Clinical Cancer Research CTRC-AACR San Antonio Breast Cancer Symposium Infection and Cancer: Biology, Therapeutics, and Prevention
HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
Cancer Research Clinical Cancer Research
Cancer Epidemiology Biomarkers & Prevention Molecular Cancer Therapeutics
Molecular Cancer Research Cancer Prevention Research
Cancer Prevention Journals Portal Cancer Reviews Online
Annual Meeting Education Book Cell Growth & Differentiation
Clinical Cancer Research 13, 3753-3758, June 15, 2007. doi: 10.1158/1078-0432.CCR-06-1911
© 2007 American Association for Cancer Research
This Article
Right arrow Abstract Freely available
Right arrow Full Text (PDF)
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Right arrow reprints & permissions
Citing Articles
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Wang, Y.
Right arrow Articles by Wu, X.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Wang, Y.
Right arrow Articles by Wu, X.

Cancer Prevention

Nucleotide Excision Repair Pathway Genes and Oral Premalignant Lesions

Yunfei Wang1, Margaret R. Spitz1, J. Jack Lee2, Maosheng Huang1, Scott M. Lippman3 and Xifeng Wu1

Authors' Affiliations: Departments of 1 Epidemiology, 2 Biostatistics, and 3 Thoracic/Head and Neck Oncology, The University of Texas M. D. Anderson Cancer Center, Houston, Texas

Requests for reprints: Xifeng Wu, Department of Epidemiology, Box 189, The University of Texas M.D. Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, TX 77030. Phone: 713-745-2485; Fax: 713-792-0807; E-mail: xwu{at}mdanderson.org.


   Abstract
Top
 Abstract
Materials and Methods
 Results
 Discussion
 References
 
Purpose: Oral premalignant lesions (OPL) are associated with tobacco exposure and an increase in risk of oral cancer. Nucleotide excision repair (NER) is one of the major DNA repair pathways involved in the removal of tobacco carcinogen adducts. Polymorphisms in NER genes may cause variations in DNA repair capacity and increase susceptibility to both premalignant lesions and cancer.

Experimental Design: In this case-control study of 144 OPL patients and 288 controls, we genotyped 11 polymorphisms in 8 major NER genes, including XPA [A23G at 5' untranslated region (UTR)], XPD (Asp312Asn, Lys751Gln), XPC (Ala499Val, Lys939Gln), XPG (His1104Asp), XPF (Pro662Ser), ERCC6 (Met1097Val, Arg1230Pro) Rad23B (Ala249Val), and CCNH (Val270Ala).

Results: We found significant or borderline-significant associations between OPL risk and the polymorphisms XPA (A23G), XPD (Lys751Gln), XPC (Ala499Val), Rad23B (Ala249Val), and XPD (Asp312Asn), with adjusted odds ratios (ORs) of 1.97 [95% confidence interval (95% CI), 1.27-3.06], 1.60 (95% CI, 1.02-2.51), 0.63 (95% CI, 0.40-1.00), 0.67 (95% CI, 0.41-1.07), and 1.42 (95% CI, 0.90-2.23), respectively. When further stratified analyses were done, the decreased risk conferred by the XPC (Ala499Val) variant allele was more evident in older individuals (OR, 0.50; 95% CI, 0.24-1.03), in women (OR, 0.46; 95% CI, 0.21-1.01), in ever smokers (OR, 0.59; 95% CI, 0.33-1.05), and in never drinkers (OR, 0.42; 95% CI, 0.18-1.00). Finally, we found joint effects between these NER gene variants and smoking status. For example, when never smokers with the XPA 23A genotypes were used as the reference group, the ORs for never smokers with the XPA 23G genotype, smokers with the 23A genotype, and smokers with 23G genotypes were 2.19 (1.07-4.46), 2.64 (1.42-4.89), and 5.04 (2.62-9.69), respectively. Gene-gene and gene-smoking interaction for OPLs risk were also confirmed by multifactor dimensionality reduction (MDR) analysis in our study. MDR analysis revealed that a model containing ever smoking, XPA (A23G), XPC (Ala499Val), and XPD (Asp312Asn) was the best model to predict OPL risk with maximum average cross-validation consistency and minimum prediction error (P < 0.001).

