Influence of single nucleotide polymorphisms among cigarette smoking and non-smoking patients with coronary artery disease, urinary bladder cancer and lung cancer
DOI: 10.1371/journal.pone.0243084, Volume: 16, Issue: 1
Article Type: research-article, Article History

Table of Contents





Cigarette smoke is suggested to be a risk factor for coronary artery disease (CAD), urinary bladder cancer (UBCa) or lung cancer (LCa). However, not all heavy smokers develop these diseases and elevated cancer risk among first-degree relatives suggests an important role of genetic factor.


Three hundred and ten healthy blood donors (controls), 98 CAD, 74 UBCa and 38 LCa patients were included in this pilot study. The influence of 92 single nucleotide polymorphisms (SNPs) and impact of cigarette smoking were analysed.


Out of 92 SNPs tested, differences in distribution of 14 SNPs were detected between controls and patient groups. Only CTLA4 rs3087243 showed difference in both CAD and UBCa patient group compared to control group. Stratified by smoking status, the impact of smoking was associated to frequencies of 8, 3 and 4 SNPs in CAD, UBCa, LCa patients, respectively. None of these 92 SNPs showed a statistically significant difference to more than one type of disease among smoking patients. In non-smoking patients, 7, 3 and 6 SNPs were associated to CAD, UBCa, LCa, respectively. Out of these 92 SNPs, CTLA4 rs3087243 was associated to both non-smoking CAD and UBCa. The XRCC1 rs25487 was associated to both non-smoking UBCa and LCa.


SNPs might be important risk factors for CAD, UBCa and LCa. Distribution of the SNPs was specific for each patient group, not a random event. Impact of cigarette smoking on the disease was associated to the specific SNP sequences. Thus, smoking individuals with SNPs associated to risk of these serious diseases is an important target group for smoking cessation programs.

Laytragoon Lewin, Karlsson, Robinsson, Fagerberg, Kentsson, Sayardoust, Nilsson, Shamoun, Andersson, Löfgren, Rutqvist, Lewin, and Parine: Influence of single nucleotide polymorphisms among cigarette smoking and non-smoking patients with coronary artery disease, urinary bladder cancer and lung cancer


Cigarette smoke is a toxic and carcinogenic agent that is suggested to be a major risk factor for serious diseases [1, 2]. The smoking associated diseases could among other diseases, be coronary artery disease (CAD), head and neck cancer, urinary bladder cancer (UBCa), obstructive pulmonary diseases or lung cancer (LCa) [36].

It is assumed that about 50% of all tobacco smokers will die from smoking and 60% of all deaths caused by cigarette smoke are in cancer or CAD [7, 8]. The mechanisms that determines which one of the smoking related disease each patient will suffer from are unknown. Smoking related diseases put a major strain on the health care systems and are major cause of early death in the world [8].

However, not all heavy smokers develop tobacco induced diseases. Elevated cancer risk found among first-degree relatives of cancer patients suggests an important role of genetic factors [9, 10]. Single nucleotide polymorphism (SNP) is the most common source of human genetic variation in DNA sequences [1113]. SNPs are inborn and lifelong stable. SNPs might influence risk of individual specific disease independents from cigarette smoke toxic agents.

Cigarette smoke induces massive normal cell death in vitro [13]. As a consequence of massive normal cell death, long term cigarette smoking could induce systemic chronic inflammation and immune-suppression in healthy smokers [4]. The possible impact of genetics and cigarette smoking on circulating immune response cells and inflammatory biomarkers was also found in healthy smokers [4, 14]. Overtime, a chronic inflammatory environment might influence tumor suppressor genes, oncogenes and various functional genes [15, 16].

In this pilot study, the SNP distribution in CAD, UBCa, LCa patients were compared with healthy controls, and the impact of cigarette smoking on these diseases were investigated. Ninety-two SNPs in genes associated to cell cycle, cell death, immune response, DNA repair, inflammation, microRNA and oncogenesis were analyzed.

Material and methods

Patients and controls

A total of 512 individuals were investigated. The study patients (n = 210) and controls (n = 302) were from a community-based population of European descent in Jönköping region, Sweden (Table 1). A non-randomized and discretionary group of CAD, UBCa and LCa patients, aged ≥ 19 years were invited to participate. No power calculation was applied since this was a pilot study aimed to be hypothesis generating. The patient inclusion criteria were based on relevant diagnostic procedure or pathological diagnosis of the given diseases.

Table 1
Characteristics of coronary artery disease (CAD), urinary bladder cancer (UBCa), lung cancer (LCa) patients and healthy blood donors (controls).
Mean aged, year (SD)Sex (n)Smoking (n)
Controls30256 (11)155147142160

Healthy controls, aged ≥ 19 years were recruited from the blood bank, the periodontal clinic, and the smoking prevention clinic. Samples from the periodontal clinic donors were drawn at least three months after any treatment and the individuals showed no clinical signs of local inflammation. None of the controls had a history of cardiovascular disease, kidney disorder, malignant or pulmonary disease.

Thirty ml peripheral blood was collected from the controls and patients using EDTA containing tubes. All blood samples were stored at -80°C in the biobank, at Ryhov hospital, Sweden until analysed.

Ethics statement

This pilot study was conducted in accordance with the Declaration of Helsinki and Regional Research Ethics Review Board of Linköping, Sweden approved our study (Dnr 2011/271-31 and 2015/178-32). Written informed consent was obtained from all participants.

