Skip to content


Environmental Health and Preventive Medicine

Open Access

The genotype of the transporter associated with antigen processing gene affects susceptibility to colorectal cancer in Japanese

  • Takenori Yamauchi1,
  • Shouhei Takeuchi1,
  • Naoki Maehara2 and
  • Yoshiki Kuroda1Email author
Environmental Health and Preventive Medicine201419:388

Received: 30 January 2014

Accepted: 4 March 2014

Published: 7 May 2014



Although colorectal cancer (CRC) is one of the most frequent malignancies in Japan, the associated genetic factors remain to be elucidated. Functional loss of the transporter associated with antigen processing (TAP) 1 gene induces carcinogenesis. We investigated whether single nucleotide polymorphisms (SNPs) in the TAP1 gene (rs735883) are associated with susceptibility to CRC in a Japanese population.


The study participants were 143 cases and 243 clinical controls. After extracting DNA from their peripheral blood cells, genotyping was conducted by the polymerase chain reaction–restriction fragment length polymorphism method.


Participants with a mutated allele had an increased risk for CRC. The adjusted odds ratios for the C/T, T/T, and the mutation type (C/T + T/T) compared to that of wild type (C/C) were 2.27 [95 % confidence interval (CI), 1.43–3.67], 1.95 (95 % CI, 0.88–4.30), and 2.22 (95 % CI, 1.42–3.55), respectively. Furthermore, a significant trend in the rate of cases was observed with an increasing number of mutated alleles (P for trend = 0.0068).


The genotype of the TAP1 gene is associated with susceptibility to CRC.


Immune escapeTAP1Genetic polymorphismColorectal cancerJapanese


According to a report by the International Agency for Research on Cancer (IARC), colorectal cancer (CRC) is one of the most lethal malignant neoplasms worldwide despite the decreasing mortality and incidence of CRC in developed countries. The age-standardized incidence and mortality due to CRC per 100,000 are 17.2 and 8.2, respectively [1]. In Japan, the age-standardized incidence of CRC, which accounts for 16.5 % of all malignant neoplasms, is the third highest among all kinds of cancer. The age-standardized mortality of CRC is 14.8 in men and 8.4 in women [2].

CRC carcinogenesis is a complex and multifactorial process. As is often true with malignancies, the development of CRC is the result of interactions between environmental and genetic factors. Many epidemiological studies have confirmed the effects of environmental factors. A diet containing red or processed meats can markedly increase the risk of CRC [3]. Heavy alcohol intake and smoking are also associated with CRC prognosis [4]. As a consequence, the IARC reported that diet, exercise, and obesity are associated with CRC carcinogenesis. In contrast, genetic factors are also associated with susceptibility to CRC. Gender differences have been reported in both age-standardized incidence and mortality of CRC [1]. A personal or familial history of specific diseases (e.g., a personal history of chronic inflammatory bowel disease and a familial or personal history of adenomatous polyps) has also been considered a risk factor for CRC [49]. Although the biological mechanisms underlying the carcinogenesis of CRC are not fully understood, one of the proposed mechanisms is immune escape, which allows tumor cells to escape from immune surveillance.

The transporter associated with antigen processing (TAP) protein, a heterodimer of TAP1 and TAP2 belonging to the major histocompatibility complex (MHC) class I, is responsible for immune escape [10, 11]. TAP translocates antigen peptides from the cytosol to the endoplasmic reticulum (ER) lumen and helps MHC class I molecules bind to antigen peptides [12, 13]. Several TAP1 gene polymorphisms have been identified, and antigen processing ability has been evaluated in many studies. Some of the polymorphisms were found to decrease the efficacy of antigen processing [14]. Recent studies have suggested that TAP1 gene polymorphisms may increase the risk for vitiligo [15], nasopharyngeal carcinoma [16], and CRC via downregulation of MHC class I molecules [17]. However, few epidemiological studies have focused on the TAP1 gene polymorphisms (rs735883). In this case–control study, we investigated the association of the TAP1 genotype with susceptibility to CRC in relation to gender and smoking status.