Conclusion: Our results suggest that polymorphisms in NER genes may contribute to genetic susceptibility to OPLs and may therefore contribute to the development of oral cancer.

Oral white or red patches, known as leukoplakia and erythroplakia, are the major forms of oral premalignant lesions (OPL; ref. 1). Several clinical and epidemiologic studies have shown that the malignant transformation rates of oral leukoplakia range from 8.9% to 17.5% (2–4). The erythroplakia, although not as common as leukoplakia, are much more likely to become dysplasia or carcinoma (1, 5, 6). The factors that most greatly increase the risk of developing OPLs are the consumption of alcohol and the chewing or smoking of tobacco (7–10); nevertheless, the inherited genetic susceptibility may also play an important role. Individuals with weakened DNA repair capacity may have more genetic abnormalities compared with those with normal DNA repair capacity under similar carcinogenetic exposure conditions because DNA damages accumulate faster in individuals with suboptimal DNA repair capacity (11).

DNA repair systems play critical roles in protecting against mutations and are essential for maintaining the integrity of the genome. Nucleotide excision repair (NER) is one of the major DNA repair pathways and is mainly responsible for the removal of bulky DNA adducts induced by chemical carcinogens, such as the polycyclic aromatic hydrocarbons (PAH) found in tobacco smoke, car exhaust, coal burning, etc. (12, 13). NER includes two subpathways, global-genome repair and transcription-coupled repair. XPC-hHR23B and XPA-RPA complexes recognize the initial DNA damage in global-genome repair, whereas the ERCC6 (CSB) proteins recognize DNA lesions in the transcribed strand of an active gene in transcription-coupled repair (14–19). Once the DNA damage is recognized in either subpathway, the DNA helix around the lesion is unwound by the XPB and XPD helicases of the multi-subunit transcription factor THIIH, and the two strands are separated to form a preincision structure (20, 21). Then, 3' and 5' incisions are completed by ERCC5/XPG and ERCC1/XPF endonucleases, respectively, and finally, the DNA polymerase and ligase complete the repair by filling the gaps (12, 22–24). The functional mutations in NER genes may cause NER abnormalities and reduce the NER capacity and lead to altered genetic susceptibility to cancer.

Polymorphisms in DNA repair genes have been extensively studied for their associations with diseases. Most NER genes are polymorphic, and many studies have reported the associations between some polymorphisms in NER genes and the risk of tobacco-related cancer, such as lung cancer, head and neck cancer, and breast cancer (25–30). Spitz et al. (27) reported that polymorphisms in the XPD gene might modulate NER capability in lung cancer patients, and individuals with wide-type genotypes exhibited the most proficient DNA repair capacity. A meta-analysis of 9 individual case-control studies of 3,725 lung cancer cases and 4,152 controls showed that the homozygous variant alleles of these two polymorphisms exhibited 20% to 30% increased lung cancer risks (28).

To our knowledge, there have been no epidemiologic studies on NER genetic polymorphisms and risk of OPLs. In this study, we applied a pathway-based approach to systematically examine the associations between a panel of polymorphisms in major NER genes and risk of OPLs. Based on published studies, the polymorphisms that we selected in our study included XPA [A23G at 5' untranslated region (UTR)], XPD (Asp312Asn, Lys751Gln), XPC (Ala499Val, Lys939Gln), XPG (His1104Asp), XPF (Pro662Ser), ERCC6 (Met1097Val, Arg1230Pro), Rad23B (Ala249Val), and CCNH (Val270Ala). All these single nucleotide polymorphisms (SNP) have potential functional significance (nonsynonymous SNPs or located in the promoter and/or untranslated regions) and have been studied in literature. We also evaluated the joint effects of these polymorphisms with smoking in modulating the risk of OPLs.