SNP analysis

High molecular weight DNA was extracted from blood samples using QIAGEN Bio Robot M48 with MagAttract DNA Blood M48 kits EZ1 ( The quantity and quality of DNA was determined by NanoDrop. According to genetics home reference-NIH ( and previous investigations, 92 SNPs located in genes associated to cell cycle controls, cell death, immune response, DNA repair, inflammation, microRNAs, oncogenes and tumour suppressor genes (Table 2) were selected for this study [4, 1214, 17, 18].

Table 2
Genes and SNPs.
ABCC1rs2230671CYC oxidasers4646miR146Ars2910164
CCND3rs3218086IFNg RNArs2430561TERTrs2736100

All 92 SNP sequences were tested and passed two-hits in the dbSNP database and were Hap Map-validated with Illumina design ability score [19]. Genotyping of SNP was done at the SNP & SEQ Technology Platform, Uppsala University, Sweden ( High-throughput genotyping with the Illumina Golden gate assay was used according to the manufacturer’s protocol (

Statistical analysis

Results are presented as numbers, means and standard deviations. Fishers exact test were used to evaluate possible differences in the frequency distribution of SNPs between the patient and control group. To examine the impact of cigarette smoking and SNPs, comparisons between SNP frequencies among the cigarette smoking (smoking) patients in each group to their corresponding smoking controls were analyzed. For the impact of unknown co-factors, SNP frequencies in patients who never had used any type of tobacco products (non-smoking) in each patient group were compared to non-smoking controls.

In the univariable logistic regression, the results were presented as Wald Chi-square test p-values, odds ratios (OR) and corresponding 95% confidence intervals (95% CI). The most frequent SNP among controls were used as reference level for OR in regression models. Stratified by disease and smoking status, no other interaction effects were analyzed.

Statistical analyses were done using SAS 9.4 software (SAS Institute, Cary, NC). All comparisons were two-sided and a Fishers exact test p value ≤0.05 was considered statistically significant.


Controls and patients

A total of 512 participants were enrolled from one community based population in Jönköping region, Sweden during 2016 to 2019 (Table 1). There were 302 controls (155 males and 147 females). The median age of females was 55 years (range 20–89) and of males was 56 years (range 19–79). Among controls, 150 had a history of cigarette smoking (smoking) and 160 had never use any type of tobacco products (non-smoking).

A total 210 patients (154 males and 56 females) were included. The median age of females was 69 years (range 52–84) and of males was 71 years (range 39–91). The 98 CAD patients (83 males and 15 females) were 53 smoking and 45 non-smoking. The 74 UBCa patients (59 males and 15 females) were 15 smoking and 59 non-smoking. The 38 LCa patients (12 males and 26 females) were 31 smoking and 7 non-smoking.

SNP distribution among CAD, UBCa and LCa patient groups and control group

Out of the 92 SNPs tested (Table 2), differences in distribution of 14 SNPs were detected between controls and the CAD, UBCa or LCa patients (Table 3). Differences between controls and CAD patients were detected in six SNPs located in ATM, CTLA4, BRCA1, CCND3, HRas and IL2. Differences between controls and UBCa patients were detected in six SNPs located in ABCC5, CDH13, CRP, CTLA4, p2 and TNFSF1 Differences between controls and LCa patients were detected in three SNPs located in DNMT3B, MTHFR, and Serpin1.

