63 | 244/396 | 124/203 | 13/25 | 1.00 |
1.00 (0.75‐1.33); P: .985 |
0.80 (0.39‐1.64); P: .535 |
0.98 (0.74‐1.28); P: .855 |
0.80 (0.39‐1.63); P: .533 |
| Smoking status | ||||||||
| Never | 229/562 | 98/281 | 12/31 | 1.00 |
0.85 (0.64‐1.13); P: .264 |
1. (0.49‐2.02); P: .996 |
0.87 (0.66‐1.14); P: .297 |
1.05 (0.52‐2.11); P: .889 |
| Ever | 234/212 | 129/99 | 12/16 | 1.00 |
1.21 (0.87‐1.68); P: .270 |
0.62 (0.28‐1.36); P: .233 |
1.12 (0.81‐1.54); P: .489 |
0.58 (0.27‐1.27); P: .173 |
| Alcohol consumption | ||||||||
| Never | 315/678 | 163/323 | 20/39 | 1.00 |
1.08 (0.85‐1.38); P: .513 |
0.96 (0.54‐1.73); P: .895 |
1.07 (0.85‐1.35); P: .569 |
0.94 (0.53‐1.67); P: .824 |
| Ever | 148/96 | 64/57 | 4/8 | 1.00 |
0.66 (0.41‐1.05); P: .082 |
0.31 (0.08‐1.13); P: .077 |
0.62 (0.39‐0.97); P: .037 |
0.35 (0.10‐1.29); P: .115 |
| BMI (kg/m2) | ||||||||
| <24 | 336/420 | 167/196 | 17/29 | 1.00 |
1.03 (0.79‐1.35); P: .811 |
0.66 (0.35‐1.25); P: .203 |
0.98 (0.76‐1.27); P: .891 |
0.65 (0.35‐1.23); P: .188 |
| ≥24 | 127/354 | 60/184 | 7/18 | 1.00 |
0.89 (0.62‐1.28); P: .521 |
1.35 (0.54‐3.40); P: .522 |
0.92 (0.65‐1.31); P: .656 |
1.40 (0.56‐3.51); P: .467 |
Bold values are statistically significant (P < .05).
Abbreviations: BMI, body mass index; CI, confidence interval; OR, odds ratio.
The genotyping was successful in 714 (99.03%) ESCC cases and 1201 (99.42%) controls for BTLA rs2171513 G > A.
Adjusted for age, sex, smoking status, and alcohol consumption (besides stratified factors accordingly) in a logistic regression model.
As exhibited in Table 5, there was a close correlation between BTLA rs3112270 A > G and the risk of ESCC in some certain subgroups. In the male population, results demonstrated that the genotype of AG in BTLA rs3112270 lowered the ESCC risk when using AA genotype as a reference (AG vs AA: adjusted OR = 0.78, 95% CI = 0.61‐0.99, P = .042). And in ever drinking subgroup, we found a similar unfavorable effect of AG genotype on the risk of ESCC (AG vs AA: adjusted OR = 0.61, 95% CI = 0.38‐0.97, P = .037). When stratified by BMI, analyses showed that the rs3112270 A > G variant decreased the genetic susceptibility to ESCC in the BMI < 24 kg/m2 subgroup (AG vs AA: adjusted OR = 0.72, 95% CI = 0.55‐0.93, P = .012; AG/GG vs AA: adjusted OR = 0.77, 95% CI = 0.60‐0.98, P = .032). But in the BMI ≥ 24 kg/m2 population, the outcome of this SNP conferred an opposite effect on the development of ESCC (GG vs AA: adjusted OR = 1.91, 95% CI = 1.02‐3.59, P = .045).
Table 5.