   Materials and Methods
Top
 Abstract
 Materials and Methods
Results
 Discussion
 References
 
Study subjects. A total of 144 OPL patients were identified at the University of Texas M.D. Anderson Cancer Center from 1997 to 2006. The inclusion criteria for cases were the presence of histologically confirmed OPL and an age of 18 years or older. Patients with acute intercurrent illnesses or infections were excluded, as were those who had retinoid or carotenoid therapy within 3 months before study entry. A self-administered questionnaire was used to gather epidemiologic data, including recent and prior tobacco and alcohol uses. Baseline blood samples were obtained in heparinized tubes for molecular analyses. A total of 288 controls without any history of cancer were recruited from a large pool of potential volunteers from the Kelsey-Seybold clinic, a large multispecialty managed-care organization in the Houston metropolitan area, from 1999 to 2006. The potential controls were identified by reviewing short survey forms distributed to patients visiting the Kelsey-Seybold clinics. The majority of control subjects are healthy individuals coming to the clinics for annual health checkups. The study design to recruit controls has been proven to be valid and efficient for large-scale molecular epidemiologic studies (31). The controls were matched to the cases by age (±5), sex, and ethnicity (Caucasian, African-American, Hispanics, and other). Questionnaire data were obtained through personal interviews with the controls. This study was approved by the Institutional Review Boards (IRB) of the M.D. Anderson Cancer Center and Kelsey-Seybold Clinic. All study participants signed an informed consent prior participation.

Genotyping of NER genes. Genomic DNA was isolated from peripheral blood samples using proteinase K digestion, followed by isopropanol extraction and ethanol precipitation. PCR-RFLP was used for the genotyping of XPA (A23G) and XPD (Asp312Asn) as described previously (25, 28). TaqMan method was used to detect the XPD (Lys751Gln), XPC (Ala499Val, Lys939Gln), XPG (His1104Asp), XPF (Pro662Ser), ERCC6 (Met1097Val, Arg1230Pro), Rad23B (Ala249Val), and CCNH (Val270Ala) genotypes. Briefly, sequences of primers and probes were either obtained from the National Cancer Institute SNP500 cancer database or designed using Primer Express Software (Applied Biosystems). The probes were labeled fluorescently with either FAM or VIC on the 5' end and a nonfluorescent minor groove binder quencher on the 3' end (Applied Biosystems). Typical amplification mixes and thermal cycling conditions were recommended by the probe supplier (Applied Biosystems). Water and internal controls were included in each plate to ensure the accuracy of the genotyping. Reactions were run on the dual 384-well GeneAmp PCR System 9700, and the plates were read in a TaqMan 7900HT sequence detection system (Applied Biosystems). The analyzed fluorescence results were then auto-called into genotypes using the built-in software of the system.

Statistical analyses. All statistical analyses were done using the Intercooled Stata 8.0 statistical software package (Stata Corporation). In comparing cases and controls, the {chi}2 test was used to test for differences in the distribution of gender, ethnicity, smoking status, and NER genotypes, whereas the Student's t test was used to test for differences in age and pack-years (among ever smokers). Odds ratios (OR) were calculated as an estimate of the relative risk. Where appropriate, unconditional multivariate logistic regression was done to control for potential confounding by age, gender, ethnicity, and smoking status. The sample size was too small for minority groups for stratified analyses. However, we checked the distribution of genotype frequencies in different ethnic groups and did not find significant differences. We also restricted the analyses in Caucasians and found that the results were similar to that of all ethnicities combined. Therefore, we only reported overall results while adjusting for ethnicity. Interaction was tested on the multiplicative scale by entering product terms in the multivariable logistic regression models.

Multifactor dimensionality reduction (MDR; refs. 32–34) is done to assess interactions among multiple SNPs and/or risk factors. In MDR, a set of n factors is selected, and the n selected factors and their possible multifactor classes are represented in n dimensional space. The ratio for the number of cases to the number of controls is calculated within each multifactor class. Each multifactor class in n-dimensional space is then labeled as "high risk" or "low risk", based on whether the case-to-control ratio has exceeded a threshold. Thus, a new variable incorporating information from several risk factors is defined, and cross-validation and permutation tests are used to assess the capability of this new variable to predict outcome risk. The cross-validation consistency is calculated to estimate the percentage of the same combination of risk factors selected as the best model among cross-validation data sets. The average prediction error is calculated as an average of the prediction errors across each of the cross-validation subsets.

In this study, we also used the MDR to identify and assess putative risk factors, as well as gene-gene and gene-smoking interactions, for NER polymorphisms. We used 100-fold cross-validation and 1,000-fold permutation testing, and all statistical tests were at the significance level of 0.05. Cross-validation and permutation testing were done for the same set of subjects.


