Abstract

Basal cell carcinoma (BCC) of the skin is the most common neoplasm among the Caucasian population of the Western world. Inflammation may result in oxidative stress and contribute to promotion and progression of tumors, including BCC. The role of cytokines, which are inflammatory modulators, in the biology of tumors has been extensively studied and it is well known that they are aberrantly produced by cancer cells, macrophages and other phagocytic cells. Genetic polymorphisms are known in several cytokine genes, which result in altered expression. In the present association study, we investigated the association of 14 functional polymorphisms in 11 cytokines genes with BCC risk in 529 BCC cases and 532 healthy controls. We have also tested the possible interactions between the genetic variants and three known risk factors for BCC: skin complexion, sun effect and skin response to sun exposure. We did not observe any statistically significant association between SNPs and BCC risk. However, we found that, in a subgroup of subjects more prone to skin burns, carriers of at least one copy of the G allele of rs1800629 ( TNF ) had an increased risk of BCC [odds ratio (OR) = 2.40, 95% confidence interval (CI) 1.38–4.16, P = 0.0005]. Moreover, in subjects less prone to sunburns, we observed that carriers of the C allele of rs1143627 ( IL1B ) showed a decreased risk (OR = 0.53, 95% CI 0.34–0.82, P = 0.0019). In conclusion, we found that two polymorphisms in inflammatory genes interacting with environmental risk factors could modulate BCC risk.

Introduction

Basal cell carcinoma (BCC) of the skin is the most common neoplasm among the Caucasian population of the Western world ( 1–3 ).

Inflammation has been recognized as a contributing factor in pathogenesis of many cancers ( 4 , 5 ). Chronic inflammation may result in oxidative stress and contribute to tumor promotion and progression ( 6–13 ). Production of cytokines by immune/inflammatory cells is a major tumor-promoting mechanism ( 14 ). Cytokines are soluble proteins or glycoproteins that act as mediators of the inflammatory response and are integral to the function of immune cells. The roles of cytokines in the biology of various neoplastic disorders have been extensively studied ( 14 ). They are aberrantly produced by cancer cells, macrophages and other phagocytic cells ( 15 , 16 ). Cytokines activate transcription factors, such as nuclear factor-kappaB, STAT3 and AP-1, in premalignant cells to induce genes that stimulate cell proliferation and survival. It has been suggested that an inflammatory microenvironment can increase DNA mutation rates, in addition to enhancing the proliferation of mutated cells ( 17 ). Immune cells affect malignant cells through production of cytokines, chemokines, growth factors, prostaglandins and reactive oxygen and nitrogen species ( 16 ). Inflammation is particularly important for the neoplastic initiation in those tissues in which epithelia are rapidly renewed, such as the skin ( 4 , 5 , 17–19 ).

Cytokine genes are highly polymorphic. Polymorphisms found in the regulatory regions, including promoters and untranslated regions, in many cases can affect in vitro expression of the gene product and have been associated with cancer risk ( 20 ). We previously reported the association of three polymorphic variants with BCC risk, one in the IL6 gene (rs1800797), one in IL10 (rs1800896) and one in IL1B (rs16944) ( 21 ). This promising result and very strong epidemiologic evidences ( 20 ) prompted us to test a larger set of common polymorphisms of genes encoding for cytokines in an association study based on 529 BCC cases and 532 healthy controls. Our a priori biological hypothesis was that polymorphisms, which could increase the gene or protein expression, could increase the inflammatory response and thus increase BCC risk. We focused our attention on 14 putatively functional single-nucleotide polymorphisms (SNPs) [including the 3 previously reported in Wilkening et al. ( 21 )] in 11 genes which play a key role in the inflammatory response ( 22 ). We also tested the hypothesis that the polymorphic variants in these genes could interact with three known host risk factors for BCC: skin complexion, sun exposure and skin response to sun exposure.

Materials and methods

Study population

Cases and controls were recruited in Hungary, Romania and Slovakia between 2002 and 2004 ( 23 , 24 ). The recruitment was carried out in the counties of Bacs, Csongrad and Jasz-Nagykun-Szolnok in Hungary; Bihor and Arad in Romania and Nitra in Slovakia. BCC cases ( n = 529) were included on the basis of histopathological examinations by pathologists. Hospital-based controls ( n = 532) were included in the study. All general hospitals in the study area were involved in the process of control recruitment and a rotation scheme was used in order to achieve appropriate geographical distribution. The controls were surgery, orthopedic and trauma patients with conditions such as appendicitis, abdominal hernias, duodenal ulcers, cholelithiasis and fractures; patients with malignant tumors, diabetes and cardiovascular diseases were excluded. Cases and controls were recruited among those individuals who have resided in the study area for at least 1 year during their lifetime. Subsequent to the signing of consent forms by the participants, clinicians took venous blood from cases and controls. The blood samples were kept frozen at −80°C until analysis. A general questionnaire was completed by trained personnel after an interview of the recruited cases and controls. The questionnaire was designed to include information on individual cumulative sun exposure in summer, sun tanning, skin complexion, effects of sun exposure on skin and age at diagnosis of BCC; the Fitzpatrick classification was not used because of non-availability of facilities uniformly across all recruiting centers of the participating countries. In addition, the interviews included items on demographic, lifestyle, socioeconomic, medical history, occupational exposures, drinking and nutritional habits as well as detailed residential history. Ethnic background for the cases and controls was recorded along with other characteristics of the study population. Local ethical boards approved the study plan and design.