Materials and methods

Study participants

A total of 143 Japanese CRC cases and 243 Japanese non-cancer clinical controls were recruited. The cases were consecutive patients treated at the University of Miyazaki (UOM) Hospital and the University of Occupational and Environmental Health (UOEH) Hospital in Japan from September 1992 to December 2006. The controls were recruited from patients suffering from non-cancerous diseases in the hospitals near UOEH Hospital between September 1996 and September 2001. All cases were histologically diagnosed with CRC including ascending, transverse, and descending colon cancer as well as rectal cancer. The subjects’ history of illness, residence, occupation, and smoking status were examined by a self-questionnaire. No patients who had been exposed to carcinogens, heavy metals, or radiation in their occupational history were included. All subjects were classified into two groups according to smoking status: the “never” group, composed of non-smokers; and the “smoker” group, composed of both current smokers and ex-smokers. All cases and controls were given an explanation of the nature of the study, and written informed consent was obtained from all participants. The Ethical Committee of UOM approved this study procedure on December 7, 2005 (approval number: 239).

Polymerase chain reaction (PCR) amplification and genotyping

Genomic DNA was extracted from peripheral blood lymphocytes with a DNA Extractor WB Kit (Wako Pure Chemical Industries, Osaka, Japan) according to the manufacturer’s protocol. The single nucleotide polymorphisms (SNPs) (rs735883) are located on the intron 7 region of the TAP1 gene, and the analysis of this SNP was carried out using a PCR–restriction fragment length polymorphism (PCR–RFLP) assay, as described previously [15]. Briefly, samples were subjected to 35 cycles of 30 s denaturing at 95 °C, 30 s annealing at 55 °C, and 30 s extension at 72 °C, followed by a 5 min final extension with PCR primers 5′-GTGCTCTCACGTTCCAAGGA-3′ and 5′-AGGAGTAGAGATAGAAGAACC-3′. Subsequently, a 183 bp PCR product was digested with the MspI restriction enzyme and the restriction fragments were separated by agarose gel electrophoresis in TAE buffer. The wild-type C allele was digested into fragments of 161 and 22 bp, and the mutated type T allele was not digested.

Statistical analysis

Results are presented as mean ± standard deviation (SD) for continuous variables. Pearson’s Chi square tests were used for a categorical comparison of the data and for evaluating the probability of Hardy–Weinberg equilibrium. The prevalence of each genotype was examined with the Cochran–Armitage trend test. Welch’s two-sample t tests were used for numerical comparisons. Multivariate analysis was conducted using a multiple logistic regression model after adjusting for age, gender, or smoking status. A P value <0.05 (two-tailed) was considered significant. Smoking status is associated with increasing risk of CRC [18], and there are many more male smokers than female smokers among the Japanese population. As gender and smoking status could be confounding factors, stratified analyses by gender and smoking status were conducted to exclude the effect of each factor. Power analysis was performed to determine the statistical power of Chi square tests. All statistical analyses were performed using R ver. 2.15.1.


The general characteristics of the cases and the controls are shown in Table 1. The mean age (±SD) was 65.8 (±16.6) years for the controls and 63.9 (±10.9) years for the cases (P = 0.18). The frequencies of gender and smoking status were not significantly different between the cases and the controls (P = 0.12, 0.18, respectively). No significant difference was observed between the cases and the controls in terms of age, gender, and smoking status. The frequencies of the TAP1 genotypes are shown in Table 2. The allele frequencies in the cases were allele C: 0.58 and allele T: 0.42, and allele C: 0.67 and allele T: 0.33 in the controls. The odds ratio (OR) for allele T compared to allele C was 1.47 [95 % confidence interval (CI), 1.08–1.98, P = 0.013]. The observed frequencies of the TAP1 allele in the controls were consistent with the allele frequencies in Japanese. Hardy–Weinberg equilibrium was confirmed for the TAP1 genotype in the controls (P = 0.31). The adjusted ORs for the C/T and T/T genotypes compared to the C/C genotype were estimated to be 2.27 (95 % CI, 1.43–3.67) and 1.95 (95 % CI, 0.88–4.30), respectively. Furthermore, that for the mutation type (C/T + T/T) was 2.22 (95 % CI, 1.42–3.55). Although a significant difference was not observed in the T/T genotype, a significant trend on the rate of the cases was observed (P for trend = 0.0068).
Table 1

