# Association ofBTLA Polymorphisms with Susceptibility to Non-Small-Cell Lung Cancer in the Chinese Population

Authors: Jusi Wang; Zhan Chen; Rui Cao; Qiang Zhang; Tingyu Chen; Chengxiong You; Weifeng Tang; Shuchen Chen
Journal: BioMed Research International
 (2021)

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## Abstract

Studies have reported thatB- and T-lymphocyte attenuator (BTLA) polymorphisms may be associated with the risk to different cancers. However, the correlation between those variations and non-small-cell lung cancer (NSCLC) is still unclear. A total of 1,003 NSCLC patients and 901 noncancer controls were recruited in the study, to confirm the association of variations in BTLA gene with the risk of NSCLC. The SNPscan™ genotyping assay was used to obtain the genotypes of the four BTLA polymorphisms (BTLA rs1982809 G>A, rs16859629 T>C, rs2171513 G>A, and rs3112270 A>G). It was found that BTLA rs1982809 polymorphism reduced the risk of NSCLC (GA vs. GG: adjustedoddsratioOR=0.81, 95%confidenceintervalCI=0.66‐0.99, and P=0.043). However, the BTLA rs16859629, rs2171513, and rs3112270 polymorphisms showed no significant association between NSCLC patients and controls in overall comparison. In subgroup analyses, we found that BTLA rs1982809 polymorphism reduced the risk of NSCLC (nonsquamous cell carcinoma: GA vs. GG: adjustedOR=0.79, 95%CI=0.64‐0.97, and P=0.026; AA/GA vs. GG: adjustedOR=0.81, 95%CI=0.66‐0.99, and P=0.037; ≥59 years: GA vs. GG: P=0.036; never alcohol consumption: GA vs. GG: P=0.013; GA/AA vs. GG: P=0.016; bodymassindexBMI≥24 kg/m2: GA vs. GG: P=0.030; GA/AA vs. GG: P=0.041). The BTLA rs16859629 polymorphism increased the risk of the development of squamous cell carcinoma (CC vs. TT: adjustedOR=9.85, 95%CI=1.37‐71.03, and P=0.023; CC vs. TT/TC: adjustedOR=9.55, 95%CI=1.32‐68.66, and P=0.025). Taken together, the findings of the present suggest that BTLA rs1982809 and rs16859629 polymorphisms may influence the susceptibility to NSCLC in the Chinese population.


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## Body

## 1. Introduction

Non-small-cell lung cancer (NSCLC) accounts for 80 to 85% of all the lung cancer and is the main pathological type of lung cancer (LC). LC has imposed huge diseases burden on human population and accounts for significant number of mortalities across the globe [1–3]. As per estimates, LC is currently ranked as first in terms of incidence and mortality [4]. The currently used treatment strategies for LC include surgery combined with adjuvant therapy. Owing to the recent advancements made in the diagnosis and treatment, the clinical outcomes have significantly improved [5]. However, the identification of the potential risk factors for the occurrence of LC is considered essential as it will permit early diagnosis and immediate management.The B- and T-lymphocyte attenuator (BTLA), an immunosuppressive receptor, was identified after the cytotoxic T-lymphocyte-associated antigen-4 (CTLA-4) and programmed death-1 (PD-1) and is thus the third member of the CD28 immunoglobulin superfamily (IgSF) [6]. The BTLA upon binding with herpesvirus entry mediator (HVEM) inhibits the T cell response. In contrary, blocking of BTLA may in turn activate T cells [7–9]. Evidences suggest that BTLA plays a crucial immune regulatory role in human malignancies. Liu et al. [10] have reported that BTLA/HVEM pathway plays an important immunosuppressive role in the regulation of T cells in peripheral blood of hepatocellular carcinoma patients. Quan et al. [11] and Li et al. [12] found that the level of BTLA expression may act as a biomarker for the prognosis of diffuse large B cell lymphoma and NSCLC. In yet another study, Chen et al. [13] concluded that chemotherapy in combination with anti-BTLA antibody improves the prognosis of ovarian cancer in a mouse model. Similarly, Zhang et al. [14] reported that BTLA could directly intervene the effects of miR-32 on cancer cells. Collectively, these studies point towards a correlation between BTLA and the development and progression of human cancers.Single nucleotide polymorphism (SNP) mutations constitute an important form of genetic variations. Some recent studies have revealed a significant correlation betweenBTLA SNPs and development of cancer [15–19]. Fu et al. reported that BTLA rs1844089 C>T and rs2705535 A>G variation could increase the risk of breast cancer in human [15]. Partyka et al. and Karabon et al. found that the variation on BTLA rs1982809 G>A gene polymorphism might be a potential risk factor for the development of chronic lymphocytic leukemia (CLL) and renal cell carcinoma [16, 17]. In a previous study, we reported that BTLA rs1982809 SNPs increased the susceptibility of esophagogastric junction adenocarcinoma (EGJA) in smoking subgroup analyses [18]. In another study, we found that the BTLA rs3112270 A>G and rs2171513 G>A polymorphisms could modify the risk of esophageal squamous cell carcinoma (ESCC) [19]. Given this background, we hypothesize that some associations may exist between BTLA polymorphisms and NSCLC pathogenesis.Combined with these previous studies, theBTLA tagging SNPs (rs1982809, rs16859629, rs2171513, and rs3112270) were selected for analysis. Consistently, the present study is aimed at exploring the relationship between BTLA tagging SNPs and the risk of NSCLC.


## 2. Materials and Methods

### 2.1. Subject

The present study involved 1,003 NSCLC patients and 901 healthy controls. Those patients were continuously recruited from Fujian Medical University Union Hospital, Fuzhou, China, from October 2014 to January 2018, and the diagnosis was confirmed by postoperative pathology. The major inclusion criteria for NSCLC patients were (a) the case firstly to be diagnosed, (b) the individuals with no history of other cancers, (c) without any autoimmune disease, and (d) did not receive any chemo- or radiotherapy prior to enrollment. In the same period, the controls were recruited in the Affiliated Union Hospital of Fujian Medical University (Fuzhou, China) and the Affiliated People’s Hospital of Jiangsu University (Zhenjiang, China), and the individuals without any cancer history, mainly healthy, checking individuals, matched with the NSCLC patients by age. The information collected from the patients included age, sex, drinking, smoking history, height, and weight. Furthermore, body mass index (BMI) of more than 24 kg/m2 was considered as obesity and overweight [20]. Each participant was informed about the purpose of the study and was asked to sign a written consent form for participation in the study. The study was approved by the research ethics committee of Fujian Medical University Union Hospital (Approval No. 2018KY023).


### 2.2. Selection ofBTLA Tagging SNPs

The four candidate SNPs were ascertained by applying Genome Variation Server data [18, 19, 21] (http://gvs.gs.washington.edu/GVS147/). We gave priority to represent the main linkage disequilibrium blocks. The following main criteria were used: (a) minorallelefrequency≥0.05, (b) linkage disequilibrium of r2<0.8 between the SNPs, (c) with the 5 kb extent upstream and downstream of the gene regions, and (d) the genotypingvalue≥95% in the CHB cohort. Four BTLA tagging SNPs (rs1982809, rs16859629, rs2171513, and rs3112270) were finally selected to decipher the association between the BTLA polymorphisms and NSCLC risk. The results are shown in Table 1.Table 1
Primary information forBTLA tagging polymorphisms.
Genotyped polymorphismsrs2171513 G>Ars3112270 A>Grs1982809 G>Ars16859629 T>CChr3333Position_38112466080112461797112463893112471533Region3′-UTRPromoter3′-UTRIntron_variantMAF in database (1000 genomes—Chinese Han populations)0.1980.2780.1950.082MAF in our controls (n=901)0.1940.2950.2700.079P value for HWE test in our controls0.5540.2590.5840.747% genotyping value99.00%98.84%98.90%97.48%Abbreviations: MAF: minor allele frequency; HWE: Hardy–Weinberg equilibrium.


### 2.3. DNA Extraction and Genotyping

Around 2 mL of the peripheral blood samples of the subjects (early in the morning, empty stomach) was collected and placed in a vacuum ethylene diamine tetra-acetic acid anticoagulant tube. Genomic DNA was carefully extracted from the collected samples with the help of DNA blood mini kit (Promega, Madison, USA) by following manufacturer’s guidelines. TheBTLA rs1982809, rs16859629, rs2171513, and rs3112270 genotypes were assessed by the SNPscan™ Kit (Genesky Biotechnologies Inc., Shanghai, China) as per manufacturer’s guidelines. For the qualitative assessment, 76 samples (4%) were randomly selected and subsequently tested by another laboratory technician. The genotypes of BTLA were well confirmed repeatedly.


### 2.4. PCR-Specific Experimental Procedures

#### 2.4.1. DNA Cleavage

The DNA was diluted to a concentration of 30 to 50 ng/μL. From this 4 μL, DNA was transferred to a 96-well plate followed by the addition of 2.5 μL 4X DNA buffer. Thereafter, the total volume was made up to 10 μL by the addition of sterile ddH2O. The contents were mixed well and incubated at 98°C for 5 min. Afterwards, the plates were immediately placed in ice.