Selection of genes and polymorphisms

We newly genotyped 14 SNPs in 10 cytokine genes which play a key role in the inflammatory response: interferon gamma ( IFNG , rs2430561), interleukin-1 alpha ( IL1A , rs17561), interleukin-10 ( IL10 , rs1800872, rs3024505), interleukin-12 ( IL12 , rs3212227), interleukin-1 beta ( IL1B rs1143627, rs1143623, rs4848306), interleukin-1 receptor antagonist ( IL1RN , rs419598), interleukin-2 ( IL2 , rs2069762), interleukin-4 ( IL4 , rs2070874, rs2243250), interleukin-8 ( IL8 , rs4073) and tumor necrosis alpha ( TNF , rs1800629). In Table I are given more information on each SNP analyzed in this study: the position in the genome and in the gene, the eventual amino acid change, the trivial names, the reported effect or a previously reported association with disease risk.

Table I.

SNPs selected in the study, their Position in the genome, in the gene, the trivial name and the reported function or association

Gene/SNP Position in the genome Position in the gene and type of substitution Trivial name  Effect or association a Reference 
IL10 rs3024505 chr1:206939904 Intronic GWAS on IBD  ( 25 )  
IL10 rs1800872  chr1:206946407 Promoter −592C/A ↓  ( 26 )  
IL10 rs1800896  chr1:206946897 Promoter −1082G/A ↓  ( 27 )  
IL1A rs17561  chr2:113537223 Missense (Ala > Ser) IL1A 4845G > T Risk of multiple sclerosis  ( 28 )  
IL1B rs1143627  chr2:113594387 Promoter (−)31T > C ↑ and↓  ( 29–31 )  
IL1B rs16944  chr2:113594867 Promoter (−)511G > A ↑  ( 30 )  
IL1B rs1143627 IL1B rs16944 Haplotype  chr2:113594387–113594867 Promoter (−)31T > C (−)511G>A Haplotype ↑  ( 29 )  
IL1B rs1143623 chr2:113595829 Promoter IL1B-1473C ↑  ( 29 )  
IL1B rs4848306 chr2:113598107 Promoter (−)3737 G/A ↓  ( 29 )  
IL1RN rs419598  chr2:113887207 Synonymous (Ala > Ala) (Ala23Ala) + 2018T/C Coronary symptoms  ( 32 )  
IL2 rs2069762  chr4:123377980 Promoter IL2-330G ↑  ( 26 )  
IL8 rs4073  chr4:74606024 Promoter −251T/A ↑  ( 26 , 33 )  
IL4 rs2243250  chr5:132009154 Promoter IL-4 C-590T ↑  ( 26 )  
IL4 rs2070874  chr5:132009710 5′ UTR IL4 T-168C ↑  ( 26 )  
IL12 rs3212227  chr5:158742950 3′ UTR IL-12 + 1188 or (3′ UTR A > C) ↑  ( 27 )  
TNF rs1800629  chr6:31543031 Promoter TNF-308 ↑  ( 34 )  
IL6 rs1800797  chr7:22766221 Promoter −597G/A ↓  ( 26 )  
IFNG rs2430561  chr12:68552520 Intronic IFN-γ T + 874A ↓  ( 35 )  
Gene/SNP Position in the genome Position in the gene and type of substitution Trivial name  Effect or association a Reference 
IL10 rs3024505 chr1:206939904 Intronic GWAS on IBD  ( 25 )  
IL10 rs1800872  chr1:206946407 Promoter −592C/A ↓  ( 26 )  
IL10 rs1800896  chr1:206946897 Promoter −1082G/A ↓  ( 27 )  
IL1A rs17561  chr2:113537223 Missense (Ala > Ser) IL1A 4845G > T Risk of multiple sclerosis  ( 28 )  
IL1B rs1143627  chr2:113594387 Promoter (−)31T > C ↑ and↓  ( 29–31 )  
IL1B rs16944  chr2:113594867 Promoter (−)511G > A ↑  ( 30 )  
IL1B rs1143627 IL1B rs16944 Haplotype  chr2:113594387–113594867 Promoter (−)31T > C (−)511G>A Haplotype ↑  ( 29 )  
IL1B rs1143623 chr2:113595829 Promoter IL1B-1473C ↑  ( 29 )  
IL1B rs4848306 chr2:113598107 Promoter (−)3737 G/A ↓  ( 29 )  
IL1RN rs419598  chr2:113887207 Synonymous (Ala > Ala) (Ala23Ala) + 2018T/C Coronary symptoms  ( 32 )  
IL2 rs2069762  chr4:123377980 Promoter IL2-330G ↑  ( 26 )  
IL8 rs4073  chr4:74606024 Promoter −251T/A ↑  ( 26 , 33 )  
IL4 rs2243250  chr5:132009154 Promoter IL-4 C-590T ↑  ( 26 )  
IL4 rs2070874  chr5:132009710 5′ UTR IL4 T-168C ↑  ( 26 )  
IL12 rs3212227  chr5:158742950 3′ UTR IL-12 + 1188 or (3′ UTR A > C) ↑  ( 27 )  
TNF rs1800629  chr6:31543031 Promoter TNF-308 ↑  ( 34 )  
IL6 rs1800797  chr7:22766221 Promoter −597G/A ↓  ( 26 )  
IFNG rs2430561  chr12:68552520 Intronic IFN-γ T + 874A ↓  ( 35 )  