   Results
Top
 Abstract
 Materials and Methods
 Results
Discussion
 References
 
By study design, the 144 OPL cases and 288 controls were matched by age, gender and ethnicity (Both P = 1.0; Table 1 ). The mean age of controls (59.7 ± 11.0 years) was slightly higher than that of cases (58.3 ± 12.8 years), but the difference was not statistically significant (P = 0.25). As expected, current smokers were statistically significantly overrepresented among the cases (27.1%), as compared with the controls (10.4%; P < 0.001).


View this table:
[in this window]
[in a new window]

  		Table 1. Distribution of host characteristics in cases and controls

 
In single SNP analysis, the GG genotype for XPA (A23G) was more common in cases (50.7%) than in controls (34.7%). Adjusted by age, gender, ethnicity, smoking status, and alcohol use, individuals with the GG genotype had a significantly increased OPL risk compared with those with the AA and AG genotypes [OR, 1.97; 95% confidence interval (95% CI), 1.27-3.06]. Individuals with at least one variant allele of XPD (Lys751Gln) polymorphism exhibited a significant increase in OPL risks with OR of 1.60 (95% CI, 1.02-2.51). A significant decrease in OPL risks with OR of 0.63 (95% CI, 0.40-0.999) was observed in individuals with the variant allele of XPC (Ala499Val). The GA + AA genotypes for XPD (Asp312Asn) and the CT + TT genotypes for Rad23B (Ala249Val) showed an increased/reduced OPL risk with ORs of 1.42 (95% CI, 0.90-2.23) and 0.67 (95% CI, 0.41-1.07), respectively, but did not reach statistical significance (Table 1). No statistically significant association was found between other NER SNPs and OPL risk in this study (Table 2 ).


View this table:
[in this window]
[in a new window]

  		Table 2. Risk estimates of NER polymorphisms

 
Stratified by age, gender, ethnicity, smoking status, and drinking status, we found that the reduced risk conferred by the XPC (Ala499Val) variant alleles was more evident in older individuals (age ≥ 61; OR, 0.50; 95% CI, 0.24-1.03), in women (OR, 0.46; 95% CI, 0.21-1.01), in ever smokers (OR, 0.59; 95% CI, 0.33-1.05), and in never drinkers (OR, 0.42; 95% CI, 0.18-0.998; data not shown). There were no obvious effects of age, gender, smoking, or drinking on the association between other polymorphisms and OPL risk (data not shown). However, due to the borderline confidence intervals and the multiple comparison issue and the small sample size of each stratum, the results from these stratified analyses need to be interpreted with caution.

Due to the fact that XPA (A23G), XPD (Lys751Gln), and XPC (Ala499Val) polymorphisms showed significant associations with OPL risk in the single SNP analysis above, we also evaluated the joint effects of the three SNPs (Table 3 ). The reference group included individuals with the low-risk genotypes at all three loci. Of these individuals, those with one, two, or three high-risk genotypes all exhibited increased OPL risks, with the ORs ranging from 2.27 (95% CI, 0.64-8.12) to 8.99 (95% CI, 2.66-30.4; Table 3). Finally, we found joint effects between NER gene variants and smoking status. Combining heterozygote and homozygote variant genotypes and using a reference group that included never smokers with the XPA 23A genotypes, the ORs for never smokers with the XPA 23G genotype, smokers with 23A genotype, and smokers with 23G genotypes were found to be 2.19 (1.07-4.46), 2.64 (1.42-4.89), and 5.04 (2.62-9.69), respectively. A similar joint effect was observed between smoking status and the XPD (Lys751Gln), Rad23B Ala249Val, and XPC Ala499Val adverse alleles (Table 4 ). A gene-drinking interaction was also analyzed, and no significant results were found (data not shown). We also did haplotype analysis for SNPs of XPD, ERCC6 and XPC (Table 5 ). Overall, there is no significant association between OPL risk and haplotypes of XPD, ERCC6, and XPC.