Stratified analyses between BTLA rs3112270 A > G polymorphism and ESCC risk by sex, age, smoking status, and alcohol consumption
| Variable | BTLA rs3112270 A > G (case/control)a | Adjusted OR (95% CI)b; P | ||||||
|---|---|---|---|---|---|---|---|---|
| AA | GA | GG | AA | AG vs AA | GG vs AA | AG/ GG vs AA | GG vs (AA/AG) | |
| Sex | ||||||||
| Male | 305/459 | 190/372 | 48/64 | 1.00 |
0.78 (0.61‐0.99); P: .042 |
1.18 (0.77‐1.81); P: .459 |
0.83 (0.66‐1.05); P: .123 |
1.31 (0.86‐1.99); P: .209 |
| Female | 82/155 | 69/128 | 17/23 | 1.00 |
1.08 (0.72‐1.62); P: .720 |
1.66 (0.82‐3.36); P: .158 |
1.16 (0.79‐1.71); P: .451 |
1.61 (0.82‐3.16); P: .172 |
| Age | ||||||||
| <63 | 180/291 | 118/239 | 33/47 | 1.00 |
0.77 (0.56‐1.06); P: .111 |
1.19 (0.71‐2.01); P: .514 |
0.84 (0.63‐1.13); P: .256 |
1.32 (0.80‐2.20); P: .280 |
| ≥63 | 207/323 | 141/261 | 32/40 | 1.00 |
0.88 (0.66‐1.16); P: .357 |
1.33 (0.79‐2.23); P: .281 |
0.94 (0.72‐1.22); P: .624 |
1.41 (0.85‐2.33); P: .183 |
| Smoking status | ||||||||
| Never | 179/446 | 124/361 | 34/67 | 1.00 |
0.86 (0.66‐1.14); P: .295 |
1.27 (0.80‐2.01); P: .306 |
0.93 (0.72‐1.20); P: .572 |
1.35 (0.87‐2.11); P: .183 |
| Ever | 208/168 | 135/139 | 31/20 | 1.00 |
0.80 (0.58‐1.10); P: .169 |
1.33 (0.72‐2.47); P: .366 |
0.86 (0.631‐1.17); P: .348 |
1.46 (0.80‐2.68); P: .217 |
| Alcohol consumption | ||||||||
| Never | 259/530 | 189/432 | 48/78 | 1.00 |
0.90 (0.71‐1.14); P: .388 |
1.28 (0.86‐1.92); P: .225 |
0.96 (0.77‐1.20); P: .720 |
1.34 (0.91‐1.99); P: .139 |
| Ever | 128/84 | 70/68 | 17/9 | 1.00 |
0.61 (0.38‐0.97); P: .037 |
1.25 (0.49‐3.21); P: .643 |
0.68 (0.44‐1.06); P: .087 |
1.52 (0.61‐3.82); P: .373 |
| BMI (kg/m2) | ||||||||
| <24 | 296/325 | 176/267 | 46/53 | 1.00 |
0.72 (0.55‐0.93); P: .012 |
1.03 (0.66‐1.61); P: .906 |
0.77 (0.60‐0.98); P: .032 |
1.18 (0.76‐1.82); P: .462 |
| ≥24 | 91/289 | 83/233 | 19/34 | 1.00 |
1.16 (0.81‐1.65); P: .420 |
1.91 (1.02‐3.59); P: .045 |
1.25 (0.897‐1.76); P: .200 |
1.78 (0.97‐3.27); P: .062 |
Bold values are statistically significant (P < .05).
Abbreviations: BMI, body mass index; CI, confidence interval; OR, odds ratio.
The genotyping was successful in 711 (98.61%) ESCC cases and 1201 (99.42%) controls for BTLA rs3112270 A > G.
Adjusted for age, sex, smoking status, and alcohol consumption (besides stratified factors accordingly) in a logistic regression model.
However, as shown in Tables 6 and 7, our results identified that there was no significant difference of distribution in BTLA rs1982809 G > A and rs16859629 T > C variants among any ESCC subgroup and age‐/sex‐matched controls (all P > .05).
Table 6.