UTR, untranslated region.

a

Reported effect of the variant allele on the gene expression, on protein function or reported association with a disease.

We specifically selected genes for which functional SNPs were previously reported either being non-synonymous or located in promoters or 3′ untranslated regions. Non-synonymous SNPs were included in our analyses due to their potential effect on protein function. Promoter polymorphisms within or close to transcription factor binding sites may alter gene expression and contribute to tumourigenesis. SNPs located in the 3′ untranslated region of the genes may have an effect on microRNA-binding sites. MicroRNAs can downregulate protein translation or even degrade messenger RNA.

Genotyping

The order of DNA samples from cases and controls was randomized on polymerase chain reaction plates in order to ensure that an equal number of cases and controls could be analyzed simultaneously.

Genotyping was performed using an allele-specific polymerase chain reaction-based KASPar SNP genotyping system (KBiosciences, Hoddesdon, UK). Thermocycling was performed according to the manufacturer’s instructions. More information on the method has been given elsewhere ( 36 , 37 ). Detection was performed using an ABI PRISM 7900 HT sequence detection system with SDS 2.2 software (Applied Biosystems, Foster City, CA).

Statistical analysis

The frequency distribution of genotypes was examined for cases and controls. Hardy–Weinberg equilibrium was tested in controls. We used logistic regression for multivariate analyses (using a codominant model) to assess the main effects of the genetic polymorphisms on BCC risk. The primary end point of the analysis was cancer risk, measured with odds ratio (OR) and associated confidence intervals (CIs). All estimates were adjusted for age, gender, center of recruitment and three known risk factors for BCC: skin complexion, sun exposure and skin response to sun exposure. The host factors, skin complexion and skin response to sun exposure were further merged into an additional variable of high (H: light complexion or burns/blisters, respectively), medium (M: medium complexion or mild burns) and low (L: dark complexion or tan/no change) risk. Sun exposure was estimated by taking the mean of eight categorical variables measuring average daily exposure to the sun over the respondents’ lifetimes. The calculated mean was then used to create four categories corresponding to the hours of sun exposure during summer. The four cut-off points were <2.5/2.5 h–3.5/3.6 h–4.5/>4.5 h. Due to the fairly large number of comparisons performed, we applied a study-wise threshold of P = 0.0036 (0.05/14). Thus, P -values will be interpreted in light of the multiple comparisons.

All analyses were done with STATA software (StataCorp, College Station, TX).

Effects of genotyped SNPs in subgroups defined by age and host factors

We analyzed associations of SNPs with BCC risk by grouping cases according to age, skin complexion, sun effect and skin response to sun exposure. Age was divided in four quartiles (<55 years, 55–63 years, 64–72, >72 years). The host factor strata have been described above.

Gene–gene and gene–environment interactions

We analyzed all the possible pair-wise interactions between SNPs (gene–gene; G–G) and between SNPs and two host factors (skin complexion and skin response to sun exposure) and one environmental factor (sun exposure) (gene–environment; G–E). For these analyses, we included also the three SNPs previously reported ( 21 ). Assessment of G–G and G–E interactions was carried out using multifactor dimensionality reduction (MDR). The details of MDR are described elsewhere ( 38 , 39 ). Briefly, MDR is a data reduction approach that seeks to identify combinations of multilocus genotypes and discrete environmental factors that are associated with either high risk or low risk of disease. MDR defines a single variable that incorporates information from several loci and/or environmental factors. This new variable can be evaluated for its ability to classify and predict outcome risk status using cross-validation and permutation testing. The MDR software is open source and freely available from http://www.epistasis.org .

Haplotype reconstruction and analysis

For the IL1B and IL10 genes, haplotype blocks were constructed from the control genotyping data using Phase software ( 40 ) and SNP tool ( http://www.dkfz.de/de/molgen_epidemiology/tools/SNPtool.html ) ( 41 ). The following cut-off values were used: MAF >5%, Hardy–Weinberg equilibrium P ≥ 0.01 and 75% of non-missing genotypes. Unconditional logistic regression adjusted for age (continuous), gender, skin complexion, sun exposure and skin response to sun exposure was used to calculate risk estimates. The most frequent haplotype was set as the reference, whereas haplotypes with a frequency <0.05 were declared as rare haplotypes and combined.