View this table:
[in this window]
[in a new window]

  		Table 3. Joint effects among XPA A23G, XPD Lys751Gln, and XPC Ala499Val genes

 

View this table:
[in this window]
[in a new window]

  		Table 4. Joint effect between polymorphism and smoking status

 

View this table:
[in this window]
[in a new window]

  		Table 5. Haplotype analyses of OPL risk

 
The results of the MDR analysis are presented in Table 6 . The model including only smoking was the best single factor model for predicting OPL risk, with average cross-validation consistency of 100% and an average prediction error of 41.0%. Among two-factor models, the combination of smoking and XPC Ala499Val proved to be the strongest, with an average cross-validation consistency of 52.7% and an average prediction error of 52.9%. When three factors were considered, the combination of smoking, XPC Ala499Val, and XPA A23G was the strongest model, with a cross-validation consistency of 100% and an average prediction error of 36.0%. A model containing smoking, XPD Asp312Asn, XPA A23G, and XPC Ala499Val was the strongest among all four-factor models, with a cross validation consistency of 99.9% and an average prediction error rate of 34.8%. The average cross-validation consistency decreased in five-factor models. Among all models, the four-factor model had the highest cross-validation consistency and the lowest prediction error (P < 0.001; Table 6).


View this table:
[in this window]
[in a new window]

  		Table 6. Summary of results for OPL from MDR analysis

 

   Discussion
Top
 Abstract
 Materials and Methods
 Results
 Discussion
References
 
Polymorphisms in NER genes may reduce the efficiency of DNA repair (35). The XPA protein binds to damaged DNA and maintains an intricate network of contacts with core repair factors. It plays a key role in accurately positioning the repair machinery around the DNA lesion (36). Previously, we have reported that the XPA (A23G) G allele was associated with a reduced lung cancer risk for Caucasians (OR, 0.69; 95% CI, 0.53-0.90), Mexican-Americans (OR, 0.32; 95% CI, 0.12-0.83), and African-Americans (OR, 0.45; 95% CI, 0.16-1.22; ref. 25). In our current report, however, we found that the homozygous G allele was associated with a significantly increased risk for OPL. The reason for this discrepancy is unknown. It may be due to the differences in the etiology of lung cancer and OPL.

The XPC gene encodes a 940-amino-acid protein that is involved in DNA damage recognition (37). The XPC protein is tightly combined with Rad23 and plays an early role in NER by initially detecting the DNA damage (38, 39). The accurate function of Rad23 in NER is not fully understood. But recently, many studies indicated that the complex of HR23B (Rad23 homologue) and XPC is involved in the damage recognition step of NER, and that the primary function of the Rad23 protein is to stabilize the XPC protein (40). In our study, the variant T alleles for XPC (Ala499Val) polymorphisms were associated with a 0.63-fold decrease in OPL risk, which is consistent with the results reported recently. Huang et al. (41) reported that individuals with XPC 499Val exhibited a significantly reduced advanced colorectal adenoma risk in their case-control study with 772 cases and 777 controls, and this finding was supported by a haplotype analysis. Similar results of the variant T alleles for XPC (Ala499Val) were also reported by Zhu et al. and Zhou et al. (42, 43). The variant T allele of the Rad23B (Ala249Val) polymorphism was associated with a borderline significant decrease in OPL risk in our study. However, Shen et al. (44) showed a 2-fold increased risk in lung cancer patients with RAD23 Val allele in Chinese population, and no association was found for this polymorphism in a study of advance colorectal adenoma (41). The heterogeneity of these results may be attributed to different cancer site, race, and sample size. Our results showed a joint effect among NER polymorphisms and a significant trend for an increase in OPL risk in subjects with increasing numbers of risk alleles. This result is in agreement with our hypothesis that these polymorphisms coordinate in the DNA repair pathway, and that the joint effects of their genotypes yield higher risk for OPL.

Our results also showed a gene-smoking interaction between XPA, XPC, and Rad23 genotypes and smoking status in OPL (Table 4). It suggested that smokers with variant homozygous genotypes exhibit a higher OPL risk than other groups. Cigarette smoke contains multiple carcinogenic substrates, such as polycyclic aromatic hydrocarbons, and may induce DNA damage by covalent binding or oxidation (45, 46). NER is the major pathway that repairs tobacco carcinogen-induced bulky DNA adducts. Spitz et al. (27) reported a lower capacity of DNA repair in lung cancer patients when compared with healthy controls, and this risk may be modulated by smoking status. OPL is a tobacco-related premalignant lesion, and it is biologically plausible that polymorphisms of NER genes predispose an individual to OPLs.