Stratified analyses between BTLA rs1982809 G > A polymorphism and ESCC risk by sex, age, smoking status, and alcohol consumption
| Variable | BTLA rs1982809 G > A (case/control)a | Adjusted OR (95% CI)b; P | ||||||
|---|---|---|---|---|---|---|---|---|
| GG | GA | AA | GG | GA vs GG | AA vs GG | GA/AA vs GG | AA vs (GG/GA) | |
| Sex | ||||||||
| Male | 316/497 | 189/342 | 39/56 | 1.00 |
0.95 (0.74‐1.21); P: .669 |
1.37 (0.87‐2.17); P: .178 |
1.01 (0.80‐1.27); P: .967 |
1.40 (0.89‐2.20); P: .142 |
| Female | 92/160 | 63/122 | 14/24 | 1.00 |
0.89 (0.60‐1.34); P: .586 |
1.20 (0.58‐2.49); P: .626 |
0.94 (0.64‐1.38); P: .747 |
1.26 (0.62‐2.55); P: .528 |
| Age | ||||||||
| <63 | 192/302 | 111/232 | 30/43 | 1.00 |
0.78 (0.57‐1.07); P: .119 |
1.36 (0.80‐2.34); P: .260 |
0.86 (0.64‐1.16); P: .332 |
1.51 (0.89‐2.55); P: .125 |
| ≥63 | 216/355 | 141/232 | 23/37 | 1.00 |
1.05 (0.80‐1.40); P: .718 |
1.18 (0.67‐2.07); P: .567 |
1.07 (0.82‐1.40); P: .623 |
1.16 (0.66‐2.01); P: .610 |
| Smoking status | ||||||||
| Never | 186/470 | 121/339 | 32/65 | 1.00 |
0.91 (0.69‐1.19); P: .483 |
1.25 (0.79‐2.00); P: .344 |
0.96 (0.74‐1.25); P: .772 |
1.30 (0.83‐2.05); P: .252 |
| Ever | 222/187 | 131/125 | 21/15 | 1.00 |
0.96 (0.69‐1.33); P: .802 |
1.46 (0.71‐2.97); P: .304 |
1.01 (0.74‐1.38); P: .954 |
1.48 (0.73‐2.99); P: .275 |
| Alcohol consumption | ||||||||
| Never | 270/558 | 189/410 | 39/72 | 1.00 |
0.96 (0.76‐1.22); P: .753 |
1.22 (0.79‐1.88); P: .364 |
1.00 (0.80‐1.25); P: .998 |
1.24 (0.82‐1.88); P: .315 |
| Ever | 138/99 | 63/54 | 14/8 | 1.00 |
0.76 (0.47‐1.23); P: .270 |
1.98 (0.72‐5.45); P: .188 |
0.88 (0.56‐1.38); P: .576 |
2.16 (0.80‐5.89); P: .131 |
| BMI (kg/m2) | ||||||||
| <24 | 306/355 | 180/245 | 33/45 | 1.00 |
0.88 (0.68‐1.14); P: .329 |
1.11 (0.67‐1.82); P: .695 |
0.91 (0.71‐1.16); P: .454 |
1.16 (0.72‐1.89); P: .545 |
| ≥24 | 102/302 | 72/219 | 20/35 | 1.00 |
1.02 (0.71‐1.46); P: .936 |
1.71 (0.93‐3.16); P: .087 |
1.11 (0.79‐1.57); P: .532 |
1.70 (0.94‐3.08); P: .081 |
Abbreviations: BMI, body mass index; CI, confidence interval; OR, odds ratio.
The genotyping was successful in 713 (98.89%) ESCC cases and 1201 (99.42%) controls for BTLA rs1982809 G > A.
Adjusted for age, sex, smoking status, and alcohol consumption (besides stratified factors accordingly) in a logistic regression model.
Table 7.