Results

In this study, 529 cases with BCC and 532 controls were recruited from Hungary, Romania and Slovakia. The mean age at diagnosis of cases (237 men and 292 women) was 64.8 (±10.3) years (median 67; 25–75% percentile 58–73) and that of controls (274 men and 259 women) was 60.0 (±11.8) years (median 61; 25–75% percentile 52–70). Although the complexion and nature of skin response to sun exposure showed association with BCC risk, the average cumulative sun exposure was not associated with the risk. Baseline characteristics of cases and controls are reported in Table II .

Table II.

Distribution of BCC cases and controls for different characteristics

Variable Cases (%) Controls (%) P -value a 
Male 237 (44.8) 274 (51.4)  
Female 292 (55.2) 259 (48.6)  
Median age (25–75% percentile) 67 (58–73) 61 (52–70)  
Nationality 
    Hungarian 208 (39) 283 (53)  
    Romanian 125 (24) 118 (22)  
    Slovak 184 (35) 121 (23)  
    Others 12 (2) 10 (2)  
Skin complexion   <0.0001 
    Light 280 (53) 212 (40)  
    Medium 233 (44) 261 (49)  
    Dark 16 (3) 59 (11)  
Skin response to sun exposure   0.04 
    Blistered/burnt 185 (35) 141 (26)  
    Mild burn 169 (32) 159 (30)  
    Tanning/no change 175 (33) 232 (44)  
Average cumulative sun exposure (hours per day during summer) b 0.59 
    <2.4 129 (24) 137 (26)  
    2.5–3.5 151 (29) 153 (29)  
    3.6–4.5 135 (26) 111 (21)  
    >4.5 112 (21) 125 (23)  
Variable Cases (%) Controls (%) P -value a 
Male 237 (44.8) 274 (51.4)  
Female 292 (55.2) 259 (48.6)  
Median age (25–75% percentile) 67 (58–73) 61 (52–70)  
Nationality 
    Hungarian 208 (39) 283 (53)  
    Romanian 125 (24) 118 (22)  
    Slovak 184 (35) 121 (23)  
    Others 12 (2) 10 (2)  
Skin complexion   <0.0001 
    Light 280 (53) 212 (40)  
    Medium 233 (44) 261 (49)  
    Dark 16 (3) 59 (11)  
Skin response to sun exposure   0.04 
    Blistered/burnt 185 (35) 141 (26)  
    Mild burn 169 (32) 159 (30)  
    Tanning/no change 175 (33) 232 (44)  
Average cumulative sun exposure (hours per day during summer) b 0.59 
    <2.4 129 (24) 137 (26)  
    2.5–3.5 151 (29) 153 (29)  
    3.6–4.5 135 (26) 111 (21)  
    >4.5 112 (21) 125 (23)  
a

P -value is for the effect of factor.

b

Sun exposure estimated by calculating a mean of eight categorical variables measuring average daily exposure to the sun over the respondents’ lifetimes. For two cases and six controls, exposure information was not available.

Genotyping success rates and quality control

For the 14 SNPs newly genotyped, genotype success rate for cases and controls was >95%. Blinded duplicate samples (6.2%) included for quality control showed >99% genotype concordance. The genotype frequencies for all SNPs in controls were in accordance with Hardy–Weinberg equilibrium and any deviation from the expected was not statistically significant (data not shown).

Main effects of genotyped SNPs

The distribution of the genotypes and their ORs for association with BCC risk are shown in Table III . The genotype frequencies were not found to be significantly different between cases and controls for all the newly reported SNPs.

Table III.