General characteristics of the controls and the CRC patients




Age (years)

65.8 ± 16.6

63.9 ± 10.9

Gender (%)


110 (45.3)

53 (37.1)


133 (54.7)

90 (62.9)

Smoking Status (%)


112 (46.1)

76 (53.1)


131 (53.9)

67 (46.9)




Age is presented as mean ± standard deviation. Gender and smoking status are presented as number of subjects. No significant difference was observed between cases and controls

Table 2

Associations between the TAP1 genotype and CRC




Crude OR (95 % CI)

Adjusted OR (95 % CI)

C/C (%)

106 (43.6)

38 (26.6)

C/T (%)

115 (47.3)

91 (63.6)

2.21 (1.39–3.49)**

2.27 (1.43–3.67)**

T/T (%)

22 (9.05)

14 (9.79)

1.78 (0.83–3.78)

1.95 (0.88–4.30)





C/T + T/T (%)

137 (56.4)

105 (73.4)

2.14 (1.37–3.34)**

2.22 (1.42–3.55)**

Crude OR and adjusted OR for age, gender and smoking status were estimated using Chi square statistic and multivariate logistic regression, respectively

95 % CI 95 % confidence interval, OR odds ratio

* P < 0.05, ** P < 0.01

The results of the stratified analysis by smoking status are shown in Table 3. In the “never” group, the adjusted ORs were estimated to be 1.94 (95 % CI, 1.02–3.75) for the C/T genotype and 2.04 (95 % CI, 0.62–6.58) for the T/T compared to the C/C genotype. Those in the “smoker” group were calculated to be 2.84 (95 % CI, 1.43–5.92) for C/T and 1.98 (95 % CI 0.64–5.83) for T/T. For the mutated type (C/T + T/T), the ORs were 1.95 (95 % CI 1.05–3.72) in the “never” group and 2.69 (95 % CI, 1.37–5.51) in the “smoker” group, respectively. The P for the trend was 0.079 in the “never” group and 0.027 in the “smoker” group.
Table 3

Associations between the TAP1 genotype and CRC when stratified by smoking status

Smoking status




Crude OR (95 % CI)

Adjusted OR (95 % CI)


C/C (%)

51 (45.5)

24 (31.6)

C/T (%)

53 (47.3)

45 (59.2)

1.80 (0.97–3.36)

1.94 (1.02–3.75)*

T/T (%)

8 (7.14)

7 (9.21)

1.86 (0.62–5.55)

2.04 (0.62–6.58)





C/T + T/T (%)

61 (54.5)

52 (68.4)

1.81 (0.99–3.32)

1.95 (1.05–3.72)*


C/C (%)

55 (42.0)

14 (20.9)

C/T (%)

62 (47.3)

46 (68.7)

2.91 (1.46–5.82)**

2.84 (1.43–5.92)**

T/T (%)

14 (10.7)

7 (10.4)

1.96 (0.68–5.64)

1.98 (0.64–5.83)





C/T + T/T (%)

76 (58.0)

53 (79.1)

2.74 (1.39–5.38)**

2.69 (1.37–5.51)**

Crude OR and adjusted OR for age and gender were estimated using Chi square statistic and multivariate logistic regression, respectively

95 % CI 95 % confidence interval, OR odds ratio

* P < 0.05; ** P < 0.01

The results of the analysis stratified by gender are shown in Table 4. In the male group, the adjusted ORs were estimated to be 2.04 (95 % CI, 1.11–3.84) for the C/T genotype and 2.38 (95 % CI, 0.86–6.61) for the T/T genotype compared to the C/C genotype. The ORs for the C/T and T/T were 2.91 (95 % CI, 1.40–6.32) and 1.45 (95 % CI, 0.36–5.10), respectively, in the female group. For the mutated type, the ORs were 2.09 (95 % CI, 1.15–3.87) in the male group and 2.63 (95 % CI, 1.29–5.62) in the female group. P for the trend was 0.065 in the male group and 0.052 in the female group.
Table 4

Associations between the TAP1 genotype and CRC when stratified by gender





Crude OR (95 % CI)

Adjusted OR (95 % CI)


C/C (%)

55 (41.4)

25 (27.8)

C/T (%)