#### 2.4.2. PCR Reaction

A 10μL of the premixture prepared was centrifuged at 3000 rpm for 30 seconds. After 4 cycles of the PCR program, the mixture was subjected to 70°C warm bath.


#### 2.4.3. Multiple Fluorescence PCR Reactions

A 19μL PCR premixture was added to each hole. This was followed thorough mixing and centrifugation at 3000 rpm for 30 seconds. The reaction was subjected to 34 cycles of the PCR program followed by a 4°C warm bath.


#### 2.4.4. Reading of PCR Results

The GeneMapper 4.2 software (Applied Biosystem, USA) was employed for the genotype analysis of the original sequence data.


### 2.5. Statistical Analysis

For the selectedBTLA genotypes in controls, the internet software (http://ihg.gsf.de/cgi-bin/hw/hwa1.pl) was employed to assess if the genotype frequency distribution conforms to Hardy–Weinberg equilibrium (HWE) or not. Mean±standarddeviationSD was adopted for continuous variables. The t-test was applied to calculate the difference between NSCLC and healthy subjects. The chi-square (χ2) or Fisher test was utilized to compare the distributions of the BTLA SNPs in related categorical variables between the two groups, including age, gender, tobacco use, drinking, BMI, and genotype frequency. Multivariate logistic regression was used to examine the adjusted odds ratio (OR) and 95% confidence intervals (CIs) in evaluating the relationship between selective BTLA SNPs and susceptibility to NSCLC. All data were analyzed by the SAS 9.4 (windows version; SAS Institute Inc., Cary, NC) and SPSS 23 software (windows version; International Business Machines Corp.). Only P<0.05 was considered as a statistically significance.


## 2.1. Subject

The present study involved 1,003 NSCLC patients and 901 healthy controls. Those patients were continuously recruited from Fujian Medical University Union Hospital, Fuzhou, China, from October 2014 to January 2018, and the diagnosis was confirmed by postoperative pathology. The major inclusion criteria for NSCLC patients were (a) the case firstly to be diagnosed, (b) the individuals with no history of other cancers, (c) without any autoimmune disease, and (d) did not receive any chemo- or radiotherapy prior to enrollment. In the same period, the controls were recruited in the Affiliated Union Hospital of Fujian Medical University (Fuzhou, China) and the Affiliated People’s Hospital of Jiangsu University (Zhenjiang, China), and the individuals without any cancer history, mainly healthy, checking individuals, matched with the NSCLC patients by age. The information collected from the patients included age, sex, drinking, smoking history, height, and weight. Furthermore, body mass index (BMI) of more than 24 kg/m2 was considered as obesity and overweight [20]. Each participant was informed about the purpose of the study and was asked to sign a written consent form for participation in the study. The study was approved by the research ethics committee of Fujian Medical University Union Hospital (Approval No. 2018KY023).


## 2.2. Selection ofBTLA Tagging SNPs

The four candidate SNPs were ascertained by applying Genome Variation Server data [18, 19, 21] (http://gvs.gs.washington.edu/GVS147/). We gave priority to represent the main linkage disequilibrium blocks. The following main criteria were used: (a) minorallelefrequency≥0.05, (b) linkage disequilibrium of r2<0.8 between the SNPs, (c) with the 5 kb extent upstream and downstream of the gene regions, and (d) the genotypingvalue≥95% in the CHB cohort. Four BTLA tagging SNPs (rs1982809, rs16859629, rs2171513, and rs3112270) were finally selected to decipher the association between the BTLA polymorphisms and NSCLC risk. The results are shown in Table 1.Table 1
Primary information forBTLA tagging polymorphisms.
Genotyped polymorphismsrs2171513 G>Ars3112270 A>Grs1982809 G>Ars16859629 T>CChr3333Position_38112466080112461797112463893112471533Region3′-UTRPromoter3′-UTRIntron_variantMAF in database (1000 genomes—Chinese Han populations)0.1980.2780.1950.082MAF in our controls (n=901)0.1940.2950.2700.079P value for HWE test in our controls0.5540.2590.5840.747% genotyping value99.00%98.84%98.90%97.48%Abbreviations: MAF: minor allele frequency; HWE: Hardy–Weinberg equilibrium.


## 2.3. DNA Extraction and Genotyping

Around 2 mL of the peripheral blood samples of the subjects (early in the morning, empty stomach) was collected and placed in a vacuum ethylene diamine tetra-acetic acid anticoagulant tube. Genomic DNA was carefully extracted from the collected samples with the help of DNA blood mini kit (Promega, Madison, USA) by following manufacturer’s guidelines. TheBTLA rs1982809, rs16859629, rs2171513, and rs3112270 genotypes were assessed by the SNPscan™ Kit (Genesky Biotechnologies Inc., Shanghai, China) as per manufacturer’s guidelines. For the qualitative assessment, 76 samples (4%) were randomly selected and subsequently tested by another laboratory technician. The genotypes of BTLA were well confirmed repeatedly.


## 2.4. PCR-Specific Experimental Procedures

### 2.4.1. DNA Cleavage

The DNA was diluted to a concentration of 30 to 50 ng/μL. From this 4 μL, DNA was transferred to a 96-well plate followed by the addition of 2.5 μL 4X DNA buffer. Thereafter, the total volume was made up to 10 μL by the addition of sterile ddH2O. The contents were mixed well and incubated at 98°C for 5 min. Afterwards, the plates were immediately placed in ice.


### 2.4.2. PCR Reaction

A 10μL of the premixture prepared was centrifuged at 3000 rpm for 30 seconds. After 4 cycles of the PCR program, the mixture was subjected to 70°C warm bath.


### 2.4.3. Multiple Fluorescence PCR Reactions

A 19μL PCR premixture was added to each hole. This was followed thorough mixing and centrifugation at 3000 rpm for 30 seconds. The reaction was subjected to 34 cycles of the PCR program followed by a 4°C warm bath.


### 2.4.4. Reading of PCR Results

The GeneMapper 4.2 software (Applied Biosystem, USA) was employed for the genotype analysis of the original sequence data.


## 2.4.1. DNA Cleavage

The DNA was diluted to a concentration of 30 to 50 ng/μL. From this 4 μL, DNA was transferred to a 96-well plate followed by the addition of 2.5 μL 4X DNA buffer. Thereafter, the total volume was made up to 10 μL by the addition of sterile ddH2O. The contents were mixed well and incubated at 98°C for 5 min. Afterwards, the plates were immediately placed in ice.


## 2.4.2. PCR Reaction

A 10μL of the premixture prepared was centrifuged at 3000 rpm for 30 seconds. After 4 cycles of the PCR program, the mixture was subjected to 70°C warm bath.


## 2.4.3. Multiple Fluorescence PCR Reactions

A 19μL PCR premixture was added to each hole. This was followed thorough mixing and centrifugation at 3000 rpm for 30 seconds. The reaction was subjected to 34 cycles of the PCR program followed by a 4°C warm bath.


## 2.4.4. Reading of PCR Results

The GeneMapper 4.2 software (Applied Biosystem, USA) was employed for the genotype analysis of the original sequence data.


## 2.5. Statistical Analysis

For the selectedBTLA genotypes in controls, the internet software (http://ihg.gsf.de/cgi-bin/hw/hwa1.pl) was employed to assess if the genotype frequency distribution conforms to Hardy–Weinberg equilibrium (HWE) or not. Mean±standarddeviationSD was adopted for continuous variables. The t-test was applied to calculate the difference between NSCLC and healthy subjects. The chi-square (χ2) or Fisher test was utilized to compare the distributions of the BTLA SNPs in related categorical variables between the two groups, including age, gender, tobacco use, drinking, BMI, and genotype frequency. Multivariate logistic regression was used to examine the adjusted odds ratio (OR) and 95% confidence intervals (CIs) in evaluating the relationship between selective BTLA SNPs and susceptibility to NSCLC. All data were analyzed by the SAS 9.4 (windows version; SAS Institute Inc., Cary, NC) and SPSS 23 software (windows version; International Business Machines Corp.). Only P<0.05 was considered as a statistically significance.


## 3. Results

### 3.1. Baseline Characteristics

A total of 1,003 NSCLC cases and 901 controls were recruited in this study. The risk factors that may influence the development of NSCLC are listed in Table2. Although a good match was observed between the age of the NSCLC patients and the control group (P=0.139), significant differences were observed in sex, smoking, alcohol consumption, and BMI between the two groups (P<0.001). The genotyping rate of BTLA rs2171513, rs3112270, rs1982809, and rs16859629 was higher than 95% (99.00%, 98.84%, 98.90%, and 97.48%, respectively) as shown in Table 1. Minor allele frequencies (MAFs) in the control were similar to those of the Chinese populations, and the genotype frequency distributions were in accordance with the HWE.Table 2
Distribution of selected demographic variables and risk factors in NSCLC cases and controls.
VariableCases (n=1,003)Controls (n=901)Pan%n%Age (years)58.76±9.9259.43±9.670.139Age (years)0.292<5946546.3639643.95≥5953853.6450556.05Sex<0.001Male52051.8455261.27Female48348.1634938.73Tobacco use<0.001Never63463.2171779.58Ever36936.7918420.42Alcohol use<0.001Never78978.6680489.23Ever21421.349710.77BMI (kg/m2)<0.001<2467267.0049755.16≥2433133.0040444.84Type of NSCLCSCC14514.46Non-SCC85885.54Notes:atwo-sided χ2 test and Student’s t-test. Abbreviations: BMI: body mass index; NSCLC: non-small-cell lung cancer; SCC: squamous cell carcinoma.