Table 3
SNP sequences of 302 controls (C), 98 cardiovascular artery disease (CAD), 74 urinary bladder cancer (UBCa) and 38 lung cancer (LCa) patients.
Fisher exact testFisher exact test
Geners IDSequenceControl (n)CAD (n)p-valueWald Chi2Pr>Chi2Odds RatioEstimate95% Confidence LimitsUBCa (n)p-valueWald Chi2Pr>Chi2Odds RatioEstimate95% Confidence LimitsLCa (n)
C vs CADC vs UBCa
ABCC5rs7636910AA138460.482.730.25A/A vs A/G1.180.73–1.92370.027.480.02A/A vs A/G1.520.87–2.6621
AG14240A/A vs G/G0.610.28–1.3325A/A vs G/G0.490.22–1.0913
GG2212G/G vs A/G1.930.88–4.2512G/G vs A/G3.101.36–7.054
ATMrs1801516AA660.055.360.07A/A vs A/G4.091.20–13.9110.690.720.70A/A vs A/G0.790.09–6.940
AG9022A/A vs G/G2.910.73–1.9219A/A vs G/G0.630.07–5.3412
GG20470A/G vs G/G0.710.41–1.2254A/G vs G/G0.790.45–1.4226
BRCA1rs1799966AA156390.036.800.03A/G vs A/A1.460.90–2.36390.220.940.63A/G vs A/A0.870.51–1.4924
AG12947G/G vs A/A2.821.24–6.3928G/G vs A/A1.410.52–3.8212
GG1712A/G vs G/G0.520.23–1.166A/G vs G/G0.620.22–1.702
CCND3rs3218086AA1370.018.560.01A/A vs A/G1.080.39–2.9210.282.330.31A/A vs A/G0.250.03–1.992
AG7437A/A vs G/G2.140.82–5.6323A/A vs G/G0.330.04–2.5913
GG21554A/G vs G/G1.991.21–3.2750A/G vs G/G1.340.76–2.3423
CDH13rs12445758AA3960.060.770.68A/A vs A/G1.280.65–2.53160.027.460.02A/A vs A/G1.420.71–2.821
AG12841A/A vs G/G1.350.69–2.6637A/A vs G/G2.641.26–5.5422
GG13541A/G vs G/G1.060.64–1.7321A/G vs G/G1.861.03–3.3415
CRPrs1800947CC261860.620.880.64C/G vs C/C0.840.41–1.69580.054.710.10C/G vs C/C1.580.80–3.0830
CG4011G/G vs C/C3.040.19–49.0414G/G vs C/C9.000.80–100.937
GG11C/G vs G/G0.280.02–4.762C/G vs G/G0.180.02–2.081
CTLA4rs3087243AA68360.00311.120.003A/A vs A/G1.500.86–2.61290.00410.790.005A/A vs A/G1.670.91–3.096
AG10236A/A vs G/G2.691.50–4.8216A/A vs G/G2.961.55–5.6718
GG13226A/G vs G/G1.791.02–3.1619A/G vs G/G1.770.93–3.3814
DNMT3Brs2424913AA67160.241.690.43A/A vs A/G0.690.37–1.31150.920.160.92A/A vs A/G0.870.45–1.6914
AG15252A/A vs G/G0.650.32–1.2839A/A vs G/G0.910.43–1.9120
GG8130G/G vs A/G1.080.64–1.8320G/G vs A/G0.960.52–1.764
Hrasrs12628AA119550.046.430.04A/A vs A/G1.781.09–2.92330.740.590.74A/A vs A/G1.210.69–2.1118
AG13134A/A vs G/G1.950.88–4.3230A/A vs G/G0.960.44–2.0817
GG389G/G vs A/G0.910.40–2.0711G/G vs A/G1.260.58–2.763
IL2rs6822844AA1520.046.020.05A/A vs A/C0.590.13–2.7550.610.980.61A/A vs A/C1.610.53–4.880
AC12624A/A vs C/C0.340.08–1.5022A/A vs C/C1.280.44–3.7110
CC8172C/C vs A/C1.761.04–2.9647C/C vs A/C1.250.72–2.1928
MTHFRrs1801133AA20110.262.600.27A/A vs A/G1.970.86–4.52100.143.770.15A/A vs A/G2.100.89–4.977
AG12234A/A vs G/G1.660.74–3.6929A/A vs G/G2.290.98–5.3115
GG16053A/G vs G/G0.840.52–1.3835A/G vs G/G1.090.63–1.8816
P21rs7767246CC209700.511.300.52C/G vs C/C0.830.05–1.41400.056.050.04C/G vs C/C1.881.11–3.2126
CG8624G/G vs C/C1.710.48–6.0131G/G vs C/C2.240.56–9.039
GG74C/G vs G/G0.490.13–1.813C/G vs G/G0.840.21–3.463
Serpin 1rs1243168AA2170.820.400.82A/A vs A/G1.130.44–2.8820.371.860.39A/A vs A/G0.390.09–1.770
AG11534A/A vs G/G0.970.39–2.3928A/A vs G/G0.360.08–1.588
GG16557A/G vs G/G0.860.53–1.3944A/G vs G/G0.910.54–1.5530
TNFSF1rs1054016AA65180.481.480.48A/A vs A/C0.690.37–1.2690.027.210.03A/A vs A/C0.500.23–1.117
AC13153A/A vs C/C0.760.38–1.5336A/A vs C/C0.330.15–0.7517
CC6925C/C vs A/C0.900.51–1.5629C/C vs A/C1.530.87–2.7014
Fisher exact test
Geners IDSequenceControl (n)CAD (n)LCa (n)p-valueWald Chi2Pr>Chi2Odds RatioEstimate95% Confidence Limits
C vs LCa
ABCC5rs7636910AA13846210.312.300.32A/A vs A/G1.660.80–3.45
AG1424013A/A vs G/G0.840.26–2.67
GG22124G/G vs A/G1.990.59–6.64
ATMrs1801516AA6600.851.459*0.48A/A vs A/GN/AN/A
AG902212A/A vs G/GN/AN/A
GG2047026A/G vs G/G1.050.51–2.17
BRCA1rs1799966AA15639240.391.830.39A/G vs A/A0.610.29–1.26
AG1294712G/G vs A/A0.770.16–3.52
GG17122A/G vs G/G0.790.16–3.84
CCND3rs3218086AA13720.291.840.39A/A vs A/G0.880.17–4.34
AG743713A/A vs G/G1.440.30–6.77
GG2155423A/G vs G/G1.640.79–3.40
CDH13rs12445758AA39610.084.290.12A/A vs A/G0.150.01–1.14
AG1284122A/A vs G/G0.230.03–1.80
GG1354115A/G vs G/G1.550.76–3.11
CRPrs1800947CC26186300.143.010.22C/G vs C/C1.520.62–3.69
CG40117G/G vs C/C8.700.53–142.70
GG111C/G vs G/G0.180.01–3.13
CTLA4rs3087243AA683660.242.780.25A/A vs A/G0.500.18–1.32
AG1023618A/A vs G/G0.830.30–2.26
GG1322614A/G vs G/G1.660.79–3.50
DNMT3Brs2424913AA6716140.046.060.05A/A vs A/G1.590.75–3.33
AG1525220A/A vs G/G4.231.33–13.45
GG81304G/G vs A/G0.370.12–1.13
Hrasrs12628AA11955180.591.020.59A/A vs A/G1.160.57–2.36
AG1313417A/A vs G/G1.910.53–6.86
GG3893G/G vs A/G0.610.16–2.18
IL2rs6822844AA15200.151.62*0.44A/A vs A/CN/AN/A
AC1262410A/A vs C/CN/AN/A
CC817228C/C vs A/C1.640.77–3.51
MTHFRrs1801133AA201170.036.120.05A/A vs A/G2.851.03–7.85
AG1223415A/A vs G/G3.501.28–9.54
GG1605316A/G vs G/G1.230.58–2.58
P21rs7767246CC20970260.153.420.18C/G vs C/C0.840.37–1.86
CG86249G/G vs C/C3.450.83–14.15
GG743C/G vs G/G0.240.05–1.11
Serpin 1rs1243168AA21700.015.33*0.07A/A vs A/GN/AN/A
AG115348A/A vs G/GN/AN/A
GG1655730A/G vs G/G0.380.17–0.87
TNFSF1rs1054016AA651870.352.050.36A/A vs A/C0.830.32–2.10
AC1315317A/A vs C/C0.530.20–1.39
CC692514C/C vs A/C1.560.72–3.36
*N/A not estimable due to zero or few individuals.