Associations of selected genes and SNPs with BCC risk

Genotypes  Position a  Cases b  Controls b  OR (95% CI) c P -value  Ptrend 
IL8 rs4073  
    TT  167 148   0.153 
    TA  233 247 0.86 (0.64–1.16) 0.319  
    AA  103 116 0.82 (0.57–1.18) 0.279  
    TA + AA    0.85 (0.64–1.12) 0.241  
IL1A rs17561  
    CC  243 234   0.4376 
    CA  215 224 0.98 (0.74–1.28) 0.87  
    AA  43 48 0.86 (0.54–1.39) 0.545  
    CA + AA    0.96 (0.74–1.24) 0.741  
IL1RN rs419598  
    TT  267 263   0.8261 
    TC  149 185 0.79 (0.59–1.05) 0.106  
    CC  31 20 1.58 (0.86–2.91) 0.142  
    TC + CC    0.87 (0.66–1.14) 0.307  
IL1B rs16944 d 
    CC  230 217   0.1511 
    CT  210 224 0.9 (0.69–1.19) 0.479  
    TT  52 65 0.72 (0.47–1.1) 0.127  
    CT + TT    0.86 (0.66–1.12) 0.263  
IL1B rs1143627  
    TT  229 212   0.1634 
    TC  192 205 0.88 (0.66–1.17) 0.364  
    CC  50 60 0.73 (0.47–1.13) 0.16  
    TC + CC    0.84 (0.64–1.1) 0.209  
IL1B rs1143623 
    GG  269 271    
    GC  202 204 1.01 (0.77–1.32) 0.971 0.207 
    CC  36 44 0.77 (0.47–1.27) 0.311  
    GC + CC    0.96 (0.74–1.25) 0.773  
IL1B rs4848306 
    GG  140 161   0.576 
    GA  261 259 1.2 (0.89–1.62) 0.237  
    AA  103 95 1.3 (0.89–1.89) 0.179  
    GA + AA    1.23 (0.92–1.63) 0.163  
TNF rs1800629  
    GG  358 390   0.0216 
    GA  128 117 1.2 (0.89–1.63) 0.235  
    AA  20 2.47 (1.05–5.77) 0.038  
    GA + AA    1.29 (0.96–1.72) 0.087  
IL6 rs1800797 d 
    GG  200 168   0.1754 
    GA  218 261 0.64 (0.48–0.85) 0.002  
    AA  89 86 0.76 (0.52–1.11) 0.153  
    GA + AA    0.67 (0.51–0.88) 0.004  
IL10 rs3024505 
    CC  379 355   0.049 
    CT  123 147 0.82 (0.61–1.1) 0.182  
    TT  10 15 0.55 (0.23–1.27) 0.16  
    CT + TT    0.79 (0.59–1.05) 0.104  
IL10 rs1800872  
    CC  271 268   0.6035 
    CA  205 216 0.98 (0.75–1.28) 0.883  
    AA  25 27 0.76 (0.42–1.38) 0.362  
    CA + AA    0.95 (0.73–1.23) 0.713  
IL10 rs1800896 d 
    AA  152 158   0.3091 
    AG  224 241 0.94 (0.7–1.28) 0.71  
    GG  100 83 1.19 (0.81–1.75) 0.372  
    AG + GG    1.01 (0.76–1.34) 0.955  
IL2 rs2069762  
    TT  215 221   0.9219 
    TG  223 221 1 (0.76–1.32) 0.995  
    GG  58 60 0.95 (0.62–1.46) 0.823  
    TG + GG    0.99 (0.76–1.29) 0.944  
IL4 rs2070874  
    CC  347 358   0.6478 
    CT  141 145 1.05 (0.78–1.4) 0.751  
    TT  16 12 1.3 (0.58–2.91) 0.525  
    CT + TT    1.07 (0.81–1.41) 0.645  
IL4 rs2243250  
    CC  348 350   0.8873 
    CT  141 151 0.97 (0.73–1.29) 0.838  
    TT  18 13 1.34 (0.62–2.88) 0.46  
    CT + TT    1 (0.76–1.32) 0.997  
IFNG rs2430561  
    AA  149 156   0.5949 
    AT  230 217 1.11 (0.82–1.51) 0.508  
    TT  100 118 0.85 (0.59–1.22) 0.372  
    AT + TT    1.01 (0.76–1.35) 0.924  
IL12 rs3212227  
    AA  308 315   0.6684 
    AC  169 158 1.21 (0.91–1.6) 0.194  
    CC  21 22 0.95 (0.5–1.82) 0.877  
    AC + CC    1.17 (0.89–1.54) 0.251  
Genotypes  Position a  Cases b  Controls b  OR (95% CI) c P -value  Ptrend 
IL8 rs4073  
    TT  167 148   0.153 
    TA  233 247 0.86 (0.64–1.16) 0.319  
    AA  103 116 0.82 (0.57–1.18) 0.279  
    TA + AA    0.85 (0.64–1.12) 0.241  
IL1A rs17561  
    CC  243 234   0.4376 
    CA  215 224 0.98 (0.74–1.28) 0.87  
    AA  43 48 0.86 (0.54–1.39) 0.545  
    CA + AA    0.96 (0.74–1.24) 0.741  
IL1RN rs419598  
    TT  267 263   0.8261 
    TC  149 185 0.79 (0.59–1.05) 0.106  
    CC  31 20 1.58 (0.86–2.91) 0.142  
    TC + CC    0.87 (0.66–1.14) 0.307  
IL1B rs16944 d 
    CC  230 217   0.1511 
    CT  210 224 0.9 (0.69–1.19) 0.479  
    TT  52 65 0.72 (0.47–1.1) 0.127  
    CT + TT    0.86 (0.66–1.12) 0.263  
IL1B rs1143627  
    TT  229 212   0.1634 
    TC  192 205 0.88 (0.66–1.17) 0.364  
    CC  50 60 0.73 (0.47–1.13) 0.16  
    TC + CC    0.84 (0.64–1.1) 0.209  
IL1B rs1143623 
    GG  269 271    
    GC  202 204 1.01 (0.77–1.32) 0.971 0.207 
    CC  36 44 0.77 (0.47–1.27) 0.311  
    GC + CC    0.96 (0.74–1.25) 0.773  
IL1B rs4848306 
    GG  140 161   0.576 
    GA  261 259 1.2 (0.89–1.62) 0.237  
    AA  103 95 1.3 (0.89–1.89) 0.179  
    GA + AA    1.23 (0.92–1.63) 0.163  
TNF rs1800629  
    GG  358 390   0.0216 
    GA  128 117 1.2 (0.89–1.63) 0.235  
    AA  20 2.47 (1.05–5.77) 0.038  
    GA + AA    1.29 (0.96–1.72) 0.087  
IL6 rs1800797 d 
    GG  200 168   0.1754 
    GA  218 261 0.64 (0.48–0.85) 0.002  
    AA  89 86 0.76 (0.52–1.11) 0.153  
    GA + AA    0.67 (0.51–0.88) 0.004  
IL10 rs3024505 
    CC  379 355   0.049 
    CT  123 147 0.82 (0.61–1.1) 0.182  
    TT  10 15 0.55 (0.23–1.27) 0.16  
    CT + TT    0.79 (0.59–1.05) 0.104  
IL10 rs1800872  
    CC  271 268   0.6035 
    CA  205 216 0.98 (0.75–1.28) 0.883  
    AA  25 27 0.76 (0.42–1.38) 0.362  
    CA + AA    0.95 (0.73–1.23) 0.713  
IL10 rs1800896 d 
    AA  152 158   0.3091 
    AG  224 241 0.94 (0.7–1.28) 0.71  
    GG  100 83 1.19 (0.81–1.75) 0.372  
    AG + GG    1.01 (0.76–1.34) 0.955  
IL2 rs2069762  
    TT  215 221   0.9219 
    TG  223 221 1 (0.76–1.32) 0.995  
    GG  58 60 0.95 (0.62–1.46) 0.823  
    TG + GG    0.99 (0.76–1.29) 0.944  
IL4 rs2070874  
    CC  347 358   0.6478 
    CT  141 145 1.05 (0.78–1.4) 0.751  
    TT  16 12 1.3 (0.58–2.91) 0.525  
    CT + TT    1.07 (0.81–1.41) 0.645  
IL4 rs2243250  
    CC  348 350   0.8873 
    CT  141 151 0.97 (0.73–1.29) 0.838  
    TT  18 13 1.34 (0.62–2.88) 0.46  
    CT + TT    1 (0.76–1.32) 0.997  
IFNG rs2430561  
    AA  149 156   0.5949 
    AT  230 217 1.11 (0.82–1.51) 0.508  
    TT  100 118 0.85 (0.59–1.22) 0.372  
    AT + TT    1.01 (0.76–1.35) 0.924  
IL12 rs3212227  
    AA  308 315   0.6684 
    AC  169 158 1.21 (0.91–1.6) 0.194  
    CC  21 22 0.95 (0.5–1.82) 0.877  
    AC + CC    1.17 (0.89–1.54) 0.251  
a