### 3.2. Association betweenBTLA Polymorphisms and Susceptibility to NSCLC

The genotype distribution ofBTLA rs1982809, rs16859629, rs2171513, and rs3112270 SNPs is shown in Table 3. It was found that GG+AG genotype of BTLA rs3112270 polymorphism may be a protective factor for NSCLC compared to the AA genotype (GG+AG: OR=0.83, 95%CI=0.69‐0.99, and P=0.038). Meanwhile, compared to the BTLA rs1982809 GG genotype, the BTLA rs1982809 GA and AA+GA genotypes are also associated with the development of NSCLC (GA: OR=0.81, 95%CI=0.67‐0.98, and P=0.030; AA+GA: OR=0.83, 95%CI=0.69‐0.99, and P=0.042). Adjusting the risk factors (age, sex, smoking, drinking, and BMI status), the genotypes of BTLA rs3112270 polymorphism were not closely correlated with NSCLC susceptibility, whereas the GA genotype of BTLA rs1982809 SNPs still decreased the susceptibility of NSCLC patients (GA: adjustedOR=0.81, 95%CI=0.66‐0.99, and P=0.043; Table 4).Table 3
The frequencies ofBTLA tagging polymorphisms in different NSCLC subgroups.
GenotypeNSCLC (n=1,003)SCC (n=145)Non-SCC (n=858)Controls (n=901)n%n%n%n%rs2171513 G>AGG65466.0610673.6154864.7858265.03GA29730.003524.3126230.9727630.84AA393.9432.08364.26374.13A allele37518.944114.2433419.7435019.55rs3112270 A>GAA52753.397451.3945353.7443548.60AG39239.726142.3633139.2638843.35GG686.8996.25597.00728.04G allele52826.757927.4344926.6353229.72rs1982809 G>AGG56657.297753.4748957.9447152.63GA35135.535437.5029735.1936140.34AA717.19139.03586.87637.04A allele49324.958027.7841324.4748727.21rs16859629 T>CTT83585.8211582.1472086.4374684.48TC13113.462316.4310812.9713214.95CC70.7221.4350.6050.57C allele1457.45279.641187.081428.04Table 4
Logistic regression analyses of association ofBTLA tagging polymorphisms with risk of NSCLC.
GenotypeOverall NSCLC cases (n=1,003) vs. controls (n=901)SCC (n=145) vs. controls (n=901)Non-SCC (n=858) vs. controls (n=901)Crude OR (95% CI)PAdjusted ORa (95% CI)PCrude OR (95% CI)PAdjusted ORa (95% CI)PCrude OR (95% CI)PAdjusted ORa (95% CI)Prs2171513 G>AGA vs. GG0.96 (0.79-1.17)0.6691.00 (0.81-1.24)0.9710.70 (0.46-1.05)0.0820.83 (0.51-1.32)0.4261.01 (0.82-1.24)0.9381.03 (0.83-1.28)0.760AA vs. GG0.94 (0.59-1.49)0.7870.97 (0.60-1.59)0.9080.45 (0.14-1.47)0.1840.32 (0.09-1.18)0.0861.03 (0.64-1.66)0.8921.06 (0.65-1.74)0.816AA/GA vs. GG0.96 (0.79-1.16)0.6371.00 (0.82-1.22)1.000.67 (0.45-0.99)0.0440.74 (0.47-1.17)0.1951.01 (0.83-1.23)0.9121.04 (0.84-1.28)0.728AA vs. GG/GA0.95 (0.60-1.51)0.8300.97 (0.60-1.58)0.9040.49 (0.15-1.62)0.2450.34 (0.09-1.23)0.0991.03 (0.65-1.65)0.9001.05 (0.64-1.72)0.847rs3112270 A>GAG vs. AA0.83 (0.69-1.01)0.0600.86 (0.70-1.05)0.1250.92 (0.64-1.33)0.6721.10 (0.72-1.69)0.6670.82 (0.67-1.00)0.0470.82 (0.67-1.01)0.064GG vs. AA0.78 (0.55-1.11)0.1690.81 (0.56-1.18)0.2750.74 (0.35-1.53)0.4120.80 (0.35-1.85)0.6030.79 (0.54-1.14)0.2020.81 (0.55-1.20)0.293GG/AG vs. AA0.83 (0.69-0.99)0.0380.85 (0.70-1.03)0.0920.90 (0.63-1.27)0.5351.05 (0.70-1.58)0.8180.81 (0.67-0.98)0.0330.82 (0.67-1.00)0.051GG vs. AA/AG0.85 (0.60-1.19)0.3400.87 (0.61-1.25)0.4560.76 (0.37-1.56)0.4580.77 (0.34-1.73)0.5220.86 (0.60-1.23)0.4110.89 (0.61-1.29)0.530rs1982809 G>AGA vs. GG0.81 (0.67-0.98)0.0300.81 (0.66-0.99)0.0430.92 (0.63-1.33)0.6421.05 (0.68-1.62)0.8330.79 (0.65-0.97)0.0220.79 (0.64-0.97)0.026AA vs. GG0.94 (0.65-1.35)0.7270.99 (0.68-1.45)0.9601.26 (0.66-2.40)0.4781.83 (0.84-3.97)0.1300.89 (0.61-1.30)0.5340.93 (0.63-1.39)0.724AA/GA vs. GG0.83 (0.69-0.99)0.0420.84 (0.69-1.02)0.0710.97 (0.68-1.38)0.8501.15 (0.76-1.73)0.5220.81 (0.67-0.98)0.0260.81 (0.66-0.99)0.037AA vs. GG/GA1.02 (0.72-1.45)0.9011.08 (0.74-1.57)0.6901.31 (0.70-2.45)0.3961.79 (0.84-3.80)0.1310.98 (0.67-1.41)0.8911.03 (0.70-1.51)0.900rs16859629 T>CTC vs. TT0.89 (0.68-1.15)0.3670.89 (0.68-1.18)0.4121.13 (0.70-1.84)0.6201.19 (0.68-2.11)0.5440.85 (0.64-1.12)0.2380.86 (0.65-1.15)0.307CC vs. TT1.25 (0.40-3.96)0.7031.38 (0.41-4.58)0.6022.60 (0.50-13.53)0.2589.85 (1.37-71.03)0.0231.04 (0.30-3.59)0.9551.09 (0.30-4.01)0.898CC/TC vs. TT0.90 (0.70-1.16)0.4190.91 (0.69-1.19)0.4861.18 (0.74-1.89)0.4811.31 (0.75-2.28)0.3380.86 (0.65-1.12)0.2530.87 (0.65-1.15)0.330CC vs. TT/TC1.27 (0.40-4.02)0.6821.40 (0.42-4.66)0.5842.55 (0.49-13.25)0.2679.55 (1.32-68.66)0.0251.06 (0.31-3.68)0.9261.11 (0.30-4.09)0.873Notes:aadjusted for age, sex, smoking status, alcohol use, and BMI status in a logistic regression model.Collectively, the results suggest that the genotypes inBTLA rs2171513 and rs16859629 mutations may be associated with the development of NSCLC.