67 (50.4)

55 (61.1)

1.81 (1.00–3.25)*

2.04 (1.11–3.84)*

T/T (%)

11 (8.27)

10 (11.1)

2.00 (0.77–5.22)

2.38 (0.86–6.61)





C/T + T/T (%)

78 (58.6)

65 (72.2)

1.83 (1.03–3.25)*

2.09 (1.15–3.87)*


C/C (%)

51 (46.4)

13 (24.5)

C/T (%)

48 (43.6)

36 (67.9)

2.94 (1.41–6.15)**

2.91 (1.40–6.32)**

T/T (%)

11 (10.0)

4 (7.55)

1.42 (0.41–4.95)

1.45 (0.36–5.10)





C/T + T/T (%)

59 (53.6)

40 (75.5)

2.66 (1.29–5.46)**

2.63 (1.29–5.62)**

Crude OR and adjusted OR for age and smoking status were estimated using Chi square statistic and multivariate logistic regression, respectively

95 % CI 95 % confidence interval, OR odds ratio

* P < 0.05, ** P < 0.01


A significant association was observed between the TAP1 genotype and CRC (C/C vs. C/T + T/T, adjusted OR, 2.22, P < 0.01). Although the C/T genotype was significantly associated with CRC (adjusted OR, 2.27, P < 0.01), a significant association was not observed for the T/T genotype (adjusted OR, 1.95, P = 0.093). Stratified analyses were conducted to exclude the effects of gender and smoking status, (Tables 3 and 4) and a significant association was observed between the TAP1 genotype and CRC.

However, a significant association with CRC was observed only in the C/T genotype, not in the T/T genotype. It is likely that there were not enough participants to ensure the statistical power to detect an association between the T/T genotype and CRC. The statistical power of the Chi square test between the C/C and T/T genotypes was calculated to be 0.31, although it was recommended to be larger than 0.8 [19, 20]. In contrast, the statistical powers of the Chi square test among all genotypes (C/C, C/T, and T/T), and between the C/C and C/T genotypes, were estimated to be 0.92 and 0.92, respectively. It is possible that the T/T genotype shows a stronger immunity than the C/T genotype and thus immune escape occurs less often in that genotype. Recent studies on TAP1-deficient mice indicate the existence of a compensatory mechanism. CD8+ T cells have been reported to play an important role in tumor surveillance by the immune system [21, 22]. Although the population of CD8+ T cells was diminished in the TAP1-deficient mice, compensatory increases in CD3+ and CD4+ T cell populations were observed [23]. Furthermore, the TAP1-independent pathway compensates for antigen processing and CD8+ T cells functioned normally even in the TAP1-deficient mice in vivo [24]. It is likely that some compensatory function would work only in the case of the T/T genotype as the functional deficit of the C/T genotype in the TAP1 gene might not be enough to drive a compensating network. As a result, the immune escape of tumor cells could more likely be tolerated by the C/T genotype.

The TAP protein plays an important role in antigen presentation mediated by MHC class I and this process is considered essential for immune surveillance against tumors and pathogens. The impairment of TAP function in tumor cells that induces loss of downregulation of class I molecules on the cell surface is considered one of the main mechanisms of immune escape in a variety of tumors [13, 2531]. Furthermore, TAP1 gene polymorphisms lead to the loss of MHC class I antigen processing ability [32]. The TAP gene is also considered a member of the ATP-binding cassette superfamily, which is associated with membrane transportation of solutes such as ions. Molecules belonging to the ATP-binding cassette family have nucleotide-binding domains and interact with other molecules involved in genetic events, such as chromosome maintenance and DNA repair [3335]. Our results indicate that the polymorphisms (rs735883) located on the intron 7 of the TAP1 gene were associated with CRC but the functional mechanism remains to be elucidated. One likely mechanism is exon skipping. According to a previous study, the SNP located on the intron could cause exon skipping and aberrant RNA splicing [36]. Furthermore, the E2F8 binding motif (TTTGCCGC) is located on intron 7 in the TAP1 gene. E2F8 is a transcription factor that belongs to the E2F superfamily and regulates the expression of genes related to the cell cycle and apoptosis [37]. In the case of the T allele, the cytosine at the 3′ end is substituted to thymine and the aberrant binding site (TTTGCCGT) is generated. E2Fs generally bind to the binding site located on the promoter region or intron 1 [38] but it may be that the efficacy of splicing is affected by decreasing frequencies of E2F8 binding to the aberrant binding site. Further study is essential to clarify the functional mechanism that associates the polymorphisms (rs735883) with CRC.