Stratified analyses between BTLA rs16859629 T > C polymorphism and ESCC risk by sex, age, smoking status, and alcohol consumption
| Variable | BTLA rs16859629 T > C (case/control)a | Adjusted OR (95% CI)b; P | ||||||
|---|---|---|---|---|---|---|---|---|
| TT | TC | CC | TT | TC vs TT | CC vs TT | TC/CC vs TT | CC vs (TT/TC) | |
| Sex | ||||||||
| Male | 469/739 | 68/138 | 3/5 | 1.00 |
0.87 (0.63‐1.22); P: .433 |
1.73 (0.39‐7.65); P: .469 |
0.90 (0.65‐1.25); P: .519 |
1.77 (0.40‐7.82); P: .451 |
| Female | 153/258 | 17/43 | 0/3 | 1.00 |
0.68 (0.37‐1.25); P: .213 |
‐ |
0.63 (0.34‐1.14); P: .125 |
‐ |
| Age | ||||||||
| <63 | 295/481 | 37/85 | 0/3 | 1.00 |
0.79 (0.50‐1.24); P: .305 |
‐ |
0.75 (0.48‐1.18); P: .215 |
‐ |
| ≥63 | 327/516 | 48/96 | 3/5 | 1.00 |
0.85 (0.57‐1.25); P: .398 |
1.41 (0.32‐6.21); P: .649 |
0.87 (0.59‐1.27); P: .466 |
1.45 (0.33‐6.38); P: .623 |
| Smoking status | ||||||||
| Never | 292/716 | 42/143 | 3/7 | 1.00 |
0.74 (0.51‐1.08); P: .116 |
0.97 (0.24‐3.92); P: .968 |
0.75 (0.52‐1.08); P: .126 |
1.02 (0.25‐4.10); P: .981 |
| Ever | 330/281 | 43/38 | 0/1 | 1.00 |
0.98 (0.61‐1.58); P: .931 |
‐ |
0.95 (0.59‐1.54); P: .847 |
‐ |
| Alcohol consumption | ||||||||
| Never | 436/863 | 58/157 | 3/7 | 1.00 |
0.78 (0.56‐1.09); P: .145 |
0.99 (0.25‐3.99); P: .988 |
0.79 (0.57‐1.09); P: .154 |
1.03 (0.26‐4.14); P: .971 |
| Ever | 186/134 | 27/24 | 0/1 | 1.00 |
1.00 (0.52‐1.90); P: .993 |
‐ |
0.95 (0.50‐1.80); P: .879 |
‐ |
| BMI (kg/m2) | ||||||||
| <24 | 455/534 | 61/96 | 2/5 | 1.00 |
0.82 (0.57‐1.17); P: .275 |
0.75 (0.14‐3.97); P: .734 |
0.82 (0.57‐1.16); P: .259 |
0.77 (0.15‐4.09); P: .761 |
| ≥24 | 167/463 | 24/85 | 1/3 | 1.00 |
0.84 (0.51‐1.39); P: .508 |
1.07 (0.10‐11.24); P: .958 |
0.85 (0.52‐1.39); P: .522 |
1.09 (0.10‐11.53); P: .941 |
Abbreviations: BMI, body mass index; CI, confidence interval; OR, odds ratio.
The genotyping was successful in 710 (98.47%) ESCC cases and 1186 (98.18%) controls for BTLA rs16859629 T > C.
Adjusted for age, sex, smoking status, and alcohol consumption (besides stratified factors accordingly) in a logistic regression model.
4. DISCUSSION
Recently, increasing evidence has identified the role of immunosurveillance in supporting tumor growth, and various checkpoint inhibitors, such as ipilimumab and pembrolizumab, which represent the CTLA‐4 and PD‐1 molecules, have been proven as successful in some iconic clinical trials that refer to the treatment of several advanced tumors.20, 21 And as related to the complicated cause of ESCC, although undefined, multiple gene loci have been confirmed to drive esophageal lesions, which led to a more poor prognosis of ESCC.22 In this case‐control study of exploring the potential association between polymorphisms of the co‐inhibitory BTLA gene with the susceptibility to ESCC, we found those four tagging SNPs might not influence the entire ESCC risk for the first time. But stratified analyses found a significant relationship between the two candidate SNPs of rs3112270 A > G and rs2171513 G > A and ESCC risk, which indicated the two polymorphisms in BTLA gene might be involved in the etiology of ESCC.