### 3.3. Association ofBTLA Polymorphisms with Susceptibility in Different NSCLC Subgroups

The results showed that AA genotype ofBTLA rs2171513 polymorphism might be considered as a susceptibility factor for the development of squamous cell carcinoma (SCC) (AA/GA: OR=0.67, 95%CI=0.45‐0.99, and P=0.044) (Table 4). Adjusting the risk factors (age, sex, smoking, drinking, and BMI status), the BTLA rs2171513 polymorphism was not relevant to the occurrence of SCC. Nonetheless, using TT and TT+TC genotypes as references, the results showed that the CC genotype of BTLA rs16859629 polymorphisms may promote the development of SCC (CC vs. TT: adjustedOR=9.85, 95%CI=1.37‐71.03, and P=0.023; CC vs. TT+TC: adjustedOR=9.55, 95%CI=1.32‐68.66, and P=0.025).In nonsquamous cell carcinoma (non-SCC), using AA genotype as reference, it was found that GG and GA+GG genotypes ofBTLA rs3112270 polymorphisms may decrease the incidence of non-SCC (AG: OR=0.82, 95%CI=0.67‐1.00, and P=0.047; GG+AG: OR=0.81, 95%CI=0.67‐0.98, and P=0.033). Meanwhile, it was also found that the GA and GA+AA genotypes of BTLA rs1982809 polymorphism also decreased the occurrence of non-SCC with reference to GG genotype (GA vs. GG: OR=0.79, 95%CI=0.65‐0.97, and P=0.022; AA/GA vs. GG: OR=0.81, 95%CI=0.67‐0.98, and P=0.026). Adjusting the related risk factors, it is concluded that the BTLA rs3112270 polymorphism did not exhibit the tendency to change the risk to non-SCC. However, BTLA rs1982809 polymorphism was a protective factor of the susceptibility to non-SCC (GA: adjustedOR=0.79, 95%CI=0.64‐0.97, and P=0.026; AA+GA: adjustedOR=0.81, 95%CI=0.66‐0.99, and P=0.037).The genotype frequencies ofBTLA rs1982809 polymorphism in the subgroup analyses are depicted in Table 5. In ≥59-year subgroup, it was found that the variants of BTLA rs1982809 decreased the incidence of NSCLC (GA vs. GG: P=0.036). In never alcohol and BMI≥24 kg/m2 subgroup, it was found that similar genotype variants of BTLA rs1982809 might be a protective factor of NSCLC (never smoking subgroup: GA vs. GG: P=0.013; AA/GA vs. GG: P=0.016; BMI≥24 kg/m2: GA vs. GG: P=0.030; AA/GA vs. GG: P=0.041). Additionally, it was found that BTLA rs2171513, rs3112270, and rs16859629 polymorphisms were not associated with the morbidity of NSCLC in subgroups (Tables 6–8).Table 5
Stratified analyses betweenBTLA rs1982809 G>A polymorphism and NSCLC risk by sex, age, smoking status, alcohol consumption, and BMI.
VariableBTLA rs1982809 G>A (case/control)aAdjusted ORb (95% CI); PGGGAAAGGGA vs. GGAA vs. GGGA/AA vs. GGAA vs. (GG/GA)SexMale299/289177/22837/331.000.79 (0.60-1.05);P: 0.1001.32 (0.77-2.27);P: 0.3140.86 (0.66-1.11);P: 0.2461.45 (0.86-2.46);P: 0.167Female267/182174/13334/301.000.87 (0.64-1.18);P: 0.3660.77 (0.45-1.33);P: 0.3520.85 (0.64-1.14);P: 0.2740.82 (0.49-1.38);P: 0.457Age<59254/203173/16231/281.000.93 (0.69-1.27);P: 0.6510.90 (0.50-1.62);P: 0.7280.93 (0.69-1.24);P: 0.6110.93 (0.52-1.64);P: 0.799≥59312/268178/19940/351.000.75 (0.57-0.98);P: 0.0361.08 (0.65-1.78);P: 0.7780.79 (0.61-1.03);P: 0.0791.21 (0.74-1.98);P: 0.450Smoking statusNever355/371220/28549/551.000.79 (0.62-1.01);P: 0.0600.91 (0.59-1.40);P: 0.6510.81 (0.65-1.02);P: 0.0741.00 (0.65-1.52);P: 0.980Ever211/100131/7622/81.000.86 (0.59-1.26);P: 0.4441.32 (0.56-3.11);P: 0.5250.91 (0.63-1.31);P: 0.6031.40 (0.60-3.26);P: 0.433Alcohol consumptionNever443/408280/33255/591.000.76 (0.61-0.94);P: 0.0130.87 (0.58-1.31);P: 0.5090.77 (0.63-0.95);P: 0.0160.98 (0.66-1.46);P: 0.911Ever123/6371/2916/41.001.22 (0.70-2.14);P: 0.4792.55 (0.75-8.60);P: 0.1321.36 (0.80-2.32);P: 0.2562.37 (0.71-7.86);P: 0.159BMI (kg/m2)<24368/257245/20147/351.000.89 (0.68-1.15);P: 0.3611.06 (0.65-1.73);P: 0.8280.91 (0.71-1.17);P: 0.4581.11 (0.69-1.80);P: 0.667≥24198/214106/16024/281.000.69 (0.50-0.97);P: 0.0300.88 (0.48-1.62);P: 0.6770.72 (0.53-0.99);P: 0.0411.01 (0.56-1.84);P: 0.973Notes:athe genotyping was successful in 1,003 (98.50%) NSCLC cases, and 901 (99.33%) controls for BTLA rs1982809 G>A. bAdjusted for age, sex, smoking status, alcohol consumption, and BMI (besides stratified factors accordingly) in a logistic regression model. Abbreviations: BMI: body mass index; NSCLC: non-small-cell lung cancer.Table 6
Stratified analyses betweenBTLA rs2171513 G>A polymorphism and NSCLC risk by sex, age, smoking status, alcohol consumption, and BMI.
VariableBTLA rs2171513 G>A (case/control)aAdjusted ORb (95% CI); PGGGAAAGGGA vs. GGAA vs. GGGA/AA vs. GGAA vs. (GG/GA)SexMale335/361165/16515/241.001.15 (0.86-1.53);P: 0.3460.70 (0.34-1.43);P: 0.3251.09 (0.83-1.44);P: 0.5450.67 (0.33-1.36);P: 0.266Female319/221132/11124/131.000.84 (0.61-1.15);P: 0.2731.32 (0.65-2.69);P: 0.4410.89 (0.66-1.20);P: 0.4451.40 (0.69-2.83);P: 0.350Age<59305/265133/11421/141.001.02 (0.73-1.40);P: 0.9301.58 (0.73-3.42);P: 0.2441.06 (0.78-1.46);P: 0.6721.58 (0.73-3.39);P: 0.244≥59349/317164/16218/231.000.98 (0.74-1.30);P: 0.9060.67 (0.35-1.30);P: 0.2400.94 (0.72-1.23);P: 0.6590.68 (0.35-1.30);P: 0.242Smoking statusNever408/456188/22729/281.000.97 (0.76-1.24);P: 0.8031.18 (0.67-2.08);P: 0.5710.99 (0.78-1.26);P: 0.9481.19 (0.68-2.09);P: 0.543Ever246/126109/4910/91.001.08 (0.72-1.63);P: 0.6980.58 (0.23-1.48);P: 0.2541.01 (0.68-1.48);P: 0.9800.57 (0.22-1.44);P: 0.232Alcohol consumptionNever520/520226/24534/341.000.98 (0.78-1.23);P: 0.8290.99 (0.59-1.66);P: 0.9670.98 (0.79-1.22);P: 0.8341.00 (0.60-1.66);P: 0.990Ever134/6271/315/31.001.13 (0.65-1.95);P: 0.6700.79 (0.17-3.69);P: 0.7671.10 (0.64-1.87);P: 0.7350.76 (0.17-3.50);P: 0.725BMI (kg/m2)<24441/326194/14726/201.001.05 (0.80-1.38);P: 0.7110.91 (0.49-1.69);P: 0.7611.03 (0.80-1.34);P: 0.8030.89 (0.48-1.66);P: 0.724≥24213/256103/12913/171.000.93 (0.66-1.30);P: 0.6561.09 (0.49-2.40);P: 0.8390.94 (0.68-1.30);P: 0.7231.11 (0.51-2.44);P: 0.791Notes:athe genotyping was successful in 1,003 (98.70%) NSCLC cases, and 901 (99.33%) controls for BTLA rs2171513 G>A. bAdjusted for age, sex, smoking status, alcohol consumption, and BMI (besides stratified factors accordingly) in a logistic regression model. Abbreviations: BMI: body mass index; NSCLC: non-small-cell lung cancer.Table 7
Stratified analyses betweenBTLA rs3112270 A>G polymorphism and NSCLC risk by sex, age, smoking status, alcohol consumption, and BMI.
VariableBTLA rs3112270 A>G (case/control)aAdjusted ORb (95% CI); PAAGAGGAAAG vs. AAGG vs. AAAG/GG vs. AAGG vs. (AA/AG)SexMale277/270199/23737/431.000.89 (0.68-1.17);P: 0.4130.97 (0.58-1.62);P: 0.9020.90 (0.69-1.18);P: 0.4471.02 (0.62-1.68);P: 0.940Female250/165193/15131/291.000.84 (0.62-1.13);P: 0.2570.72 (0.41-1.26);P: 0.2500.82 (0.62-1.09);P: 0.1790.78 (0.45-1.34);P: 0.367Age<59243/195184/16331/351.000.92 (0.68-1.26);P: 0.6150.81 (0.47-1.43);P: 0.4720.91 (0.68-1.21);P: 0.5070.84 (0.49-1.45);P: 0.539≥59284/240208/22537/371.000.82 (0.63-1.07);P: 0.1400.84 (0.50-1.39);P: 0.4930.82 (0.63-1.06);P: 0.1310.92 (0.56-1.51);P: 0.733Smoking statusNever330/339246/31647/561.000.81 (0.64-1.03);P: 0.0810.87 (0.56-1.35);P: 0.5280.82 (0.65-1.03);P: 0.0840.96 (0.63-1.46);P: 0.836Ever197/96146/7221/161.001.01 (0.69-1.47);P: 0.9800.67 (0.33-1.37);P: 0.2780.94 (0.66-1.36);P: 0.7580.67 (0.34-1.35);P: 0.263Alcohol consumptionNever410/383312/35056/661.000.85 (0.69-1.06);P: 0.1560.78 (0.52-1.17);P: 0.2300.84 (0.69-1.04);P: 0.1060.84 (0.57-1.24);P: 0.380Ever117/5280/3812/61.000.89 (0.52-1.52);P: 0.6661.14 (0.38-3.46);P: 0.8180.92 (0.55-1.54);P: 0.7471.20 (0.41-3.55);P: 0.742BMI (kg/m2)<24355/233255/21749/431.000.79 (0.61-1.02);P: 0.0700.81 (0.51-1.29);P: 0.3690.79 (0.62-1.01);P: 0.0630.90 (0.57-1.41);P: 0.646≥24172/202137/17119/291.000.96 (0.70-1.32);P: 0.7870.82 (0.34-1.55);P: 0.5430.94 (0.69-1.27);P: 0.6770.84 (0.45-1.56);P: 0.574Notes:athe genotyping was successful in 1,003 (98.40%) NSCLC cases, and 901 (99.33%) controls for BTLA rs3112270 A>G; badjusted for age, sex, smoking status, alcohol consumption, and BMI (besides stratified factors accordingly) in a logistic regression model. Abbreviations: BMI: body mass index; NSCLC: non-small-cell lung cancer.Table 8
Stratified analyses betweenBTLA rs16859629 T>C polymorphism and NSCLC risk by sex, age, smoking status, and alcohol consumption.
VariableBTLA rs16859629 T>C (case/control)aAdjusted ORb (95% CI); PTTTCCCTTTC vs. TTCC vs. TTTC/CC vs. TTCC vs. (TT/TC)SexMale435/45970/813/11.000.91 (0.63-1.33);P: 0.6365.89 (0.56-61.95);P: 0.1400.96 (0.66-1.39);P: 0.8155.97 (0.57-62.73);P: 0.137Female400/28761/514/41.000.85 (0.57-1.29);P: 0.4550.70 (0.17-2.89);P: 0.6180.84 (0.57-1.26);P: 0.4020.71 (0.17-2.95);P: 0.640Age<59387/32558/584/31.000.80 (0.52-1.22);P: 0.3031.17 (0.24-5.76);P: 0.8480.82 (0.54-1.24);P: 0.3421.21 (0.45-5.93);P: 0.819≥59448/42173/743/21.000.96 (0.67-1.39);P: 0.8431.56 (0.24-10.30);P: 0.6430.98 (0.68-1.41);P: 0.9081.57 (0.24-10.35);P: 0.639Smoking statusNever527/59380/1076/51.000.85 (0.61-1.18);P: 0.3251.21 (0.35-4.19);P: 0.7660.87 (0.63-1.19);P: 0.3761.24 (0.36-4.29);P: 0.738Ever308/15351/251/01.001.00 (0.59-1.70);P: 0.988-1.02 (0.60-1.72);P: 0.945-Alcohol consumptionNever659/665102/1176/51.000.89 (0.66-1.21);P: 0.4571.24 (0.36-4.31);P: 0.7340.91 (0.68-1.22);P: 0.5131.26 (0.36-4.37);P: 0.715Ever176/8129/151/01.000.86 (0.42-1.75);P: 0.681-0.89 (0.44-1.80);P: 0.735-BMI (kg/m2)<24557/41188/714/21.000.92 (0.65-1.32);P: 0.6581.81 (0.30-10.83);P: 0.5140.95 (0.67-1.34);P: 0.7521.84 (0.31-10.95);P: 0.505≥24278/33543/613/31.000.83 (0.53-1.29);P: 0.4111.08 (0.20-5.86);P: 0.9300.84 (0.55-1.30);P: 0.4371.11 (0.20-6.00);P: 0.906Notes:athe genotyping was successful in 1,003 (97.01%) NSCLC cases, and 901 (98.00%) controls for BTLA rs16859629 T>C; badjusted for age, sex, smoking status, alcohol consumption, and BMI (besides stratified factors accordingly) in a logistic regression model. Abbreviations: BMI: body mass index; NSCLC: non-small-cell lung cancer.