The adjusted OR of the alcohol dehydrogenase enzyme (ADH2) gene was 1.92 (95 % CI, 1.06–3.46) in a Japanese population [39]. Another study reported that the adjusted OR of the RAD18 gene, which is associated with DNA repair, was 2.10 (95 % CI, 1.00–4.40) [40]. An increased number of SNPs associated with CRC were identified recently in a genome-wide association study [4143]. It is likely that a genetic difference may not be observed when exposure to a carcinogen is great [44, 45]. In fact, the differences among genotypes are easily observed in non-smokers or light smokers compared to smokers [46, 47]. In the present study, a significant difference was observed in the C/T genotype and the mutation type (C/T + T/T) despite stratification by smoking status. Thus, the TAP1 genotype was strongly associated with susceptibility to CRC. As the adjusted ORs for the C/T genotype and the mutation type (C/T + T/T) were larger in the “smoker” group than in the “never” group, it is possible that the TAP1 gene polymorphism interacts with smoking status. The interaction between the TAP1 genotype and smoking status was introduced into a logistic regression model. However, the interaction was not significant. Similarly, the adjusted ORs for the C/T genotype and the mutation type (C/T + T/T) were larger in the female group than in the male group although the TAP1 gene is located not on the sex chromosomes but on chromosome 6. Although the interaction between the TAP1 genotype and gender was also introduced into a logistic regression model, it was not statistically significant either.

One of the limitations of our study was smoking status. The percentage of “smokers” (46.9 %) was less than that of controls (53.9 %) although it was not a significant difference. The rate of cigarette smoking decreases every year in Japan. The rate among males of age 60–69 years was 48.1 % in 1992 and 34.8 % in 2006. Higher rates may be found in certain regions. It is likely that there were fewer smokers in the present study because the recruitment period was longer for the cases than for the controls. It is also possible that the exact information about smoking status was not acquired for the cases. To adjust and exclude the effects of smoking, we conducted a logistic regression model analysis and a stratification analysis. The other limitation of our study was the diagnostic accuracy. The period for recruitment was 14 years, and the diagnostic accuracy is likely to have changed with marked improvements in medical examinations. Hence, the potential cases that were not diagnosed at that time might be included in the controls. However, CRC screening by the local authority began in 1992, at which point recruitment of cases also started. Furthermore, the controls were not healthy controls but clinical controls in our study. This limitation had less of an impact on the results of our study.

A significant association was found between the TAP1 gene polymorphisms (rs735883) and CRC. This suggests that those with the risk allele (T) have a higher susceptibility to CRC. In order to promote the high risk approach against the onset of CRC, additional studies of the association between rs735883 and other SNPs and between rs735883 and other carcinogens are required.



This study was supported by a Grant from the University of Miyazaki. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. This work was supported by the Faculty of Medicine of both the University of Miyazaki and the University of Occupational and Environmental Health. We give special thanks to Dr. Kazuo Chijiiwa, the staff and patients of the two facilities.

Conflict of interest

We declare that none of the authors hold any financial or personal relationship with other people or organizations that could have inappropriately influenced this study.

Authors’ Affiliations

Department of Public Health, Faculty of Medicine, University of Miyazaki, Miyazaki, Japan
Department of Surgical Oncology and Regulation of Organ Function, Faculty of Medicine, University of Miyazaki, Miyazaki, Japan