The association between NER gene polymorphisms and OPL risk, the gene-gene interaction, and gene-smoking interaction were explored by MDR analysis in our study. This algorithm may allow rapid identification of potential genetic and environmental interactions when analyzing a large number of variables.

This study has several limitations. As a hospital-based case-control study, this study may be subject to potential selection bias. In addition, recall bias cannot be ruled out. However, as we tested a genotype-driven hypothesis rather than an environment-driven hypothesis, selection bias and recall bias are of less concern. Furthermore, data collected by self-administered questionnaire and in-person interview may produce differential bias in risk assessment. However, in this study, the influence should be minimal because we only used demographic information and smoking and drinking status data collected by questionnaire. Subjects should be able to report consistent information in both self-administered questionnaire and in-person interview in terms of demographic characteristics (age, gender, ethnicity) and smoking and drinking status.

In conclusion, the results of this study suggest that after adjusting for important OPL risk factors, such as smoking and alcohol drinking, deficiency in NER pathway predisposes susceptible individuals to increased OPL risk, supporting the hypothesis that the inherited genetic susceptibility also plays an important role in OPL etiology. Therefore, the high progression rate of OPL to oral cancer may also be partly explained by the genetic predisposition factors, such as genetic alterations in NER pathway.

Although our results suggest that OPL risk is associated with variant genotypes of NER genes, these findings are subject to scrutiny due to limited power and sample size. Larger studies with a priori hypotheses for these covariates are certainly warranted for validation.


   Footnotes
 
Grant support: National Cancer Institute grants CA106451 and CA097007.

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

Received 8/ 2/06; revised 1/26/07; accepted 3/22/07.