### 3.4. SNP Haplotypes

The SHESIS software online (http://analysis.bio-x.cn/myAnalysis.php) was used to perform the haplotype analysis. If the probability of BTLA haploid in the case-control study is less than 0.03, it is classified as others. In the end, six subgroups were built. The results are shown in Table 9 and Figure 1. No significant linkage disequilibrium (LD) relationship was observed among the BTLA rs16859629, rs1982809, rs2171513, and rs3112270 (r2<0.8). It was found that compared to BTLA Trs16859629Grs1982809Grs2171513Ars3112270 haplotype, the haplotype BTLA Trs16859629Ars1982809Grs2171513Grs3112270 significantly reduced the susceptibility to NSCLC (OR=0.66, 95%CI=0.659‐0.930, and P=0.005).Table 9
BTLA haplotype analysis.
HaplotypesCase (n=2006)Control (n=1802)Crude OR (95% CI)Pn%n%T G G A101552.4085448.401.00T A G G29515.3033018.700.661 (0.659-0.930)0.005T G A A20710.7018910.700.843 (0.810-1.229)0.982T G A G1005.201025.800.757 (0.674-1.189)0.446C G G A804.10925.200.662 (0.577-1.066)0.119T A G A633.30472.701.034 (0.836-1.794)0.298T G G G492.50482.700.919 (0.614-1.374)0.680T A A A422.20482.700.674 (0.525-1.213)0.291C A G G472.40372.100.972 (0.745-1.779)0.526Others371.90170.903.595 (1.502-12.065)<0.003The haplotypes were constructed in the sequence of BTLA rs16859629 T>C, rs1982809 G>A, rs2171513 G>A, and rs3112270 A>G.Figure 1
There was no significant linkage disequilibrium betweenBTLA polymorphisms (r2<0.8).


## 3.1. Baseline Characteristics

A total of 1,003 NSCLC cases and 901 controls were recruited in this study. The risk factors that may influence the development of NSCLC are listed in Table2. Although a good match was observed between the age of the NSCLC patients and the control group (P=0.139), significant differences were observed in sex, smoking, alcohol consumption, and BMI between the two groups (P<0.001). The genotyping rate of BTLA rs2171513, rs3112270, rs1982809, and rs16859629 was higher than 95% (99.00%, 98.84%, 98.90%, and 97.48%, respectively) as shown in Table 1. Minor allele frequencies (MAFs) in the control were similar to those of the Chinese populations, and the genotype frequency distributions were in accordance with the HWE.Table 2
Distribution of selected demographic variables and risk factors in NSCLC cases and controls.
VariableCases (n=1,003)Controls (n=901)Pan%n%Age (years)58.76±9.9259.43±9.670.139Age (years)0.292<5946546.3639643.95≥5953853.6450556.05Sex<0.001Male52051.8455261.27Female48348.1634938.73Tobacco use<0.001Never63463.2171779.58Ever36936.7918420.42Alcohol use<0.001Never78978.6680489.23Ever21421.349710.77BMI (kg/m2)<0.001<2467267.0049755.16≥2433133.0040444.84Type of NSCLCSCC14514.46Non-SCC85885.54Notes:atwo-sided χ2 test and Student’s t-test. Abbreviations: BMI: body mass index; NSCLC: non-small-cell lung cancer; SCC: squamous cell carcinoma.