Position of SNP on chromosomes, in base pairs referred to UCSC Genome Browser on Human March 2006 Assembly. In parentheses, we report the position of the polymorphism with respect to the gene.

b

Numbers may not add up to 100% of subjects due to genotyping failure. All samples that did not give a reliable result in the first round of genotyping were resubmitted to up to two additional rounds of genotyping. Data points that were still not filled after this procedure were left blank.

c

Adjusted for age, gender, country, sun effect, skin complexion and skin response to sun exposure.

d

These polymorphisms have been already reported ( 21 ) .

G–G, G–E interactions and subgroup analysis

For all the 17 SNPs, we have thoroughly analyzed all the possible pair-wise G–G and G–E interactions using the MDR method with 10-fold cross-validation. This did not reveal any statistically significant interaction (the best model described had cross-validation consistency 7/10, test balance accuracy 0.58, OR 1.99, 95% CI 0.92–4.33, P -value = 0.08). Analyzing the data in subgroups stratifying for age, gender and the host factors (skin complexion, sun exposure and skin response to sun exposure), we found that two polymorphic variants had a study-wise ( P < 0.0036) significant association with BCC risk. We found that, in the group of subjects more prone to skin burns, carriers of at least one copy of the G allele of the TNF rs1800629 SNP had an increased risk of BCC (OR = 2.40, 95% CI 1.38–4.16, P = 0.0005). Moreover, in subjects less prone to sunburns, we observed that carriers of the C allele of the IL1B rs1143627 SNP showed a decreased risk of the disease (OR = 0.53, 95% CI 0.34–0.82, P = 0.0019). Table IV shows the association of rs1143627 and rs1800629 with risk of BCC in subjects grouped by skin response to sun exposure. All the other polymorphisms, considering the various strata, did not show any statistically significant association. Supplementary Tables 14 , available at Carcinogenesis Online, show the analysis of each polymorphism of this study considering the various subgroups of age- and sun-related exposures.

Table IV.