Of the 92 SNPs (Table 2), only CTLA4 rs3087243 showed a statistically significant difference in two groups of the patients, CAD and UBCa, compared to the controls (Table 3).

SNP distribution among smoking CAD, UBCa or LCa patients and smoking controls

Out of these 92 SNPs, differences in the distribution of 15 SNPs were detected between smoking controls and smoking patients (Table 4). Differences in seven SNPs located in CCND3, CTLA4, KDM4C, PFA1, PPAP2B, PRF1 and Rad52, were found to be specific for smoking CAD patients. Differences in three SNPs located in ABCA1, CCND1 and MiR206 were specific for smoking UBCa patients. Differences in four SNPs located in CDH13, HTR3B1, CRP and ZMF830 were specific for smoking LCa patients.

Table 4
Cigarette smoking and SNPs sequences of 142 controls (C), 55 cardiovascular artery disease (CAD), 15 urinary bladder cancer (UBCa) and 31 lung cancer (LCa) patients.
Fisher exact testFisher exact test
GeneIDSequenceControl (n)CAD (n)p-valueWald Chi2Pr>Chi2Odds RatioEstimate95% Confidence LimitsUBCa (n)p-valueWald Chi2Pr>Chi2Odds RatioEstimate95% Confidence Limits
C vs CADC vs UBCaLCa (n)
ABCA1rs2230806AA1340.551.180.55A/A vs A/G0.950.27–3.3010.030.570.75A/A vs A/G1.700.29–9.990
AG5618A/A vs G/G0.680.20–2.241A/A vs G/G1.900.35–10.1613
GG7333A/G vs G/G0.710.36–1.3913A/G vs G/G1.110.34–3.6118
CCND1rs602652AA21120.075.090.07A/G vs A/A2.951.15–7.5610.035.060.07A/G vs A/A11.811.35–103.0110
AG1627G/G vs A/A1.860.68–5.079G/G vs A/A6.990.74–66.1913
GG1516A/G vs G/G1.580.62–4.035A/G vs G/G1.680.46–6.198
CCND3rs3218086AA840.019.090.01A/A vs A/G0.650.17–2.4310.531.230.54A/A vs A/G0.750.07–7.361
AG3023A/A vs G/G1.850.52–6.619A/A vs G/G1.440.16–12.8711
GG10428A/G vs G/G2.841.43–5.645A/G vs G/G1.920.60–6.1819
CDH13rs12445758AA1560.790.450.79A/A vs A/G0.910.31–2.6540.074.810.08A/A vs A/G1.900.49–7.401
AG5022A/A vs G/G1.140.40–3.237A/A vs G/G5.131.15–22.8218
GG7727A/G vs G/G1.250.64–2.444A/G vs G/G2.690.75–9.6812
CRPrs1800947CC124490.730.11*0.73C/G vs C/C0.840.31–2.25140.500.44*0.50C/G vs C/C0.490.06–3.9724
CG186G/G vs C/CN/AN/A1G/G vs C/CN/AN/A6
GG00C/G vs G/GN/AN/A0C/G vs G/GN/AN/A1
CTLA4rs3087243AA35240.036.020.04A/A vs A/G0.970.42–2.2260.352.000.36A/A vs A/G1.570.44–5.595
AG4615A/A vs G/G2.581.01–6.575A/A vs G/G2.610.69–9.9013
GG6116A/G vs G/G2.661.16–6.114A/G vs G/G1.650.42–6.5113
HTR3Brs1672717AA40220.452.540.27A/A vs A/G1.690.85–3.3520.771.440.48A/A vs A/G0.400.08–1.9114
AG80126A/A vs G/G1.720.63–4.6810A/A vs G/G0.360.05–2.3610
GG227G/G vs A/G0.970.37–2.553G/G vs A/G1.090.27–4.307
KDM4Crs2296067AA530.018.580.01A/A vs A/G0.940.20–4.2210.760.530.76A/A vs A/G2.350.21–25.320
AG4730A/A vs G/G2.450.54–11.054A/A vs G/G1.800.19–16.9815
GG9022A/G vs G/G2.611.35–5.0110A/G vs G/G0.760.22–2.5716
miR206rs6920648AA41130.740.580.74A/A vs A/G0.740.35–1.5890.035.940.05A/A vs A/G5.341.36–20.849
AG7331A/A vs G/G0.800.31–2.053A/A vs G/G2.040.50–8.2417
GG2811G/G vs A/G0.920.41–2.083G/G vs A/G2.600.49–13.695