## 3.2. Association betweenBTLA Polymorphisms and Susceptibility to NSCLC

The genotype distribution ofBTLA rs1982809, rs16859629, rs2171513, and rs3112270 SNPs is shown in Table 3. It was found that GG+AG genotype of BTLA rs3112270 polymorphism may be a protective factor for NSCLC compared to the AA genotype (GG+AG: OR=0.83, 95%CI=0.69‐0.99, and P=0.038). Meanwhile, compared to the BTLA rs1982809 GG genotype, the BTLA rs1982809 GA and AA+GA genotypes are also associated with the development of NSCLC (GA: OR=0.81, 95%CI=0.67‐0.98, and P=0.030; AA+GA: OR=0.83, 95%CI=0.69‐0.99, and P=0.042). Adjusting the risk factors (age, sex, smoking, drinking, and BMI status), the genotypes of BTLA rs3112270 polymorphism were not closely correlated with NSCLC susceptibility, whereas the GA genotype of BTLA rs1982809 SNPs still decreased the susceptibility of NSCLC patients (GA: adjustedOR=0.81, 95%CI=0.66‐0.99, and P=0.043; Table 4).Table 3
The frequencies ofBTLA tagging polymorphisms in different NSCLC subgroups.
GenotypeNSCLC (n=1,003)SCC (n=145)Non-SCC (n=858)Controls (n=901)n%n%n%n%rs2171513 G>AGG65466.0610673.6154864.7858265.03GA29730.003524.3126230.9727630.84AA393.9432.08364.26374.13A allele37518.944114.2433419.7435019.55rs3112270 A>GAA52753.397451.3945353.7443548.60AG39239.726142.3633139.2638843.35GG686.8996.25597.00728.04G allele52826.757927.4344926.6353229.72rs1982809 G>AGG56657.297753.4748957.9447152.63GA35135.535437.5029735.1936140.34AA717.19139.03586.87637.04A allele49324.958027.7841324.4748727.21rs16859629 T>CTT83585.8211582.1472086.4374684.48TC13113.462316.4310812.9713214.95CC70.7221.4350.6050.57C allele1457.45279.641187.081428.04Table 4
Logistic regression analyses of association ofBTLA tagging polymorphisms with risk of NSCLC.
GenotypeOverall NSCLC cases (n=1,003) vs. controls (n=901)SCC (n=145) vs. controls (n=901)Non-SCC (n=858) vs. controls (n=901)Crude OR (95% CI)PAdjusted ORa (95% CI)PCrude OR (95% CI)PAdjusted ORa (95% CI)PCrude OR (95% CI)PAdjusted ORa (95% CI)Prs2171513 G>AGA vs. GG0.96 (0.79-1.17)0.6691.00 (0.81-1.24)0.9710.70 (0.46-1.05)0.0820.83 (0.51-1.32)0.4261.01 (0.82-1.24)0.9381.03 (0.83-1.28)0.760AA vs. GG0.94 (0.59-1.49)0.7870.97 (0.60-1.59)0.9080.45 (0.14-1.47)0.1840.32 (0.09-1.18)0.0861.03 (0.64-1.66)0.8921.06 (0.65-1.74)0.816AA/GA vs. GG0.96 (0.79-1.16)0.6371.00 (0.82-1.22)1.000.67 (0.45-0.99)0.0440.74 (0.47-1.17)0.1951.01 (0.83-1.23)0.9121.04 (0.84-1.28)0.728AA vs. GG/GA0.95 (0.60-1.51)0.8300.97 (0.60-1.58)0.9040.49 (0.15-1.62)0.2450.34 (0.09-1.23)0.0991.03 (0.65-1.65)0.9001.05 (0.64-1.72)0.847rs3112270 A>GAG vs. AA0.83 (0.69-1.01)0.0600.86 (0.70-1.05)0.1250.92 (0.64-1.33)0.6721.10 (0.72-1.69)0.6670.82 (0.67-1.00)0.0470.82 (0.67-1.01)0.064GG vs. AA0.78 (0.55-1.11)0.1690.81 (0.56-1.18)0.2750.74 (0.35-1.53)0.4120.80 (0.35-1.85)0.6030.79 (0.54-1.14)0.2020.81 (0.55-1.20)0.293GG/AG vs. AA0.83 (0.69-0.99)0.0380.85 (0.70-1.03)0.0920.90 (0.63-1.27)0.5351.05 (0.70-1.58)0.8180.81 (0.67-0.98)0.0330.82 (0.67-1.00)0.051GG vs. AA/AG0.85 (0.60-1.19)0.3400.87 (0.61-1.25)0.4560.76 (0.37-1.56)0.4580.77 (0.34-1.73)0.5220.86 (0.60-1.23)0.4110.89 (0.61-1.29)0.530rs1982809 G>AGA vs. GG0.81 (0.67-0.98)0.0300.81 (0.66-0.99)0.0430.92 (0.63-1.33)0.6421.05 (0.68-1.62)0.8330.79 (0.65-0.97)0.0220.79 (0.64-0.97)0.026AA vs. GG0.94 (0.65-1.35)0.7270.99 (0.68-1.45)0.9601.26 (0.66-2.40)0.4781.83 (0.84-3.97)0.1300.89 (0.61-1.30)0.5340.93 (0.63-1.39)0.724AA/GA vs. GG0.83 (0.69-0.99)0.0420.84 (0.69-1.02)0.0710.97 (0.68-1.38)0.8501.15 (0.76-1.73)0.5220.81 (0.67-0.98)0.0260.81 (0.66-0.99)0.037AA vs. GG/GA1.02 (0.72-1.45)0.9011.08 (0.74-1.57)0.6901.31 (0.70-2.45)0.3961.79 (0.84-3.80)0.1310.98 (0.67-1.41)0.8911.03 (0.70-1.51)0.900rs16859629 T>CTC vs. TT0.89 (0.68-1.15)0.3670.89 (0.68-1.18)0.4121.13 (0.70-1.84)0.6201.19 (0.68-2.11)0.5440.85 (0.64-1.12)0.2380.86 (0.65-1.15)0.307CC vs. TT1.25 (0.40-3.96)0.7031.38 (0.41-4.58)0.6022.60 (0.50-13.53)0.2589.85 (1.37-71.03)0.0231.04 (0.30-3.59)0.9551.09 (0.30-4.01)0.898CC/TC vs. TT0.90 (0.70-1.16)0.4190.91 (0.69-1.19)0.4861.18 (0.74-1.89)0.4811.31 (0.75-2.28)0.3380.86 (0.65-1.12)0.2530.87 (0.65-1.15)0.330CC vs. TT/TC1.27 (0.40-4.02)0.6821.40 (0.42-4.66)0.5842.55 (0.49-13.25)0.2679.55 (1.32-68.66)0.0251.06 (0.31-3.68)0.9261.11 (0.30-4.09)0.873Notes:aadjusted for age, sex, smoking status, alcohol use, and BMI status in a logistic regression model.Collectively, the results suggest that the genotypes inBTLA rs2171513 and rs16859629 mutations may be associated with the development of NSCLC.