  1. Ferlay J, Shin H-R, Bray F, Forman D, Mathers C, Parkin DM. Estimates of worldwide burden of cancer in 2008: GLOBOCAN 2008. Int J Cancer. 2010;127:2893–917.PubMedView ArticleGoogle Scholar
  2. Matsuda A, Matsuda T, Shibata A, Katanoda K, Sobue T, Nishimoto H. Cancer incidence and incidence rates in Japan in 2007: a study of 21 population-based cancer registries for the Monitoring of Cancer Incidence in Japan (MCIJ) project. Jpn J Clin Oncol. 2013;43:328–36.PubMedView ArticleGoogle Scholar
  3. Chao A, Thun MJ, Connell CJ, McCullough ML, Jacobs EJ, Flanders WD, et al. Meat consumption and risk of colorectal cancer. JAMA. 2005;293:172–82.PubMedView ArticleGoogle Scholar
  4. Phipps AI, Baron J, Newcomb PA. Prediagnostic smoking history, alcohol consumption, and colorectal cancer survival: the Seattle Colon Cancer Family Registry. Cancer. 2011;117:4948–57.PubMed CentralPubMedView ArticleGoogle Scholar
  5. Duncan JL, Kyle J. Family incidence of carcinoma of the colon and rectum in north-east Scotland. Gut. 1982;23:169–71.PubMed CentralPubMedView ArticleGoogle Scholar
  6. de Ponz Leon M, Sassatelli R, Sacchetti C, Zanghieri G, Scalmati A, Roncucci L. Familial aggregation of tumors in the three-year experience of a population-based colorectal cancer registry. Cancer Res. 1989;49:4344–8.Google Scholar
  7. Gerdes H, Gillin JS, Zimbalist E, Urmacher C, Lipkin M, Winawer SJ. Expansion of the epithelial cell proliferative compartment and frequency of adenomatous polyps in the colon correlate with the strength of family history of colorectal cancer. Cancer Res. 1993;53:279–82.PubMedGoogle Scholar
  8. Le Marchand L, Zhao LP, Quiaoit F, Wilkens LR, Kolonel LN. Family history and risk of colorectal cancer in the multiethnic population of Hawaii. Am J Epidemiol. 1996;144:1122–8.PubMedView ArticleGoogle Scholar
  9. Jass JR, Stewart SM. Evolution of hereditary non-polyposis colorectal cancer. Gut. 1992;33:783–6.PubMed CentralPubMedView ArticleGoogle Scholar
  10. Ritz U, Seliger B. The transporter associated with antigen processing (TAP): structural integrity, expression, function, and its clinical relevance. Mol Med. 2001;7:149–58.PubMed CentralPubMedGoogle Scholar
  11. Cresswell P, Bangia N, Dick T, Diedrich G. The nature of the MHC class I peptide loading complex. Immunol Rev. 1999;172:21–8.PubMedView ArticleGoogle Scholar
  12. Antoniou AN, Powis SJ, Elliott T. Assembly and export of MHC class I peptide ligands. Curr Opin Immunol. 2003;15:75–81.PubMedView ArticleGoogle Scholar
  13. Seliger B, Maeurer MJ, Ferrone S. TAP off–tumors on. Immunol Today. 1997;18:292–9.PubMedGoogle Scholar
  14. McCluskey J, Rossjohn J, Purcell AW. TAP genes and immunity. Curr Opin Immunol. 2004;16:651–9.PubMedView ArticleGoogle Scholar
  15. Casp CB, She J-X, McCormack WT. Genes of the LMP/TAP cluster are associated with the human autoimmune disease vitiligo. Genes Immun. 2003;4:492–9.PubMedView ArticleGoogle Scholar
  16. Hassen E, Farhat K, Gabbouj S, Jalbout M, Bouaouina N, Chouchane L. TAP1 gene polymorphisms and nasopharyngeal carcinoma risk in a Tunisian population. Cancer Genet Cytogenet. 2007;175:41–6.PubMedView ArticleGoogle Scholar
  17. Yang T, Lapinski PE, Zhao H, Zhou Q, Zhang H, Raghavan M, et al. A rare transporter associated with antigen processing polymorphism overpresented in HLAlow colon cancer reveals the functional significance of the signature domain in antigen processing. Clin Cancer Res. 2005;11:3614–23.PubMedView ArticleGoogle Scholar