PFA1rs10999426AA1060.023.730.15A/A vs A/G0.180.02–1.4820.471.470.47A/A vs A/G2.600.46–14.674
AG7818A/A vs G/G0.300.03–2.436A/A vs G/G1.540.27–8.5314
GG5431G/G vs A/G0.620.30–1.267G/G vs A/G1.680.53–5.2913
PPAP2Brs1261411AA1820.045.880.05A/A vs A/G0.200.04–0.9200.260.44*0.79A/A vs A/GN/AN/A5
AG6234A/A vs G/G0.350.07–1.659A/A vs G/GN/AN/A18
GG6029G/G vs A/G0.580.29–1.126G/G vs A/G0.680.23–2.058
PRF1rs3758562AA53300.033.530.17A/A vs A/G0.640.31–1.2970.441.560.45A/A vs A/G1.730.55–5.4613
AG7919A/A vs G/G3.380.41–27.486A/A vs G/G0.660.12–3.6514
GG106G/G vs A/G0.190.02–1.502G/G vs A/G2.630.46–14.854
RAD52rs11571424AA020.010.330.84A/A vs A/G1.460.13–15.6100.430.600.43A/A vs A/GN/AN/A1
AG2617A/A vs G/G1.170.11–11.704A/A vs G/GN/AN/A4
GG2636G/G vs A/G1.240.56–2.7411G/G vs A/G0.610.18–2.0926
ZMF830rs3744355CC125460.711.46*0.47C/G vs C/C0.530.19–1.47120.511.09*0.57C/G vs C/C2.080.52–8.2322
CG158G/G vs C/CN/AN/A3G/G vs C/CN/AN/A8
GG21C/G vs G/GN/AN/A0C/G vs G/GN/AN/A1
Fisher exact test
GeneIDSequenceControl (n)CAD (n)p-valueWald Chi2Pr>Chi2Odds RatioEstimate95% Confidence Limits
C vs LCa
ABCA1rs2230806AA1340.610.930.62A/A vs A/G0.350.04–3.03
AG5618A/A vs G/G0.420.05–3.49
GG7333A/G vs G/G1.190.52–2.68
CCND1rs602652AA21120.581.090.57A/G vs A/A1.700.59–4.87
AG1627G/G vs A/A1.120.35–3.50
GG1516A/G vs G/G1.520.49–4.70
CCND3rs3218086AA840.222.920.23A/A vs A/G0.340.03–3.04
AG3023A/A vs G/G0.680.08–5.79
GG10428A/G vs G/G2.000.86–4.67
CDH13rs12445758AA1560.045.640.05A/A vs A/G0.180.02–1.50
AG5022A/A vs G/G0.420.05–3.54
GG7727A/G vs G/G2.311.02–5.20
CRPrs1800947CC124490.051.08*0.58C/G vs C/C1.720.62–4.78
CG186G/G vs C/CN/AN/A
GG00C/G vs G/GN/AN/A
CTLA4rs3087243AA35240.481.450.48A/A vs A/G0.500.16–1.55
AG4615A/A vs G/G0.670.22–2.03
GG6116A/G vs G/G1.320.56–3.13
HTR3Brs1672717AA40220.055.670.05A/A vs A/G2.801.14–6.85
AG80126A/A vs G/G1.100.38–3.13
GG227G/G vs A/G2.540.86–7.45
KDM4Crs2296067AA530.192.12*0.34A/A vs A/GN/AN/A
AG4730A/A vs G/GN/AN/A
GG9022A/G vs G/G1.790.81–3.94
miR206rs6920648AA41130.890.220.89A/A vs A/G0.940.38–2.30
AG7331A/A vs G/G1.220.37–4.05
GG2811G/G vs A/G0.760.25–2.27
PFA1rs10999426AA1060.441.590.45A/A vs A/G2.220.61–8.10
AG7818A/A vs G/G1.660.44–6.14
GG5431G/G vs A/G1.340.58–3.07
PPAP2Brs1261411AA1820.212.990.22A/A vs A/G0.950.31–2.93
AG6234A/A vs G/G2.080.60–7.15
GG6029G/G vs A/G0.450.18–1.13
PRF1rs3758562AA53300.421.690.42A/A vs A/G1.380.60–3.17
AG7919A/A vs G/G0.610.16–2.27
GG106G/G vs A/G2.250.62–8.21
RAD52rs11571424AA020.080.42*0.80A/A vs A/GN/AN/A
AG2617A/A vs G/GN/AN/A
GG2636G/G vs A/G1.450.46–4.53
ZMF830rs3744355CC125460.055.430.06C/G vs C/C3.031.14–7.99
CG158G/G vs C/C2.840.24–32.68
GG21C/G vs G/G1.060.08–13.65
*N/A not estimable due to zero or few individuals.

None of these 92 SNPs showed a statistically significant difference in distribution in more than one disease among smoking patients compared to their corresponding smoking controls (Table 4).