Association of rs1143627 and rs1800629 risk of BCC and skin response to sun exposure

  Controls a  Cases a Total  OR (95% CI) b P -value  Ptrend 
IL1B , rs1143627        
    Blistered/burnt       
        TT 59 85 144   0.5083 
        TC 53 64 117 0.83 (0.5–1.36) 0.454  
        CC 14 17 31 0.83 (0.38–1.81) 0.635  
        TC + CC    0.83 (0.52–1.32) 0.426  
        Total 126 166 292    
   Mild burn       
        TT 67 64 131   0.3838 
        TC 56 66 122 1.17 (0.71–1.93) 0.535  
        CC 18 22 40 1.23 (0.6–2.52) 0.568  
        TC + CC    1.19 (0.74–1.89) 0.474  
        Total 141 152 293    
   Tanning/no  change       
        TT 83 80 163   0.0019 
        TC 91 53 144 0.59 (0.37–0.94) 0.026  
        CC 28 37 0.33 (0.15–0.74) 0.007  
        TC + CC    0.53 (0.34–0.82) 0.005  
        Total 202 142 344    
TNF, rs1800629       
    Blistered/burnt       
        GG 113 120 233   0.0005 
        GA 23 48 71 2.02 (1.15–3.55) 0.015  
        AA    
        GA + AA    2.4 (1.38–4.16) 0.002  
        Total 136 177 313    
    Mild burn       
        GG 112 110 222   0.4677 
        GA 41 45 86 1.13 (0.68–1.86) 0.643  
        AA 1.92 (0.44–8.31) 0.383  
        GA + AA    1.18 (0.72–1.91) 0.512  
        Total 156 160 316    
   Tanning/no  change       
        GG 158 119 277   0.9473 
        GA 52 33 85 0.84 (0.51–1.39) 0.506  
        AA 11 1.57 (0.46–5.29) 0.471  
        GA + AA    0.91 (0.57–1.46) 0.69  
        Total 215 158 373    
  Controls a  Cases a Total  OR (95% CI) b P -value  Ptrend 
IL1B , rs1143627        
    Blistered/burnt       
        TT 59 85 144   0.5083 
        TC 53 64 117 0.83 (0.5–1.36) 0.454  
        CC 14 17 31 0.83 (0.38–1.81) 0.635  
        TC + CC    0.83 (0.52–1.32) 0.426  
        Total 126 166 292    
   Mild burn       
        TT 67 64 131   0.3838 
        TC 56 66 122 1.17 (0.71–1.93) 0.535  
        CC 18 22 40 1.23 (0.6–2.52) 0.568  
        TC + CC    1.19 (0.74–1.89) 0.474  
        Total 141 152 293    
   Tanning/no  change       
        TT 83 80 163   0.0019 
        TC 91 53 144 0.59 (0.37–0.94) 0.026  
        CC 28 37 0.33 (0.15–0.74) 0.007  
        TC + CC    0.53 (0.34–0.82) 0.005  
        Total 202 142 344    
TNF, rs1800629       
    Blistered/burnt       
        GG 113 120 233   0.0005 
        GA 23 48 71 2.02 (1.15–3.55) 0.015  
        AA    
        GA + AA    2.4 (1.38–4.16) 0.002  
        Total 136 177 313    
    Mild burn       
        GG 112 110 222   0.4677 
        GA 41 45 86 1.13 (0.68–1.86) 0.643  
        AA 1.92 (0.44–8.31) 0.383  
        GA + AA    1.18 (0.72–1.91) 0.512  
        Total 156 160 316    
   Tanning/no  change       
        GG 158 119 277   0.9473 
        GA 52 33 85 0.84 (0.51–1.39) 0.506  
        AA 11 1.57 (0.46–5.29) 0.471  
        GA + AA    0.91 (0.57–1.46) 0.69  
        Total 215 158 373    

Study-wise significant association ( P < 0.0029) are written in bold.

a

Numbers may not add up to 100% of subjects due to genotyping failure. All samples that did not give a reliable result in the first round of genotyping were resubmitted to up to two additional rounds of genotyping. Data points that were still not filled after this procedure were left blank.

b

Adjusted for country, age and gender.

Haplotype analysis

Haplotype analysis was performed for IL1B and IL10 genes. We found that the haplotype rs3024505-rs1800872-rs1800896:T_C_G of the IL10 gene was associated with a decreased risk of BCC in comparison with the reference haplotype rs3024505-rs1800872-rs1800896: C_C_G (OR = 0.67, 95% CI 0.51–0.90, P = 0.007). There was no association for the IL1B haplotypes with disease risk. Table V reports the finding for IL10 haplotypes, whereas Supplementary Table 5 , available at Carcinogenesis Online, shows the associations between the IL1B haplotypes and BCC risk.

Table V.

IL10 haplotypes

rs3024505 rs1800872 rs1800896 Cases Controls  OR (95% CI) a P -value  
321 264   
337 350 0.83 (0.66–1.05) 0.118 
255 271 0.78 (0.61–1.00) 0.05 
145 181 0.67 (0.51–0.90) 0.007 
rs3024505 rs1800872 rs1800896 Cases Controls  OR (95% CI) a P -value  
321 264   
337 350 0.83 (0.66–1.05) 0.118 
255 271 0.78 (0.61–1.00) 0.05 
145 181 0.67 (0.51–0.90) 0.007 
a

Adjusted for age, gender, country, sun effect, skin complexion and skin response to sun exposure.

Discussion

The risk for development of BCC is mainly associated with environmental factors (especially sun exposure) but also with genetic factors ( 21 , 42–45 ) In this report, we selected 11 SNPs known to alter the gene function and we genotyped them in 529 BCC cases and 532 hospital-based controls. In addition, three previously reported polymorphisms were added to the study in order to include them in all the interaction analyses performed. We found that none of the newly tested SNPs were independently associated with the disease susceptibility. These results are in concordance with other reports in which genetic variants in inflammatory genes were evaluated ( 46 , 47 ).