## 3.3. Association ofBTLA Polymorphisms with Susceptibility in Different NSCLC Subgroups

The results showed that AA genotype ofBTLA rs2171513 polymorphism might be considered as a susceptibility factor for the development of squamous cell carcinoma (SCC) (AA/GA: OR=0.67, 95%CI=0.45‐0.99, and P=0.044) (Table 4). Adjusting the risk factors (age, sex, smoking, drinking, and BMI status), the BTLA rs2171513 polymorphism was not relevant to the occurrence of SCC. Nonetheless, using TT and TT+TC genotypes as references, the results showed that the CC genotype of BTLA rs16859629 polymorphisms may promote the development of SCC (CC vs. TT: adjustedOR=9.85, 95%CI=1.37‐71.03, and P=0.023; CC vs. TT+TC: adjustedOR=9.55, 95%CI=1.32‐68.66, and P=0.025).In nonsquamous cell carcinoma (non-SCC), using AA genotype as reference, it was found that GG and GA+GG genotypes ofBTLA rs3112270 polymorphisms may decrease the incidence of non-SCC (AG: OR=0.82, 95%CI=0.67‐1.00, and P=0.047; GG+AG: OR=0.81, 95%CI=0.67‐0.98, and P=0.033). Meanwhile, it was also found that the GA and GA+AA genotypes of BTLA rs1982809 polymorphism also decreased the occurrence of non-SCC with reference to GG genotype (GA vs. GG: OR=0.79, 95%CI=0.65‐0.97, and P=0.022; AA/GA vs. GG: OR=0.81, 95%CI=0.67‐0.98, and P=0.026). Adjusting the related risk factors, it is concluded that the BTLA rs3112270 polymorphism did not exhibit the tendency to change the risk to non-SCC. However, BTLA rs1982809 polymorphism was a protective factor of the susceptibility to non-SCC (GA: adjustedOR=0.79, 95%CI=0.64‐0.97, and P=0.026; AA+GA: adjustedOR=0.81, 95%CI=0.66‐0.99, and P=0.037).The genotype frequencies ofBTLA rs1982809 polymorphism in the subgroup analyses are depicted in Table 5. In ≥59-year subgroup, it was found that the variants of BTLA rs1982809 decreased the incidence of NSCLC (GA vs. GG: P=0.036). In never alcohol and BMI≥24 kg/m2 subgroup, it was found that similar genotype variants of BTLA rs1982809 might be a protective factor of NSCLC (never smoking subgroup: GA vs. GG: P=0.013; AA/GA vs. GG: P=0.016; BMI≥24 kg/m2: GA vs. GG: P=0.030; AA/GA vs. GG: P=0.041). Additionally, it was found that BTLA rs2171513, rs3112270, and rs16859629 polymorphisms were not associated with the morbidity of NSCLC in subgroups (Tables 6–8).Table 5
Stratified analyses betweenBTLA rs1982809 G>A polymorphism and NSCLC risk by sex, age, smoking status, alcohol consumption, and BMI.
VariableBTLA rs1982809 G>A (case/control)aAdjusted ORb (95% CI); PGGGAAAGGGA vs. GGAA vs. GGGA/AA vs. GGAA vs. (GG/GA)SexMale299/289177/22837/331.000.79 (0.60-1.05);P: 0.1001.32 (0.77-2.27);P: 0.3140.86 (0.66-1.11);P: 0.2461.45 (0.86-2.46);P: 0.167Female267/182174/13334/301.000.87 (0.64-1.18);P: 0.3660.77 (0.45-1.33);P: 0.3520.85 (0.64-1.14);P: 0.2740.82 (0.49-1.38);P: 0.457Age<59254/203173/16231/281.000.93 (0.69-1.27);P: 0.6510.90 (0.50-1.62);P: 0.7280.93 (0.69-1.24);P: 0.6110.93 (0.52-1.64);P: 0.799≥59312/268178/19940/351.000.75 (0.57-0.98);P: 0.0361.08 (0.65-1.78);P: 0.7780.79 (0.61-1.03);P: 0.0791.21 (0.74-1.98);P: 0.450Smoking statusNever355/371220/28549/551.000.79 (0.62-1.01);P: 0.0600.91 (0.59-1.40);P: 0.6510.81 (0.65-1.02);P: 0.0741.00 (0.65-1.52);P: 0.980Ever211/100131/7622/81.000.86 (0.59-1.26);P: 0.4441.32 (0.56-3.11);P: 0.5250.91 (0.63-1.31);P: 0.6031.40 (0.60-3.26);P: 0.433Alcohol consumptionNever443/408280/33255/591.000.76 (0.61-0.94);P: 0.0130.87 (0.58-1.31);P: 0.5090.77 (0.63-0.95);P: 0.0160.98 (0.66-1.46);P: 0.911Ever123/6371/2916/41.001.22 (0.70-2.14);P: 0.4792.55 (0.75-8.60);P: 0.1321.36 (0.80-2.32);P: 0.2562.37 (0.71-7.86);P: 0.159BMI (kg/m2)<24368/257245/20147/351.000.89 (0.68-1.15);P: 0.3611.06 (0.65-1.73);P: 0.8280.91 (0.71-1.17);P: 0.4581.11 (0.69-1.80);P: 0.667≥24198/214106/16024/281.000.69 (0.50-0.97);P: 0.0300.88 (0.48-1.62);P: 0.6770.72 (0.53-0.99);P: 0.0411.01 (0.56-1.84);P: 0.973Notes:athe genotyping was successful in 1,003 (98.50%) NSCLC cases, and 901 (99.33%) controls for BTLA rs1982809 G>A. bAdjusted for age, sex, smoking status, alcohol consumption, and BMI (besides stratified factors accordingly) in a logistic regression model. Abbreviations: BMI: body mass index; NSCLC: non-small-cell lung cancer.Table 6
Stratified analyses betweenBTLA rs2171513 G>A polymorphism and NSCLC risk by sex, age, smoking status, alcohol consumption, and BMI.
VariableBTLA rs2171513 G>A (case/control)aAdjusted ORb (95% CI); PGGGAAAGGGA vs. GGAA vs. GGGA/AA vs. GGAA vs. (GG/GA)SexMale335/361165/16515/241.001.15 (0.86-1.53);P: 0.3460.70 (0.34-1.43);P: 0.3251.09 (0.83-1.44);P: 0.5450.67 (0.33-1.36);P: 0.266Female319/221132/11124/131.000.84 (0.61-1.15);P: 0.2731.32 (0.65-2.69);P: 0.4410.89 (0.66-1.20);P: 0.4451.40 (0.69-2.83);P: 0.350Age<59305/265133/11421/141.001.02 (0.73-1.40);P: 0.9301.58 (0.73-3.42);P: 0.2441.06 (0.78-1.46);P: 0.6721.58 (0.73-3.39);P: 0.244≥59349/317164/16218/231.000.98 (0.74-1.30);P: 0.9060.67 (0.35-1.30);P: 0.2400.94 (0.72-1.23);P: 0.6590.68 (0.35-1.30);P: 0.242Smoking statusNever408/456188/22729/281.000.97 (0.76-1.24);P: 0.8031.18 (0.67-2.08);P: 0.5710.99 (0.78-1.26);P: 0.9481.19 (0.68-2.09);P: 0.543Ever246/126109/4910/91.001.08 (0.72-1.63);P: 0.6980.58 (0.23-1.48);P: 0.2541.01 (0.68-1.48);P: 0.9800.57 (0.22-1.44);P: 0.232Alcohol consumptionNever520/520226/24534/341.000.98 (0.78-1.23);P: 0.8290.99 (0.59-1.66);P: 0.9670.98 (0.79-1.22);P: 0.8341.00 (0.60-1.66);P: 0.990Ever134/6271/315/31.001.13 (0.65-1.95);P: 0.6700.79 (0.17-3.69);P: 0.7671.10 (0.64-1.87);P: 0.7350.76 (0.17-3.50);P: 0.725BMI (kg/m2)<24441/326194/14726/201.001.05 (0.80-1.38);P: 0.7110.91 (0.49-1.69);P: 0.7611.03 (0.80-1.34);P: 0.8030.89 (0.48-1.66);P: 0.724≥24213/256103/12913/171.000.93 (0.66-1.30);P: 0.6561.09 (0.49-2.40);P: 0.8390.94 (0.68-1.30);P: 0.7231.11 (0.51-2.44);P: 0.791Notes:athe genotyping was successful in 1,003 (98.70%) NSCLC cases, and 901 (99.33%) controls for BTLA rs2171513 G>A. bAdjusted for age, sex, smoking status, alcohol consumption, and BMI (besides stratified factors accordingly) in a logistic regression model. Abbreviations: BMI: body mass index; NSCLC: non-small-cell lung cancer.Table 7
Stratified analyses betweenBTLA rs3112270 A>G polymorphism and NSCLC risk by sex, age, smoking status, alcohol consumption, and BMI.
VariableBTLA rs3112270 A>G (case/control)aAdjusted ORb (95% CI); PAAGAGGAAAG vs. AAGG vs. AAAG/GG vs. AAGG vs. (AA/AG)SexMale277/270199/23737/431.000.89 (0.68-1.17);P: 0.4130.97 (0.58-1.62);P: 0.9020.90 (0.69-1.18);P: 0.4471.02 (0.62-1.68);P: 0.940Female250/165193/15131/291.000.84 (0.62-1.13);P: 0.2570.72 (0.41-1.26);P: 0.2500.82 (0.62-1.09);P: 0.1790.78 (0.45-1.34);P: 0.367Age<59243/195184/16331/351.000.92 (0.68-1.26);P: 0.6150.81 (0.47-1.43);P: 0.4720.91 (0.68-1.21);P: 0.5070.84 (0.49-1.45);P: 0.539≥59284/240208/22537/371.000.82 (0.63-1.07);P: 0.1400.84 (0.50-1.39);P: 0.4930.82 (0.63-1.06);P: 0.1310.92 (0.56-1.51);P: 0.733Smoking statusNever330/339246/31647/561.000.81 (0.64-1.03);P: 0.0810.87 (0.56-1.35);P: 0.5280.82 (0.65-1.03);P: 0.0840.96 (0.63-1.46);P: 0.836Ever197/96146/7221/161.001.01 (0.69-1.47);P: 0.9800.67 (0.33-1.37);P: 0.2780.94 (0.66-1.36);P: 0.7580.67 (0.34-1.35);P: 0.263Alcohol consumptionNever410/383312/35056/661.000.85 (0.69-1.06);P: 0.1560.78 (0.52-1.17);P: 0.2300.84 (0.69-1.04);P: 0.1060.84 (0.57-1.24);P: 0.380Ever117/5280/3812/61.000.89 (0.52-1.52);P: 0.6661.14 (0.38-3.46);P: 0.8180.92 (0.55-1.54);P: 0.7471.20 (0.41-3.55);P: 0.742BMI (kg/m2)<24355/233255/21749/431.000.79 (0.61-1.02);P: 0.0700.81 (0.51-1.29);P: 0.3690.79 (0.62-1.01);P: 0.0630.90 (0.57-1.41);P: 0.646≥24172/202137/17119/291.000.96 (0.70-1.32);P: 0.7870.82 (0.34-1.55);P: 0.5430.94 (0.69-1.27);P: 0.6770.84 (0.45-1.56);P: 0.574Notes:athe genotyping was successful in 1,003 (98.40%) NSCLC cases, and 901 (99.33%) controls for BTLA rs3112270 A>G; badjusted for age, sex, smoking status, alcohol consumption, and BMI (besides stratified factors accordingly) in a logistic regression model. Abbreviations: BMI: body mass index; NSCLC: non-small-cell lung cancer.Table 8
Stratified analyses betweenBTLA rs16859629 T>C polymorphism and NSCLC risk by sex, age, smoking status, and alcohol consumption.
VariableBTLA rs16859629 T>C (case/control)aAdjusted ORb (95% CI); PTTTCCCTTTC vs. TTCC vs. TTTC/CC vs. TTCC vs. (TT/TC)SexMale435/45970/813/11.000.91 (0.63-1.33);P: 0.6365.89 (0.56-61.95);P: 0.1400.96 (0.66-1.39);P: 0.8155.97 (0.57-62.73);P: 0.137Female400/28761/514/41.000.85 (0.57-1.29);P: 0.4550.70 (0.17-2.89);P: 0.6180.84 (0.57-1.26);P: 0.4020.71 (0.17-2.95);P: 0.640Age<59387/32558/584/31.000.80 (0.52-1.22);P: 0.3031.17 (0.24-5.76);P: 0.8480.82 (0.54-1.24);P: 0.3421.21 (0.45-5.93);P: 0.819≥59448/42173/743/21.000.96 (0.67-1.39);P: 0.8431.56 (0.24-10.30);P: 0.6430.98 (0.68-1.41);P: 0.9081.57 (0.24-10.35);P: 0.639Smoking statusNever527/59380/1076/51.000.85 (0.61-1.18);P: 0.3251.21 (0.35-4.19);P: 0.7660.87 (0.63-1.19);P: 0.3761.24 (0.36-4.29);P: 0.738Ever308/15351/251/01.001.00 (0.59-1.70);P: 0.988-1.02 (0.60-1.72);P: 0.945-Alcohol consumptionNever659/665102/1176/51.000.89 (0.66-1.21);P: 0.4571.24 (0.36-4.31);P: 0.7340.91 (0.68-1.22);P: 0.5131.26 (0.36-4.37);P: 0.715Ever176/8129/151/01.000.86 (0.42-1.75);P: 0.681-0.89 (0.44-1.80);P: 0.735-BMI (kg/m2)<24557/41188/714/21.000.92 (0.65-1.32);P: 0.6581.81 (0.30-10.83);P: 0.5140.95 (0.67-1.34);P: 0.7521.84 (0.31-10.95);P: 0.505≥24278/33543/613/31.000.83 (0.53-1.29);P: 0.4111.08 (0.20-5.86);P: 0.9300.84 (0.55-1.30);P: 0.4371.11 (0.20-6.00);P: 0.906Notes:athe genotyping was successful in 1,003 (97.01%) NSCLC cases, and 901 (98.00%) controls for BTLA rs16859629 T>C; badjusted for age, sex, smoking status, alcohol consumption, and BMI (besides stratified factors accordingly) in a logistic regression model. Abbreviations: BMI: body mass index; NSCLC: non-small-cell lung cancer.