The pathogenesis of ESCC is complex, where the interrelationship between environmental exposures and individual genetic mutations could result in the deterioration of ESCC.23, 24, 25 As revealed in our study, there were possible gene‐environment interactions for the polymorphisms of BTLA rs3112270 A > G with ESCC susceptibility; especially for the individuals with different BMI settings, their corresponding risk of ESCC was different. Although the mechanism between BMI and ESCC development remained unclear, and concerning that BMI could reflect the status of body nutrition, there was some possible evidence proving that obesity was correlated with the increased level of cancer‐related hormones, such as insulin‐like growth factor, which could be involved in the regulation of cell cycle.26 Additionally, the site of this SNP was located at the promoter region of BTLA gene, where this region could bind to some proteins and further affect the process of DNA transcription and translation in vitro,27, 28 which might explain the mutation from A to G in BTLA gene could influence the progression of ESCC. Considering the role of this SNP was not set up yet, more case‐control studies should be conducted to clarify the accurate mechanism of this variant.
As for the rs2171513 G > A polymorphism, we identified a significant difference in the distribution of BTLA rs2171513 G > A variant in the ever drinking subgroup, which suggested the frequencies of GG genotype in rs2171513 are higher in ESCC subjects than that of the controls, which was consistent with previous researches.29, 30 Yang et al29 showed that the AA and GA genotypes of this SNP were associated with increased susceptibility to ankylosing spondylitis among the Chinese population, while Lnuo et al30 found no distribution differences in alleles, genotypes, and haplotypes of this SNP for type 1 diabetes and systemic lupus erythematosus among Japanese people. Besides, the expression of BTLA was found to be up‐regulated in various tumors.31, 32 For instance, Oguro et al31 showed that the elevated expression of BTLA was closely correlated with a lower density of CD8+ T cells, and further indicated that the higher expression of BTLA was associated with a worse prognosis in gallbladder cancer patients. Taken together, we postulated that the genetic mutation occurred in the exon 5 of BTLA gene might influence the function of BTLA‐HVEM pathway, where this signaling pathway has been proven to decrease local immune response in the tumor tissue of ESCC.33 In the future, more replicated studies about this SNP are required to confirm our hypothesis in esophageal carcinogenesis.
However, several limitations should be addressed when explaining the final results. First, the included participants with limited samples were originated from only two hospitals, which could not fully represent the eastern Chinese population and might inevitably lead to selection bias. So we used stratified analyses as compensation. Second, only four tagging BTLA SNPs were selected in this study, which might restrict to draw a firm conclusion about the exact relationship of BTLA polymorphisms with the risk of ESCC. Third, despite the samples of our research were relatively large, unfortunately, we had no extra DNA specimens to validate our primary findings. Finally, in the current research, functional experiments were not carried out to explore the biologic mechanisms of these polymorphisms during the development of ESCC.
5. CONCLUSION
Despite these limitations, our preliminary findings suggest that the two tagging variants of rs3112270 A > G and rs2171513 G > A in the BTLA gene might contribute to the progression of ESCC in the eastern Chinese population, which is the first study for the involvement of the co‐inhibitory BTLA SNPs in ESCC to our knowledge. But future intensive studies with larger samples are worth to elucidate these works as well as the underlying molecular function of BTLA polymorphisms.
CONFLICT OF INTEREST
The author reports no potential financial conflicts of interest in this work.
ACKNOWLEDGMENTS
We appreciate all subjects who participated in this study. We wish to thank Dr Yan Liu (Genesky Biotechnologies Inc, Shanghai, China) for technical support. This work was supported in part by Young and Middle‐aged Talent Training Project of Health Development Planning Commission in Fujian Province (2016‐ZQN‐25), Program for New Century Excellent Talents in Fujian Province University (NCETFJ‐2017B015), Joint Funds for the innovation of science and Technology, Fujian province (2017Y9099), General Project of Jiangsu Provincial Commission of Health and Family Planning (Z2017021), and 333 Talent Training Project of Organization Department in Jiangsu Province (BRA2017147).
Cao R, Tang W, Chen S. Association between BTLA polymorphisms and susceptibility to esophageal squamous cell carcinoma in the Chinese population. J Clin Lab Anal. 2020;34:e23221 10.1002/jcla.23221
Rui Cao and Weifeng Tang authors contributed equally to this work.