Sun exposure is the major etiological factor in the genesis of BCC; however, many studies have suggested that risk involves an interplay between genetic (SNPs), host (skin complexion, skin response to sun exposure) and environmental (sun exposure) factors ( 21 , 24 , 43 , 45 , 48–55 ). In fact, in the group of subjects more prone to skin burns, carriers of the A allele of the rs1800629 of the TNF -alpha gene had an increased risk of BCC ( P = 0.0005), while in subjects less prone to sunburns carriers of the C allele of IL1B , rs1143627 SNP showed a decreased risk of the disease ( P = 0.0019). The P -value for heterogeneity was 0.01 for the TNF -alpha SNP and 0.3 for IL1B .

In a recent study, rs3024505, which is situated in the IL10 gene promoter, was associated with increased risk of ulcerative colitis and Crohn’s disease ( 25 ). We found that the variant allele (T) was associated with decreased risk of BCC in subjects with low sun exposure. This association did not pass correction for multiple testing but was very close to it ( P = 0.003).

The two significantly associated polymorphisms are thought to be functional. The SNP at position −308 (rs1800629) is situated in the promoter region of the TNF gene. The presence of guanine defines the common variant (a.k.a. TNF1); the presence of adenine defines the less common variant (a.k.a. TNF2). Functional studies of the TNF2 allele have shown both higher constitutional and inducible expression ( 34 ). In our study, the allele, which is considered to increase the expression of the gene, is associated with increased risk of BCC in those individuals that are more prone to sunburns. We can therefore speculate that having the genetic pro-inflammatory allele and the prone to burning phenotype could identify individuals at higher disease risk.

SNP rs1143627 (a.k.a. −31T>C) of IL1B is situated in a promoter region within the TATA box. It has been extensively studied ( 29–31 ) with contrasting results and it seems to markedly affect DNA–protein interactions in vitro especially if evaluated in the haplotype context ( 29 ). El Omar et al. ( 30 ) suggested that in the stomach the wild-type allele (T) is associated with a 5-fold elevated binding activity with the transcription initiation factors and therefore the variant allele (C) is considered to be associated with a lower expression level of the gene. Chen et al. ( 29 ) found that the variant alleles in combination gave higher IL1B transcription in cell lines, and when analyzed separately, the C allele of 31T>C gave lower transcription, whereas Lind et al. ( 56 ) analyzing the −31 polymorphism alone found lower response from the −31 variant allele, while for Hart et al. ( 31 ), it is the C allele that in the lung is more induced by carcinogens than the T allele. The haplotype context could be a possible reason for the discrepancies in the literature. We could speculate that the T allele works in tandem with the phenotype non-prone to sunburn to define lower disease risk individuals.

In both cases the ‘pro-inflammatory allele’ is associated with an increased risk and this is complete concordance with our biological a priori hypothesis that polymorphisms increasing the inflammatory process would also increase the risk of developing BCC.

Finally, we observed an association with decreased risk of BCC for carriers of the rs3024505-rs1800872-rs1800896:T_C_G haplotype of the IL10 gene. This association did not pass the multiple test comparison threshold, although it was quite close to it ( P = 0.007). The associated haplotype differs from the reference only for the first polymorphism: rs3024505 (T allele versus C). Carriers of the T allele showed a decreased risk of BCC in subjects with a low sun exposure, as previously mentioned. Therefore, it is interesting to note the association of the T allele alone or in the haplotype context, with decreased risk of BCC, is consistent throughout the study.

Our study had some potential limitations, such as the moderate sample size and the fairly high number of tests. Another possible limitations of this study are the fact that sun exposure was collected after diagnosis and this could lead to recollection bias. Correcting for the number of the tests performed leads to a study-wise P = 0.0036. Using this threshold, the association between the two SNPs and the disease risk remains significant. It has to be noted that these results have to be taken with caution due to the fact that the heterogeneity test resulted to be not significant for IL1B and only weakly significant for TNF -alpha.

In this study, we had >80% power to detect a possible association with a minimum OR of 1.28 for an SNP with minor allele frequency of 0.45 in the controls assuming alpha = 0.05, two-sided test and a codominant model.

In conclusion, we have observed that the interplay of a host factor and three functional polymorphisms in three cytokine genes could modulate the risk of BCC. Moreover, we observed an association with an IL10 haplotype with decreased risk of BCC. However, these results have to be taken with caution due to the relatively large number of comparisons done and have to be replicated in a larger independent study.

Supplementary material

Supplementary Tables 15 can be found at http://carcin.oxfordjournals.org/ .

Abbreviations

    Abbreviations
  • BCC

    basal call carcinoma

  • CI

    confidence interval

  • MDR

    multifactor dimensionality reduction

  • OR

    odds ratio

  • SNP

    single-nucleotide polymorphism

Conflict of Interest Statement: None declared.

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