SNPs distribution among non-smoking CAD, UBCa, LCa patients and non-smoking controls

Out of these 92 SNPs tested, differences in distribution of 14 SNPs were detected between the non-smoking controls and non-smoking patients (Table 5). They were seven SNPs located in ATM, BRCA1, CTLA4, CYP19A1, FGFR4, MiR34A and PRKDC in non-smoking CAD patients. Three SNPs located in MTHFR, CTLA4, and XRCC1, in non-smoking UBCa patients and four SNPs located in HTRB1, Lig4, Serpin1, TGFb, and XRCC1 in non-smoking LCa patients (Table 5).

Table 5
Non-smoking and SNP sequences of 160 controls (C), 43 cardiovascular artery disease (CAD), 59 urinary bladder cancer (UBCa) and 7 lung cancer (LCa) patients.
Fisher exact testFisher exact test
GeneIDSequenceControl (n)CAD (n)p-valueWald Chi2Pr>Chi2Odds RatioEstimate95% Confidence LimitsUBCa (n)p-valueWald Chi2Pr>Chi2Odds RatioEstimate95% Confidence Limits
C vs CADC vs UBCa
ATMrs1801516AA130.024.900.08A/A vs A/G14.391.35–153.0310.710.650.72A/A vs A/G2.990.17–50.74
AG4810A/A vs G/G10.991.10–109.5816A/A vs G/G2.610.16–42.79
GG11030A/G vs G/G0.760.34–1.6842A/G vs G/G0.870.44–1.70
BRCA1rs1799966AA87150.026.920.03A/G vs A/A1.960.94–4.07290.571.070.58A/G vs A/A1.100.59–2.07
AG6522G/G vs A/A4.351.32–14.3224G/G vs A/A1.870.56–6.18
GG86A/G vs G/G0.450.14–1.445A/G vs G/G0.590.17–1.98
CTLA4rs3087243AA33120.046.020.04A/A vs A/G0.970.42–2.22230.019.240.01A/A vs A/G1.850.89–3.86
AG5621A/A vs G/G2.581.01–6.5721A/A vs G/G3.291.52–7.13
GG7110A/G vs G/G2.661.16–6.1015A/G vs G/G1.770.83–3.75
CYP19A1rs51502844AA570.027.320.02A/A vs A/C5.461.42–10.8840.850.320.85A/A vs A/C1.300.31–5.32
AC3910A/A vs C/C2.260.65–7.8724A/A vs C/C1.080.26–4.37
CC4226A/C vs C/C0.410.17–0.9631A/C vs C/C0.830.41–1.66
EHBP1rs721048AA420.193.220.19A/A vs A/G1.350.22–7.9520.810.410.81A/A vs A/G1.220.20–7.19
AG5420A/A vs G/G2.420.41–14.1322A/A vs G/G1.450.25–8.30
GG10221A/G vs G/G1.790.89–3.6035A/G vs G/G1.180.63–2.22
FGFR4rs2011077AA800.043.61*0.16A/A vs A/GN/AN/A20.531.260.53A/A vs A/G0.540.10–2.76
AG5021A/A vs G/GN/AN/A23A/A vs G/G0.750.15–3.70
GG10222A/G vs G/G1.940.98–3.8734A/G vs G/G1.380.73–2.58
HTR3B1rs3782025AA44110.920.060.96A/A vs A/G0.910.40–2.07150.711.870.39A/A vs A/G0.770.37–1.57
AG7721A/A vs G/G0.880.34–2.2734A/A vs G/G1.320.53–3.29
GG3911G/G vs A/G1.030.45–2.360G/G vs A/G0.580.26–1.29
Lig4rs1805386AA120290.461.490.47A/G vs A/A1.580.74–3.37450.370.06*0.96A/G vs A/A1.090.54–2.23
AG3413G/G vs A/A0.820.09–7.3514G/G vs A/AN/AN/A
GG51A/G vs G/G1.910.20–17.950A/G vs G/GN/AN/A
miR34Ars4938723AA63170.036.600.03A/A vs A/G1.420.65–3.06240.890.230.89A/A vs A/G1.000.53–1.88
AG7915A/A vs G/G0.410.16–1.0530A/A vs G/G1.290.43–3.90
GG1711G/G vs A/G3.401.33–8.705G/G vs A/G0.770.26–2.28
MTHFRrs1801133AA840.451.570.45A/A vs A/G2.300.60–8.82100.019.140.01A/A vs A/G3.001.06–8.48
AG6013A/A vs G/G1.760.49–6.3425A/A vs G/G4.791.70–13.45
GG9226A/G vs G/G0.760.36–1.6024A/G vs G/G1.590.83–3.05
PRKDCrs1231204CC020.011.60*0.44C/C vs C/GN/AN/A00.151.970.15C/C vs C/GN/AN/A
CG294C/C vs G/GN/AN/A6C/C vs G/GN/AN/A
GG13137C/G vs G/G0.480.16–1.4753C/G vs G/G0.510.20–1.30
Serpin1rs1243168AA1240.860.310.85A/A vs A/G1.160.33–4.0620.411.710.42A/A vs A/G0.500.10–2.41
AG6318A/A vs G/G1.340.39–4.6021A/A vs G/G0.390.08–1.84
GG8521A/G vs G/G1.150.56–2.3536A/G vs G/G0.780.42–1.47
TGFbrs1800469AA1830.322.980.22A/A vs A/G0.440.12–1.6660.391.841.84A/A vs A/G0.710.25–1.98
AG6223A/A vs G/G0.770.20–2.9229A/A vs G/G1.090.39–3.07
GG7917A/G vs G/G1.720.84–3.5024A/G vs G/G1.540.81–2.90
XRCC1rs25487AA1740.780.480.78A/A vs A/G0.980.29–3.33110.055.700.05A/A vs A/G1.440.60–3.45
AG6716A/A vs G/G0.770.23–2.5430A/A vs G/G2.731.09–6.82
GG7623A/G vs G/G0.780.38–1.6118A/G vs G/G1.890.96–3.69
Fisher exact test
GeneIDSequenceControl (n)CAD (n)LCa (n)p-valueWald Chi2Pr>Chi2Odds RatioEstimate95% Confidence Limits
C vs LCa
ATMrs1801516AA1300.640.77*0.68A/A vs A/GN/AN/A
AG48101A/A vs G/GN/AN/A
GG110306A/G vs G/G0.380.04–3.25
BRCA1rs1799966AA871500.201.88*0.39A/G vs A/A0.220.02–1.89
AG65221G/G vs A/AN/AN/A
GG866A/G vs G/GN/AN/A
CTLA4rs3087243AA331210.143.230.19A/A vs A/G0.330.03–3.03
AG56215A/A vs G/G2.150.13–35.46
GG71101A/G vs G/G6.330.72–55.82
CYP19A1rs51502844AA5710.242.340.31A/A vs A/C7.800.41–145.19
AC39101A/A vs C/C1.680.16–17.41
CC42265A/C vs C/C0.220.02–1.92
EHBP1rs721048AA4200.024.93*0.08A/A vs A/GN/AN/A
AG54206A/A vs G/GN/AN/A
GG102211A/G vs G/G11.331.33–96.57
FGFR4rs2011077AA8000.331.63*0.44A/A vs A/GN/AN/A
AG50214A/A vs G/GN/AN/A
GG102223A/G vs G/G2.720.58–12.62
HTR3B1rs3782025AA441110.0215.660.05A/A vs A/G1.750.10–28.67
AG77211A/A vs G/G0.170.02–1.58
GG39115G/G vs A/G9.871.11–87.44
Lig4rs1805386AA1202920.016.42*0.04A/G vs A/A8.821.63–47.51
AG34135G/G vs A/AN/AN/A
GG510A/G vs G/GN/AN/A
miR34Ars4938723AA631740.123.210.21A/A vs A/GN/AN/A
AG79151A/A vs G/GN/AN/A
GG17112G/G vs A/GN/AN/A
MTHFRrs1801133AA8400.202.48*0.29A/A vs A/GN/AN/A
AG60135A/A vs G/GN/AN/A
GG92262A/G vs G/G3.830.72–20.31
PRKDCrs1231204CC0200.210.00*0.96C/C vs C/GN/AN/A
CG2940C/C vs G/GN/AN/A
GG131377C/G vs G/GN/AN/A
Serpin1rs1243168AA12400.050.01*0.99A/A vs A/GN/AN/A
AG63180A/A vs G/GN/AN/A
GG85217A/G vs G/GN/AN/A
TGFbrs1800469AA18310.030.25*0.88A/A vs A/G0.570.07–5.08
AG62236A/A vs G/GN/AN/A
GG79170A/G vs G/GN/AN/A
XRCC1rs25487AA17430.045.230.07A/A vs A/G5.910.91–38.23
AG67162A/A vs G/G6.711.04–43.29
GG76232A/G vs G/G1.130.16–8.28
*N/A not estimable due to zero or few individuals.