   References
Top
 Abstract
 Materials and Methods
 Results
 Discussion
 References
 

  1. Neville BW, Day TA. Oral cancer and precancerous lesions. CA Cancer J Clin 2002;52:195–215.[Abstract/Free Full Text]
  2. Bouquot JE, Whitaker SB. Oral leukoplakia-rationale for diagnosis and prognosis of its clinical subtypes or "phases". Quintessence Int 1994;25:133–40.[Medline]
  3. Silverman S, Jr., Gorsky M, Lozada F. Oral leukoplakia and malignant transformation. A follow-up study of 257 patients. Cancer 1984;53:563–8.[CrossRef][Medline]
  4. Lind PO. Malignant transformation in oral leukoplakia. Scand J Dent Res 1987;95:449–55.[Medline]
  5. Shafer WG, Waldron CA. Erythroplakia of the oral cavity. Cancer 1975;36:1021–8.[CrossRef][Medline]
  6. Mashberg A, Samit A. Early diagnosis of asymptomatic oral and oropharyngeal squamous cancers. CA Cancer J Clin 1995;45:328–51.[Abstract/Free Full Text]
  7. Hashibe M, Sankaranarayanan R, Thomas G, et al. Body mass index, tobacco chewing, alcohol drinking and the risk of oral submucous fibrosis in Kerala, India. Cancer Causes Control 2002;13:55–64.[CrossRef][Medline]
  8. Campisi G, Margiotta V. Oral mucosal lesions and risk habits among men in an Italian study population. J Oral Pathol Med 2001;30:22–8.[CrossRef][Medline]
  9. Silverman S, Jr. Prevention, early detection and diagnosis of oral cancer. Dermatol Clin 1987;5:675–80.[Medline]
  10. Silverman S, Jr. Precancerous lesions and oral cancer in the elderly. Clin Geriatr Med 1992;8:529–41.[Medline]
  11. Wu X, Lippman SM, Lee JJ, et al. Chromosome instability in lymphocytes: a potential indicator of predisposition to oral premalignant lesions. Cancer Res 2002;62:2813–8.[Abstract/Free Full Text]
  12. Aboussekhra A, Biggerstaff M, Shivji MK, et al. Mammalian DNA nucleotide excision repair reconstituted with purified protein components. Cell 1995;80:859–68.[CrossRef][Medline]
  13. Wood RD. DNA repair. Variants on a theme. Nature 1999;399:639–40.[CrossRef][Medline]
  14. Lindahl T, Wood RD. Quality control by DNA repair. Science 1999;286:1897–905.[Abstract/Free Full Text]
  15. He Z, Henricksen LA, Wold MS, Ingles CJ. RPA involvement in the damage-recognition and incision steps of nucleotide excision repair. Nature 1995;374:566–9.[CrossRef][Medline]
  16. Li L, Lu X, Peterson CA, Legerski RJ. An interaction between the DNA repair factor XPA and replication protein A appears essential for nucleotide excision repair. Mol Cell Biol 1995;15:5396–402.[Abstract]
  17. Matsuda T, Saijo M, Kuraoka I, et al. DNA repair protein XPA binds replication protein A (RPA). J Biol Chem 1995;270:4152–7.[Abstract/Free Full Text]
  18. van Gool AJ, Citterio E, Rademakers S, et al. The Cockayne syndrome B protein, involved in transcription-coupled DNA repair, resides in an RNA polymerase II–containing complex. EMBO J 1997;16:5955–65.[CrossRef][Medline]
  19. Kamiuchi S, Saijo M, Citterio E, de Jager M, Hoeijmakers JH, Tanaka K. Translocation of Cockayne syndrome group A protein to the nuclear matrix: possible relevance to transcription-coupled DNA repair. Proc Natl Acad Sci U S A 2002;99:201–6.[Abstract/Free Full Text]
  20. Schaeffer L, Roy R, Humbert S, et al. DNA repair helicase: a component of BTF2 (TFIIH) basic transcription factor. Science 1993;260:58–63.[Abstract/Free Full Text]
  21. Schaeffer L, Moncollin V, Roy R, et al. The ERCC2/DNA repair protein is associated with the class II BTF2/TFIIH transcription factor. EMBO J 1994;13:2388–92.[Medline]
  22. Habraken Y, Sung P, Prakash L, Prakash S. A conserved 5' to 3' exonuclease activity in the yeast and human nucleotide excision repair proteins RAD2 and XPG. J Biol Chem 1994;269:31342–5.[Abstract/Free Full Text]
  23. O'Donovan A, Davies AA, Moggs JG, West SC, Wood RD. XPG endonuclease makes the 3' incision in human DNA nucleotide excision repair. Nature 1994;371:432–5.[CrossRef][Medline]
  24. Sijbers AM, de Laat WL, Ariza RR, et al. Xeroderma pigmentosum group F caused by a defect in a structure-specific DNA repair endonuclease. Cell 1996;86:811–22.[CrossRef][Medline]
  25. Wu X, Zhao H, Wei Q, et al. XPA polymorphism associated with reduced lung cancer risk and a modulating effect on nucleotide excision repair capacity. Carcinogenesis 2003;24:505–9.[Abstract/Free Full Text]
  26. Park JY, Park SH, Choi JE, et al. Polymorphisms of the DNA repair gene xeroderma pigmentosum group A and risk of primary lung cancer. Cancer Epidemiol Biomarkers Prev 2002;11:993–7.[Abstract/Free Full Text]