REFERENCES
- 1. Bray F, Ferlay J, Soerjomataram I, et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2018;68(6):394‐424. [DOI] [PubMed] [Google Scholar]
- 2. Chen W, Zheng R, Baade PD, et al. Cancer statistics in China, 2015. CA Cancer J Clin. 2016;66(2):115‐132. [DOI] [PubMed] [Google Scholar]
- 3. He Y, Li D, Shan B, et al. Incidence and mortality of esophagus cancer in China, 2008–2012. Chin J Cancer Res. 2019;31(3):426‐434. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4. Jeene PM, Versteijne E, van Berge Henegouwen MI, et al. Supraclavicular node disease is not an independent prognostic factor for survival of esophageal cancer patients treated with definitive chemoradiation. Acta Oncol. 2017;56(1):33‐38. [DOI] [PubMed] [Google Scholar]
- 5. Jung IY, Kim YY, Yu HS, et al. CRISPR/Cas9‐Mediated Knockout of DGK improves antitumor activities of human T cells. Cancer Res. 2018;78(16):4692‐4703. [DOI] [PubMed] [Google Scholar]
- 6. Han P, Goularte OD, Rufner K, et al. An inhibitory Ig superfamily protein expressed by lymphocytes and APCs is also an early marker of thymocyte positive selection. J Immunol. 2004;172(10):5931‐5939. [DOI] [PubMed] [Google Scholar]
- 7. Wang Y, Subudhi SK, Anders RA, et al. The role of herpesvirus entry mediator as a negative regulator of T cell‐mediated responses. J Clin Invest. 2005;115(3):711‐717. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8. Sedy JR, Gavrieli M, Potter KG, et al. B and T lymphocyte attenuator regulates T cell activation through interaction with herpesvirus entry mediator. Nat Immunol. 2005;6(1):90‐98. [DOI] [PubMed] [Google Scholar]
- 9. Cai G, Freeman GJ. The CD160, BTLA, LIGHT/HVEM pathway: a bidirectional switch regulating T‐cell activation. Immunol Rev. 2009;229(1):244‐258. [DOI] [PubMed] [Google Scholar]
- 10. Iwata A, Watanabe N, Oya Y, et al. Protective roles of B and T lymphocyte attenuator in NKT cell‐mediated experimental hepatitis. J Immunol. 2010;184(1):127‐133. [DOI] [PubMed] [Google Scholar]
- 11. Xu Y, Flies AS, Flies DB, et al. Selective targeting of the LIGHT‐HVEM costimulatory system for the treatment of graft‐versus‐host disease. Blood. 2007;109(9):4097‐4104. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12. Fu Z, Li D, Jiang W, et al. Association of BTLA gene polymorphisms with the risk of malignant breast cancer in Chinese women of Heilongjiang Province. Breast Cancer Res Treat. 2010;120(1):195‐202. [DOI] [PubMed] [Google Scholar]
- 13. Partyka A, Tupikowski K, Kolodziej A, et al. Association of 3' nearby gene BTLA polymorphisms with the risk of renal cell carcinoma in the Polish population. Urol Oncol. 2016;34(9):e413‐e419. [DOI] [PubMed] [Google Scholar]
- 14. Karabon L, Partyka A, Jasek M, et al. Intragenic Variations in BTLA Gene Influence mRNA Expression of BTLA Gene in Chronic Lymphocytic Leukemia Patients and Confer Susceptibility to Chronic Lymphocytic Leukemia. Arch Immunol Ther Exp. 2016;64:137‐145. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15. Tang W, Chen S, Kang M, et al. Investigation of BTLA tagging variants with risk of esophagogastric junction adenocarcinoma. Biosci Rep. 2019;27:BSR20191770. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16. Gan L, Hu C, Deng Z, et al. Highlight article: Rs1982809 is a functional biomarker for the prognosis of severe post‐traumatic sepsis and MODs. Exp Biol Med . 2019;244(16):1438‐1445. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17. Zhang X, Zhang S, Li Y, et al. Association of obesity and atrial fibrillation among middle‐aged and elderly Chinese. Int J Obes. 2009;33(11):1318‐1325. [DOI] [PubMed] [Google Scholar]
- 18. Li X, Jiang W, Li L, et al. Renalase gene polymorphism in patients with hypertension and concomitant coronary heart disease. Kidney Blood Press Res. 2014;39(1):9‐16. [DOI] [PubMed] [Google Scholar]