## 3.4. SNP Haplotypes

The SHESIS software online (http://analysis.bio-x.cn/myAnalysis.php) was used to perform the haplotype analysis. If the probability of BTLA haploid in the case-control study is less than 0.03, it is classified as others. In the end, six subgroups were built. The results are shown in Table 9 and Figure 1. No significant linkage disequilibrium (LD) relationship was observed among the BTLA rs16859629, rs1982809, rs2171513, and rs3112270 (r2<0.8). It was found that compared to BTLA Trs16859629Grs1982809Grs2171513Ars3112270 haplotype, the haplotype BTLA Trs16859629Ars1982809Grs2171513Grs3112270 significantly reduced the susceptibility to NSCLC (OR=0.66, 95%CI=0.659‐0.930, and P=0.005).Table 9
BTLA haplotype analysis.
HaplotypesCase (n=2006)Control (n=1802)Crude OR (95% CI)Pn%n%T G G A101552.4085448.401.00T A G G29515.3033018.700.661 (0.659-0.930)0.005T G A A20710.7018910.700.843 (0.810-1.229)0.982T G A G1005.201025.800.757 (0.674-1.189)0.446C G G A804.10925.200.662 (0.577-1.066)0.119T A G A633.30472.701.034 (0.836-1.794)0.298T G G G492.50482.700.919 (0.614-1.374)0.680T A A A422.20482.700.674 (0.525-1.213)0.291C A G G472.40372.100.972 (0.745-1.779)0.526Others371.90170.903.595 (1.502-12.065)<0.003The haplotypes were constructed in the sequence of BTLA rs16859629 T>C, rs1982809 G>A, rs2171513 G>A, and rs3112270 A>G.Figure 1
There was no significant linkage disequilibrium betweenBTLA polymorphisms (r2<0.8).


## 4. Discussion

The pathogenesis of LC is overly complex. It is believed that LC might be a disease driven by multiple genes [22]. Epidemiological studies have proved the correlation of the etiology of LC and the gene-environment interaction [23, 24]. Recently, immune checkpoint inhibitors have attracted remarkable attention in cancer treatment. For instance, PD-1 and CTLA-4 immunity inhibitors have achieved certain efficacy in the treatment of advanced LC [25]. Nonetheless, the cause of LC is still largely unclear. A previous study had reported that overexpression of BTLA could predict a poor prognosis in NSCLC patients [12].In the present case-control study, the potential relationship between theBTLA rs1982809 G>A, rs16859629 T>C, rs2171513 G>A, and rs3112270 A>G SNPs and susceptibility to NSCLC was explored [23, 26]. It was found that BTLA rs1982809 polymorphism might reduce the risk of overall NSCLC. But candidate locus of BTLA rs2171513, rs3112270, and rs16859629 SNPs could not affect the susceptibility to NSCLC. In NSCLC subgroup analysis, BTLA rs16859629 SNPs could increase the risk of SCC. However, BTLA rs1982809 SNPs might reduce susceptibility to non-SCC. In addition, BTLA rs1982809 SNPs could reduce the susceptibility to NSCLC in the BMI≥24 kg/m2, ≥59 year, and never drinking subgroups. To the best of our knowledge, the present study for the first time reports the relationship between BTLA SNPs and NSCLC susceptibility in the Chinese population.BTLA rs1982809, as a locus in the 3′-untranslated region (UTR), has been reported to exhibit an association with the development of some malignancies. The 3′-UTR plays a crucial role in the regulation of mRNA expression [27, 28]. Karabon et al. reported that BTLA could exert its effects on T-lymphocytes by affecting mRNA expression levels due to T to C substitutions of rs1982809 [16]. Additionally, it was also reported that BTLA rs1982809 might act as a potential biomarker in predicting the multiple organ dysfunction syndrome. In another study, rs1982809 polymorphism of the BTLA was found to be associated with the risk of kidney cancer [17]. Similarly, in our previous study, we report that the gene variation in BTLA rs1982809 increased the susceptibility to EGJA in ever smoking subjects [18]. In contrary, Cao et al. concluded that the distribution of genotype in BTLA rs1982809 was not different from the ESCC and the control group [19]. In short, the previous results were ambiguous, even contradictory. Considering that genetic variations played different roles in different cancers, 1,904 participants were enrolled to conduct a more precise evaluation. In this study, we showed that the G to A changes of BTLA rs1982809 genotype reduced the overall risk of NSCLC, especially the non-SCC, BMI≥24 kg/m2, ≥59 year, and never drinking subgroups. These facts indicated that BTLA rs1982809 polymorphism could play a critical role in the susceptibility to NSCLC. However, our findings should be interpreted with cautions. Studies with larger sample sizes are required to further validate the effects of this locus on NSCLC.TheBTLA rs16859629 SNP, which is located in the intron variant sequence, plays an important role in alternative splicing [29]. It has been reported that SNP located in the intron regions may affect the susceptibility to several human diseases [30–32]. When adjusted for including 5 covariates by regression analysis, it was found that BTLA rs16859629 SNP increased the risk of SCC. In two recent studies, the genotype distribution of BTLA rs16859629 showed no statistically significant differences between EGJA cases and controls and the same conclusion was made for susceptibility to ESCC [18, 19]. Interestingly, the present study first explored that BTLA rs16859629 SNP could promote the susceptibility to cancer. However, considering that the number of SCC was relatively small, the results might not be convincing enough. In the future, a larger case-control group study should be performed to unveil the susceptibility relationship between BTLA rs16859629 and SCC.Previous studies have shown that the genetics of the SNP locus in theBTLA rs16859629, rs1982809, rs2171513, and rs3112270 may not be random [18]. In the present study, it was found that haploid type of the four candidate SNP loci may play an important role in heredity. It was found that the haplotype BTLA Trs16859629Ars1982809Grs2171513Grs3112270 could significantly change the susceptibility to NSCLC, affecting 18.70% of the normal population. Interestingly, compared to the study by Tang et al. [18], it was found that with alteration of rs1982809, the effect of this single locus on haplotype significantly reversed the association with cancer. When BTLA rs1982809 was allele A, it decreased the risk of NSCLC. This is in contrary to the findings of Tang et al. [18]. However, our findings need to be further verified in the future studies. In addition, stepwise analysis of the SPSS 23 software was used to analyze the correlation of age, sex, smoking, drinking, BMI, and the four candidate SNPs. We found that the BTLA rs1982809 among the four candidate SNPs may be a key factor affecting the susceptibility to NSCLC. The detailed results are shown in supplementary document 1.Despite some interesting findings, our study suffers from the following potential limitations. Firstly, our sample is only from two hospitals, the Affiliated Hospital of Fujian Medical University and Jiangsu University. Although the four selected SNPs were consistent with HWE and there is no significant difference between MAF and database of Chinese Han populations, the bias might still be unavoidable. Secondly, the sample sizes of some subgroups are relatively small, especially in the SCC subgroup. As such, our conclusions may not be sufficient to testify the real relationship ofBTLA polymorphisms with susceptibility to SCC. The replication of the study with larger sample sizes is required to further validate our findings. Thirdly, only four functional loci in BTLA gene were selected in our study, and, therefore, other polymorphisms of BTLA should not be ignored. Finally, a functional study for these identified SNPs was not performed.


## 5. Conclusions

Despite some shortcomings, our findings preliminarily suggest thatBTLA rs1982809 and rs16859629 SNPs may contribute to the risk of NSCLC. However, a thorough study with larger samples should be worth to elucidate the potential molecular functions of these BTLA polymorphisms.



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*Source: 9121824-2021-01-30.xml*