  27. Spitz MR, Wu X, Wang Y, et al. Modulation of nucleotide excision repair capacity by XPD polymorphisms in lung cancer patients. Cancer Res 2001;61:1354–7.[Abstract/Free Full Text]
  28. Hu Z, Wei Q, Wang X, Shen H. DNA repair gene XPD polymorphism and lung cancer risk: a meta-analysis. Lung Cancer 2004;46:1–10.[CrossRef][Medline]
  29. Smith TR, Levine EA, Perrier ND, et al. DNA-repair genetic polymorphisms and breast cancer risk. Cancer Epidemiol Biomarkers Prev 2003;12:1200–4.[Abstract/Free Full Text]
  30. Forsti A, Angelini S, Festa F, et al. Single nucleotide polymorphisms in breast cancer. Oncol Rep 2004;11:917–22.[Medline]
  31. Hudmon KS, Honn SE, Jiang H, et al. Identifying and recruiting healthy control subjects from a managed care organization: a methodology for molecular epidemiological case-control studies of cancer. Cancer Epidemiol Biomarkers Prev 1997;6:565–71.[Abstract]
  32. Ritchie MD, Hahn LW, Roodi N, et al. Multifactor dimensionality reduction reveals high-order interactions among estrogen-metabolism genes in sporadic breast cancer. Am J Hum Genet 2001;69:138–47.[CrossRef][Medline]
  33. Hahn LW, Ritchie MD, Moore JH. Multifactor dimensionality reduction software for detecting gene-gene and gene-environment interactions. Bioinformatics 2003;19:376–82.[Abstract/Free Full Text]
  34. Moore JH. Computational analysis of gene-gene interactions using multifactor dimensionality reduction. Expert Rev Mol Diagn 2004;4:795–803.[CrossRef][Medline]
  35. Xi T, Jones IM, Mohrenweiser HW. Many amino acid substitution variants identified in DNA repair genes during human population screenings are predicted to impact protein function. Genomics 2004;83:970–9.[CrossRef][Medline]
  36. Park CH, Sancar A. Formation of a ternary complex by human XPA, ERCC1, and ERCC4(XPF) excision repair proteins. Proc Natl Acad Sci U S A 1994;91:5017–21.[Abstract/Free Full Text]
  37. Sugasawa K, Ng JM, Masutani C, et al. Xeroderma pigmentosum group C protein complex is the initiator of global genome nucleotide excision repair. Mol Cell 1998;2:223–32.[CrossRef][Medline]
  38. Rybicki BA, Conti DV, Moreira A, Cicek M, Casey G, Witte JS. DNA repair gene XRCC1 and XPD polymorphisms and risk of prostate cancer. Cancer Epidemiol Biomarkers Prev 2004;13:23–9.[Abstract/Free Full Text]
  39. Sugasawa K, Ng JM, Masutani C, et al. Two human homologs of Rad23 are functionally interchangeable in complex formation and stimulation of XPC repair activity. Mol Cell Biol 1997;17:6924–31.[Abstract]
  40. Xie Z, Liu S, Zhang Y, Wang Z. Roles of Rad23 protein in yeast nucleotide excision repair. Nucleic Acids Res 2004;32:5981–90.[Abstract/Free Full Text]
  41. Huang WY, Berndt SI, Kang D, et al. Nucleotide excision repair gene polymorphisms and risk of advanced colorectal adenoma: XPC polymorphisms modify smoking-related risk. Cancer Epidemiol Biomarkers Prev 2006;15:306–11.[Abstract/Free Full Text]
  42. Zhu Y, Lai M, Yang H, et al. Genotypes, haplotypes, and diplotypes of XPC and risk of bladder cancer. Carcinogenesis 2006. Epub ahead of print.
  43. Zhou RM, Li Y, Wang N, Zhang XJ, Dong XJ, Guo W. Correlation of XPC Ala499Val and Lys939Gln polymorphisms to risks of esophageal squamous cell carcinoma and gastric cardiac adenocarcinoma. Ai Zheng 2006;25:1113–9.[Medline]
  44. Shen M, Berndt SI, Rothman N, et al. Polymorphisms in the DNA nucleotide excision repair genes and lung cancer risk in Xuan Wei, China. Int J Cancer 2005;116:768–73.[CrossRef][Medline]
  45. Hecht SS. Tobacco smoke carcinogens and lung cancer. J Natl Cancer Inst 1999;91:1194–210.[Abstract/Free Full Text]
  46. Duell EJ, Wiencke JK, Cheng TJ, et al. Polymorphisms in the DNA repair genes XRCC1 and ERCC2 and biomarkers of DNA damage in human blood mononuclear cells. Carcinogenesis 2000;21:965–71.[Abstract/Free Full Text]




This Article
Right arrow Abstract Freely available
Right arrow Full Text (PDF)
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Right arrow reprints & permissions
Citing Articles
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Wang, Y.
Right arrow Articles by Wu, X.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Wang, Y.
Right arrow Articles by Wu, X.

HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
Cancer Research Clinical Cancer Research
Cancer Epidemiology Biomarkers & Prevention Molecular Cancer Therapeutics
Molecular Cancer Research Cancer Prevention Research
Cancer Prevention Journals Portal Cancer Reviews Online
Annual Meeting Education Book Cell Growth & Differentiation