Two of these 92 SNPs, CTLA4 rs3087243 and XRCC1 rs25487 showed different distribution in more than one disease among non-smoking patient groups compared to their non-smoking controls (Table 5). The SNP in CTLA4 rs3087243 showed a difference in distribution among non-smoking controls and non-smoking CAD or UBCa patients. The SNP in XRCC1 rs25487 showed a difference in distribution among non-smoking controls and non-smoking UBCa or LCa patients.


The genetic and the environment influence human risk of various diseases and clinical outcome. We found that specific genetic variations influenced the risk of diseases that are suggested to associate to cigarette smoking such as CVD, UBCa and LCa. These diseases are heterogeneous groups regarding site location and their pathological parameters.

Endothelial malignancies are very rare in the western population and consists mainly of sarcomas. The endothelial cells seems not to be transformed into malignancies from smoking [20]. However, the blockage of the CAD patient artery caused by smoking is a complex mechanism involving inflammation and benign smooth muscle proliferation [21, 22].

Out of these 92 SNPs, only distribution of CTLA4 rs3087243 differed between controls and two patient groups, CAD and UBCa. CTLA4 located on chromosome 2q33.2, encodes for CTLA4 protein that functions as an immune checkpoint and downregulates immune responses [23, 24]. CTLA4 is constitutively expressed in regulatory T cells. Upon activation, conventional T cells in cancer patients could up-regulated CTLA4 [25]. Alteration in CLTA4 expression cells were detected in the inflammatory heart disease [26]. CTLA4 polymorphism and risk of cancer were reported [27, 28]. The possible benefits of immunotherapy with focus on CTLA4 protein as target therapy of CAD and UBCa need further investigation [25, 29, 30].