- 19. Nuyts S, Van Mellaert L, Lambin P, et al. Efficient isolation of total RNA from Clostridium without DNA contamination. J Microbiol Methods. 2001;44(3):235‐238. [DOI] [PubMed] [Google Scholar]
- 20. Mok TSK, Wu YL, Kudaba I, et al. Pembrolizumab versus chemotherapy for previously untreated, PD‐L1‐expressing, locally advanced or metastatic non‐small‐cell lung cancer (KEYNOTE‐042): a randomised, open‐label, controlled, phase 3 trial. Lancet. 2019;393(10183):1819‐1830. [DOI] [PubMed] [Google Scholar]
- 21. Hodi FS, O'Day SJ, McDermott DF, et al. Improved survival with ipilimumab in patients with metastatic melanoma. N Engl J Med. 2010;363(8):711‐723. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22. Liu P, Zhao HR, Li F, et al. Correlations of ALDH2 rs671 and C12orf30 rs4767364 polymorphisms with increased risk and prognosis of esophageal squamous cell carcinoma in the Kazak and Han populations in Xinjiang province. J Clin Lab Anal. 2018;32(2):e22248. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23. Katada C, Yokoyama T, Yano T, et al. Alcohol consumption and multiple dysplastic lesions increase risk of squamous cell carcinoma in the esophagus, head, and neck. Gastroenterology. 2016;151(5):860‐869. [DOI] [PubMed] [Google Scholar]
- 24. Liu M, Liu Z, Cai H, et al. A model to identify individuals at high risk for esophageal squamous cell carcinoma and precancerous lesions in regions of high prevalence in China. Clin Gastroenterol Hepatol. 2017;15(10):1538‐1546. [DOI] [PubMed] [Google Scholar]
- 25. Yu C, Tang H, Guo Y, et al. Hot tea consumption and its interactions with alcohol and tobacco use on the risk for esophageal cancer: a population‐based cohort study. Ann Intern Med. 2018;168(7):489‐497. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26. Dieudonne MN, Bussiere M, Dos Santos E, et al. Adiponectin mediates antiproliferative and apoptotic responses in human MCF7 breast cancer cells. Biochem Biophys Res Commun. 2006;345:271‐279. [DOI] [PubMed] [Google Scholar]
- 27. Oldfield AJ, Henriques T, Kumar D, et al. NF‐Y controls fidelity of transcription initiation at gene promoters through maintenance of the nucleosome‐depleted region. Nat Commun. 2019;10(1):3072. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28. Zhao XY, Qi CH, Jiang H, et al. MdHIR4 transcription and translation levels associated with disease in apple are regulated by MdWRKY31. Plant Mol Biol. 2019;101:149‐162. [DOI] [PubMed] [Google Scholar]
- 29. Yang B, Zhang J, Li L, et al. Genetic variations in LIGHT are associated with susceptibility to ankylosing spondylitis in a Chinese Han population. Oncotarget. 2017;8(53):91415‐91424. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30. Inuo M, Ihara K, Matsuo T, et al. Association study between B‐ and T‐lymphocyte attenuator gene and type 1 diabetes mellitus or systemic lupus erythematosus in the Japanese population. Int J Immunogenet. 2009;36(1):65‐68. [DOI] [PubMed] [Google Scholar]
- 31. Oguro S, Ino Y, Shimada K, et al. Clinical significance of tumor‐infiltrating immune cells focusing on BTLA and Cbl‐b in patients with gallbladder cancer. J Cancer science. 2015;106(12):1750‐1760. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32. Imai Y, Hasegawa K, Matsushita H, et al. Expression of multiple immune checkpoint molecules on T cells in malignant ascites from epithelial ovarian carcinoma. Oncol Lett. 2018;15(5):6457‐6468. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33. Sho M, Shimada K, Yasuda S, et al. Significant involvement of herpesvirus entry mediator in human esophageal squamous cell carcinoma. Cancer. 2014;120(6):808‐817. [DOI] [PubMed] [Google Scholar]