OMICS A Journal of Integrative Biology Volume 19, Number 4, 2015 ª Mary Ann Liebert, Inc. DOI: 10.1089/omi.2014.0121
A Systematic Gene–Gene and Gene–Environment Interaction Analysis of DNA Repair Genes XRCC1, XRCC2, XRCC3, XRCC4, and Oral Cancer Risk Cheng-Hong Yang,1 Yu-Da Lin,1 Ching-Yui Yen,2,3 Li-Yeh Chuang,4 and Hsueh-Wei Chang 5–8
Abstract
Oral cancer is the sixth most common cancer worldwide with a high mortality rate. Biomarkers that anticipate susceptibility, prognosis, or response to treatments are much needed. Oral cancer is a polygenic disease involving complex interactions among genetic and environmental factors, which require multifaceted analyses. Here, we examined in a dataset of 103 oral cancer cases and 98 controls from Taiwan the association between oral cancer risk and the DNA repair genes X-ray repair cross-complementing group (XRCCs) 1–4, and the environmental factors of smoking, alcohol drinking, and betel quid (BQ) chewing. We employed logistic regression, multifactor dimensionality reduction (MDR), and hierarchical interaction graphs for analyzing gene–gene (G · G) and gene–environment (G · E) interactions. We identified a significantly elevated risk of the XRCC2 rs2040639 heterozygous variant among smokers [adjusted odds ratio (OR) 3.7, 95% confidence interval (CI) = 1.1–12.1] and alcohol drinkers [adjusted OR = 5.7, 95% CI = 1.4–23.2]. The best two-factor based G · G interaction of oral cancer included the XRCC1 rs1799782 and XRCC2 rs2040639 [OR = 3.13, 95% CI = 1.66– 6.13]. For the G · E interaction, the estimated OR of oral cancer for two (drinking–BQ chewing), three (XRCC1–XRCC2–BQ chewing), four (XRCC1–XRCC2–age–BQ chewing), and five factors (XRCC1–XRCC2– age–drinking–BQ chewing) were 32.9 [95% CI = 14.1–76.9], 31.0 [95% CI = 14.0–64.7], 49.8 [95% CI = 21.0– 117.7] and 82.9 [95% CI = 31.0–221.5], respectively. Taken together, the genotypes of XRCC1 rs1799782 and XRCC2 rs2040639 DNA repair genes appear to be significantly associated with oral cancer. These were enhanced by exposure to certain environmental factors. The observations presented here warrant further research in larger study samples to examine their relevance for routine clinical care in oncology. Introduction
O
ral cancer is the sixth most common cancer worldwide and causes high mortality because it is commonly ignored at the early stage (Warnakulasuriya, 2009). Three common environmental exposures such as betel quid (BQ) chewing, cigarette smoking, and alcohol drinking were reported to be highly associated with oral cancer in Taiwan (Ko et al., 1995). These factors were also reported to induce DNA damage and apoptosis. For example, alcohol was reported to induce oxidative DNA damage like acetaldehyde-derived DNA adducts of esophageal (Yukawa et al., 2014) and oral cells (Balbo et al., 2012). Effects of BQ
chewing were reported to induce mitochondrial DNA mutation (Lee et al., 2001; Tan et al., 2003) and oxidative DNA damage (Chen et al., 2002). Cigarette smoke was also reported to generate toxic carbonyl compounds (Fujioka and Shibamoto, 2006). Its condensate was reported to induce DNA damage in oropharyngeal mucosa biopsies (Baumeister et al., 2009) and lung cells (Nyunoya et al., 2014). Accordingly, all tested environmental factors, alcohol, BQ, and smoking contributed to oral carcinogenesis (Chiang et al., 2013), moderately depending on the extend of DNA damage effects. Recently, growing evidence indicated that single nucleotide polymorphisms (SNPs) of DNA repair genes were associated with oral cancer susceptibility (Gal et al., 2005;
1
Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan. Department of Oral and Maxillofacial Surgery, Chi-Mei Medical Center, Tainan, Taiwan. School of Dentistry, Taipei Medical University, Taipei, Taiwan. 4 Department of Chemical Engineering and Institute of Biotechnology and Chemical Engineering, I-Shou University, Kaohsiung, Taiwan. 5 Institute of Medical Science and Technology, National Sun Yat-sen University, Kaohsiung, Taiwan. 6 Cancer Center, Kaohsiung Medical University Hospital, 7Research Center of Environmental Medicine, and 8Department of Biomedical Science and Environmental Biology, Kaohsiung Medical University, Kaohsiung, Taiwan. 2 3
238
GENE–ENVIRONMENT INTERACTION IN ORAL CANCER
Yang et al., 2012; Yen et al., 2008). For example, SNPs of DNA repair genes such as X-ray repair cross-complementing group 1 (XRCC1) (Wu et al., 2014; Zhang et al., 2013), XRCC2 (Romanowicz-Makowska et al., 2012), XRCC3 (Tsai et al., 2014), and XRCC4 (Chiu et al., 2008; Tseng et al., 2008) were reported to be associated with oral cancer. However, most of these studies focused on the single SNP effect or single SNP–environment effect. The complex gene– gene (G · G) and gene–environment (G · E) interactions associated with oral cancer are less addressed. G · G and G · E interactions were well-established to detect the epistasis which involved a complex association between disease/cancer related genes in case-control and family-based association studies (Chang et al., 2008; Chen et al., 2013, 2014; Chuang et al., 2012; Lin et al., 2009; Moore et al., 2010; Steen, 2012; Yang et al., 2011). This epistasis assists us to understand the causes of disease and cancer. Multifactor dimensionality reduction (MDR) represents an epistasis detection approach (Hahn et al., 2003; Ritchie et al., 2001) and several improved MDRs were suggested to detect particular data sets such as imbalanced data sets (Yang et al., 2013). MDR-ER (Yang et al., 2013) allowed that G · G interaction detection works on imbalanced data sets without the need of balanced demographic approaches. It can provide strong analytical abilities for imbalanced data sets for the detection of possible multiple factors interactions. In this study, we examined the G · G and G · E effect by an improved MDR (MDR-ER) by tandem consideration of genetic factors (four SNPs of XRCCs 1–4) and environmental factors (gender, age, smoking, alcohol drinking, and BQ chewing) in a dataset of 103 oral cancer cases and 98 controls from Taiwan. Risk-ranking of oral cancer was identified in terms of the G · G and G · E interactions. Methods Multifactor dimensionality reduction (MDR)
The nonparametric and model-free MDR method is widely used in the investigation of G · G and G · E interactions (Ritchie et al., 2001). Nonlinear interactions among multiple factors such as genetic and environmental factors can effectively discriminate nonsignificant effects for each individual factor (Ritchie et al., 2003). MDR is a data reduction method that searches for multifactor combinations associated with either high or low risks of oral cancer. Therefore, several genetic and environmental factors are classified as being of high and low risk. A high-order G · G interaction for the ability to classify and predict outcome risk status can be evaluated by cross-validation (CV) and permutation testing of the data space is reduced to a two-way contingency table. Supposedly the N SNPs are considered as a case-control data set, and the M is the maximum order of G · G and G · E interactions (i.e., M £ N). Let m be the number of order G · G and G · E interactions (m £ M). The procedure to use MDRs for detecting the best m-way of G · G and G · E interaction models is illustrated in Figure 1. The MDR procedure can be divided into the following eight steps: Step 1. Divide data set into a k sub data set and select a ith sub data set as the test data set and the other remaining sub data set as the training data set.