The impact of cigarette smoking on CAD risk was more pronounced among individuals with specific SNPs in genes involving the immune response (CTLA4, PFA and PRF), cell cycle control (CCND3 and p21), DNA repair (BRCA1, ATM) and oncogene (HRas). The impact of cigarette smoking on UBCa risk seem to be accumulated among individuals with SNPs in genes involved in cell cycle control (ABCC1, CDH13 and p21,) and immune response (CRP, CTLA4 and TNFSF1). The impact of cigarette smoking on LCa risk might be increased among individuals with SNPs in genes involving cell cycle control (CDH13 and HTR3B1) and immune response (CRP). The variation in SNPs among cigarette smoking CAD, UBCa and LCa patients indicates an impact of general toxic agents from cigarette smoke and specific DNA sequences, not random event on the risk for these diseases.

There are subgroups of the CAD, UBCa and LCa patients that never have used any type of tobacco products, non-smoking patient group. Despite similar type of disease, SNPs that associated to the non-smoking UBCa patients differed from those of the smoking UBCa patients. Unknown risk factors, apart from cigarette smoking, in combination with the specific SNPs could also increase the risk of UBCa in non-smoking individuals.

SNP sequence variation in CTLA4 rs3087243 was associated to risk of CAD in both smoking and non-smoking patients. Thus, the influence of immune response on smooth muscle cell proliferation of CAD patients [26] was independent from cigarette smoking. SNPs in the CTLA4 rs3087243 also associate to risk of non- smoking UBCa patients, not in smoking UBCa patients. This suggests an influence of CTLA4 rs3087243 on risk of CAD and UBCa might be independent from the cigarette smoke.

XRCC1 gene located on chromosome 19q13.2, encoded for XRCC1 protein is an essential for DNA damage repair [3133]. Genetic variations that influence DNA damage repair efficiency in combination with harm micro-environment might increase risk of UBCa and LCa [3436].

The differences in XRCC1 rs25487 of non-smoking UBCa and non-smoking LCa patients compared to non-smoking controls, confirms that DNA repair play an important role on UBCa and LCa [34, 35]. If patients with AA or AG sequences in XRCC1 rs25487 are prone to DNA damage from other agents/microenvironments more than cigarette smoke needs further investigation [3739].

Our pilot study has several limitations. Firstly, this is a single center, non- randomized retrospective study with a relatively low number of included patients [40]. In addition, the lower age among controls compared to patients was based on the blood bank regulation, needs to be considered. Secondly, cigarette smoking history of the patients and controls was self-reported and this could be influenced by recall bias [41]. Thirdly, passive smoking conditions that might have impact on risk of these diseases [8, 42], were not recorded.

In summary, our results indicate an important role of specific genetic variations on risk of CAD, UBCa or LCa. Impact of cigarette smoking was also found in a proportion of these patients in association with individual specific SNPs. The SNPs are lifelong stable genetic variation that could predict the risk to develop specific diseases.

Unable to quit smoking during or after treatment could increase rate of diseases recurrence, progression, development of a second primary tumour and disease-specific mortality. The identification of SNPs that associated to risk or disease progression could increase the cessation rate in smoking patients.

Healthy individual with SNPs associated to a smoking related risk to develop CAD, UBCa or LCa will also get higher motivation for smoking cessation. Unknown environmental factors associated to risk of these diseases in non-smoking group [43] and the possibility to use SNPs as prognostic biomarkers for treatment selection and prediction of clinical outcome needs future investigation.


We would like to thank all the patients and controls for blood donation, the blood bank and laboratory personal, Thitiya Leutragoon, nurse and medical doctors at Ryhov hospital for practical work and supported.


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This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. of single nucleotide polymorphisms among cigarette smoking and non-smoking patients with coronary artery disease, urinary bladder cancer and lung cancer&author=Nongnit Laytragoon Lewin,Jan-Erik Karlsson,David Robinsson,Matida Fagerberg,Magnus Kentsson,Shariel Sayardoust,Mats Nilsson,Levar Shamoun,Bengt-Åke Andersson,Sture Löfgren,Lars Erik Rutqvist,Freddi Lewin,Narasimha Reddy Parine,&keyword=&subject=Research Article,Biology and Life Sciences,Psychology,Behavior,Habits,Smoking Habits,Social Sciences,Psychology,Behavior,Habits,Smoking Habits,Biology and Life Sciences,Genetics,Single Nucleotide Polymorphisms,Medicine and Health Sciences,Epidemiology,Medical Risk Factors,Cancer Risk Factors,Medicine and Health Sciences,Oncology,Cancer Risk Factors,Medicine and Health Sciences,Medical Conditions,Cardiovascular Diseases,Coronary Heart Disease,Medicine and Health Sciences,Cardiology,Cardiovascular Medicine,Cardiovascular Diseases,Coronary Heart Disease,Medicine and Health Sciences,Vascular Medicine,Coronary Heart Disease,Medicine and Health Sciences,Medical Conditions,Respiratory Disorders,Smoking Related Disorders,Medicine and Health Sciences,Pulmonology,Respiratory Disorders,Smoking Related Disorders,Medicine and Health Sciences,Mental Health and Psychiatry,Substance-Related Disorders,Smoking Related Disorders,Medicine and Health Sciences,Public and Occupational Health,Substance-Related Disorders,Smoking Related Disorders,Medicine and Health Sciences,Oncology,Cancers and Neoplasms,Lung and Intrathoracic Tumors,Biology and Life Sciences,Genetics,Human Genetics,Biology and Life Sciences,Genetics,Gene Types,Suppressor Genes,Tumor Suppressor Genes,