239
Step 2. A set of m factors (loci) is consisted from all factors. Step 3. All possible combinations of genotypes in m factors are represented in m-dimensional space (multifactor cells). Equation 1 is defined as a multifactor cell that includes a set of m genetic and environmental factors. L ¼ fl1 , l2 , l3 , . . . , lm g
(Eq:1)
Step 4. High or low risk is defined in each multifactor cell. Equation 2 is used to compute the ratio between case and control and the symbol u() is used to determine a score of ‘‘1’’ if all elements l in L match a sample in P or N, otherwise given a score ‘‘0’’. Each multifactor cell is labelled as ‘H’ or ‘L’ symbol. The ‘H’ indicates the high-risk group if the ratio in multifactor cell meets or exceeds a threshold, while ‘L’ indicates a low-risk group. P
f (L) ¼
+j ¼ 1 u(L, Pj )
(Eq:2)
N
+j ¼ 1 u(L, Nj )
where
u(L, A) ¼
8
A Systematic Gene–Gene and Gene–Environment Interaction Analysis of DNA Repair Genes XRCC1, XRCC2, XRCC3, XRCC4, and Oral Cancer Risk Cheng-Hong Yang,1 Yu-Da Lin,1 Ching-Yui Yen,2,3 Li-Yeh Chuang,4 and Hsueh-Wei Chang 5–8
Abstract
Oral cancer is the sixth most common cancer worldwide with a high mortality rate. Biomarkers that anticipate susceptibility, prognosis, or response to treatments are much needed. Oral cancer is a polygenic disease involving complex interactions among genetic and environmental factors, which require multifaceted analyses. Here, we examined in a dataset of 103 oral cancer cases and 98 controls from Taiwan the association between oral cancer risk and the DNA repair genes X-ray repair cross-complementing group (XRCCs) 1–4, and the environmental factors of smoking, alcohol drinking, and betel quid (BQ) chewing. We employed logistic regression, multifactor dimensionality reduction (MDR), and hierarchical interaction graphs for analyzing gene–gene (G · G) and gene–environment (G · E) interactions. We identified a significantly elevated risk of the XRCC2 rs2040639 heterozygous variant among smokers [adjusted odds ratio (OR) 3.7, 95% confidence interval (CI) = 1.1–12.1] and alcohol drinkers [adjusted OR = 5.7, 95% CI = 1.4–23.2]. The best two-factor based G · G interaction of oral cancer included the XRCC1 rs1799782 and XRCC2 rs2040639 [OR = 3.13, 95% CI = 1.66– 6.13]. For the G · E interaction, the estimated OR of oral cancer for two (drinking–BQ chewing), three (XRCC1–XRCC2–BQ chewing), four (XRCC1–XRCC2–age–BQ chewing), and five factors (XRCC1–XRCC2– age–drinking–BQ chewing) were 32.9 [95% CI = 14.1–76.9], 31.0 [95% CI = 14.0–64.7], 49.8 [95% CI = 21.0– 117.7] and 82.9 [95% CI = 31.0–221.5], respectively. Taken together, the genotypes of XRCC1 rs1799782 and XRCC2 rs2040639 DNA repair genes appear to be significantly associated with oral cancer. These were enhanced by exposure to certain environmental factors. The observations presented here warrant further research in larger study samples to examine their relevance for routine clinical care in oncology. Introduction
O
ral cancer is the sixth most common cancer worldwide and causes high mortality because it is commonly ignored at the early stage (Warnakulasuriya, 2009). Three common environmental exposures such as betel quid (BQ) chewing, cigarette smoking, and alcohol drinking were reported to be highly associated with oral cancer in Taiwan (Ko et al., 1995). These factors were also reported to induce DNA damage and apoptosis. For example, alcohol was reported to induce oxidative DNA damage like acetaldehyde-derived DNA adducts of esophageal (Yukawa et al., 2014) and oral cells (Balbo et al., 2012). Effects of BQ
chewing were reported to induce mitochondrial DNA mutation (Lee et al., 2001; Tan et al., 2003) and oxidative DNA damage (Chen et al., 2002). Cigarette smoke was also reported to generate toxic carbonyl compounds (Fujioka and Shibamoto, 2006). Its condensate was reported to induce DNA damage in oropharyngeal mucosa biopsies (Baumeister et al., 2009) and lung cells (Nyunoya et al., 2014). Accordingly, all tested environmental factors, alcohol, BQ, and smoking contributed to oral carcinogenesis (Chiang et al., 2013), moderately depending on the extend of DNA damage effects. Recently, growing evidence indicated that single nucleotide polymorphisms (SNPs) of DNA repair genes were associated with oral cancer susceptibility (Gal et al., 2005;
1
Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan. Department of Oral and Maxillofacial Surgery, Chi-Mei Medical Center, Tainan, Taiwan. School of Dentistry, Taipei Medical University, Taipei, Taiwan. 4 Department of Chemical Engineering and Institute of Biotechnology and Chemical Engineering, I-Shou University, Kaohsiung, Taiwan. 5 Institute of Medical Science and Technology, National Sun Yat-sen University, Kaohsiung, Taiwan. 6 Cancer Center, Kaohsiung Medical University Hospital, 7Research Center of Environmental Medicine, and 8Department of Biomedical Science and Environmental Biology, Kaohsiung Medical University, Kaohsiung, Taiwan. 2 3
238
GENE–ENVIRONMENT INTERACTION IN ORAL CANCER
Yang et al., 2012; Yen et al., 2008). For example, SNPs of DNA repair genes such as X-ray repair cross-complementing group 1 (XRCC1) (Wu et al., 2014; Zhang et al., 2013), XRCC2 (Romanowicz-Makowska et al., 2012), XRCC3 (Tsai et al., 2014), and XRCC4 (Chiu et al., 2008; Tseng et al., 2008) were reported to be associated with oral cancer. However, most of these studies focused on the single SNP effect or single SNP–environment effect. The complex gene– gene (G · G) and gene–environment (G · E) interactions associated with oral cancer are less addressed. G · G and G · E interactions were well-established to detect the epistasis which involved a complex association between disease/cancer related genes in case-control and family-based association studies (Chang et al., 2008; Chen et al., 2013, 2014; Chuang et al., 2012; Lin et al., 2009; Moore et al., 2010; Steen, 2012; Yang et al., 2011). This epistasis assists us to understand the causes of disease and cancer. Multifactor dimensionality reduction (MDR) represents an epistasis detection approach (Hahn et al., 2003; Ritchie et al., 2001) and several improved MDRs were suggested to detect particular data sets such as imbalanced data sets (Yang et al., 2013). MDR-ER (Yang et al., 2013) allowed that G · G interaction detection works on imbalanced data sets without the need of balanced demographic approaches. It can provide strong analytical abilities for imbalanced data sets for the detection of possible multiple factors interactions. In this study, we examined the G · G and G · E effect by an improved MDR (MDR-ER) by tandem consideration of genetic factors (four SNPs of XRCCs 1–4) and environmental factors (gender, age, smoking, alcohol drinking, and BQ chewing) in a dataset of 103 oral cancer cases and 98 controls from Taiwan. Risk-ranking of oral cancer was identified in terms of the G · G and G · E interactions. Methods Multifactor dimensionality reduction (MDR)
The nonparametric and model-free MDR method is widely used in the investigation of G · G and G · E interactions (Ritchie et al., 2001). Nonlinear interactions among multiple factors such as genetic and environmental factors can effectively discriminate nonsignificant effects for each individual factor (Ritchie et al., 2003). MDR is a data reduction method that searches for multifactor combinations associated with either high or low risks of oral cancer. Therefore, several genetic and environmental factors are classified as being of high and low risk. A high-order G · G interaction for the ability to classify and predict outcome risk status can be evaluated by cross-validation (CV) and permutation testing of the data space is reduced to a two-way contingency table. Supposedly the N SNPs are considered as a case-control data set, and the M is the maximum order of G · G and G · E interactions (i.e., M £ N). Let m be the number of order G · G and G · E interactions (m £ M). The procedure to use MDRs for detecting the best m-way of G · G and G · E interaction models is illustrated in Figure 1. The MDR procedure can be divided into the following eight steps: Step 1. Divide data set into a k sub data set and select a ith sub data set as the test data set and the other remaining sub data set as the training data set.
239
Step 2. A set of m factors (loci) is consisted from all factors. Step 3. All possible combinations of genotypes in m factors are represented in m-dimensional space (multifactor cells). Equation 1 is defined as a multifactor cell that includes a set of m genetic and environmental factors. L ¼ fl1 , l2 , l3 , . . . , lm g
(Eq:1)
Step 4. High or low risk is defined in each multifactor cell. Equation 2 is used to compute the ratio between case and control and the symbol u() is used to determine a score of ‘‘1’’ if all elements l in L match a sample in P or N, otherwise given a score ‘‘0’’. Each multifactor cell is labelled as ‘H’ or ‘L’ symbol. The ‘H’ indicates the high-risk group if the ratio in multifactor cell meets or exceeds a threshold, while ‘L’ indicates a low-risk group. P
f (L) ¼
+j ¼ 1 u(L, Pj )
(Eq:2)
N
+j ¼ 1 u(L, Nj )
where
u(L, A) ¼
8