  18. Johnson CM, Wei C, Ensor JE, Smolenski DJ, Amos CI, Levin B, et al. Meta-analyses of colorectal cancer risk factors. Cancer Causes Control. 2013;24:1207–22.PubMedView ArticleGoogle Scholar
  19. J Cohen. Statistical power analysis. Curr Dir Psychol Sci. 1992;1:98–101.View ArticleGoogle Scholar
  20. Cohen J. A power primer. Psychol Bull. 1992;112:155–9.PubMedView ArticleGoogle Scholar
  21. Nagaraj S, Gupta K, Pisarev V, Kinarsky L, Sherman S, Kang L, et al. Altered recognition of antigen is a mechanism of CD8+ T cell tolerance in cancer. Nat Med. 2007;13:828–35.PubMed CentralPubMedView ArticleGoogle Scholar
  22. Afshar-Sterle S, Zotos D, Bernard NJ, Scherger AK, Rödling L, Alsop AE, et al. Fas ligand-mediated immune surveillance by T cells is essential for the control of spontaneous B cell lymphomas. Nat Med. 2014;20(3):283–90.PubMedView ArticleGoogle Scholar
  23. Kolbus D, Ljungcrantz I, Söderberg I, Alm R, Björkbacka H, Nilsson J, et al. TAP1-deficiency does not alter atherosclerosis development in Apoe-/- mice. PLoS One. 2012;7:e33932.PubMed CentralPubMedView ArticleGoogle Scholar
  24. Medina F, Ramos M, Iborra S, de León P, Rodríguez-Castro M, Del Val M. Furin-processed antigens targeted to the secretory route elicit functional TAP1-/-CD8+ T lymphocytes in vivo. J Immunol. 2009;183:4639–47.PubMedView ArticleGoogle Scholar
  25. Kasajima A, Sers C, Sasano H, Jöhrens K, Stenzinger A, Noske A, et al. Down-regulation of the antigen processing machinery is linked to a loss of inflammatory response in colorectal cancer. Hum Pathol. 2010;41:1758–69.PubMedView ArticleGoogle Scholar
  26. Seliger B. Molecular mechanisms of MHC class I abnormalities and APM components in human tumors. Cancer Immunol Immunother. 2008;57:1719–26.PubMedView ArticleGoogle Scholar
  27. Lankat-Buttgereit B, Tampé R. The transporter associated with antigen processing: function and implications in human diseases. Physiol Rev. 2002;82:187–204.PubMedGoogle Scholar
  28. Zheng P, Sarma S, Guo Y, Liu Y. Two mechanisms for tumor evasion of preexisting cytotoxic T-cell responses: lessons from recurrent tumors. Cancer Res. 1999;59:3461–7.PubMedGoogle Scholar
  29. Zheng P, Guo Y, Niu Q, Levy DE, Dyck JA, Lu S, et al. Proto-oncogene PML controls genes devoted to MHC class I antigen presentation. Nature. 1998;396:373–6.PubMedView ArticleGoogle Scholar
  30. Seliger B, Ritz U, Abele R, Bock M, Tampé R, Sutter G, et al. Immune escape of melanoma: first evidence of structural alterations in two distinct components of the MHC class I antigen processing pathway. Cancer Res. 2001;61:8647–50.PubMedGoogle Scholar
  31. Yang T, McNally BA, Ferrone S, Liu Y, Zheng P. A single-nucleotide deletion leads to rapid degradation of TAP-1 mRNA in a melanoma cell line. J Biol Chem. 2003;278:15291–6.PubMedView ArticleGoogle Scholar
  32. Chen HL, Gabrilovich D, Tampé R, Girgis KR, Nadaf S, Carbone DP. A functionally defective allele of TAP1 results in loss of MHC class I antigen presentation in a human lung cancer. Nat Genet. 1996;13:210–3.PubMedView ArticleGoogle Scholar
  33. Borst P, Elferink RO. Mammalian ABC transporters in health and disease. Annu Rev Biochem. 2002;71:537–92.PubMedView ArticleGoogle Scholar
  34. Holland IB, Blight MA. ABC-ATPases, adaptable energy generators fuelling transmembrane movement of a variety of molecules in organisms from bacteria to humans. J Mol Biol. 1999;293:381–99.PubMedView ArticleGoogle Scholar
  35. Hopfner KP, Karcher A, Shin DS, Craig L, Arthur LM, Carney JP, et al. Structural biology of Rad50 ATPase: ATP-driven conformational control in DNA double-strand break repair and the ABC-ATPase superfamily. Cell. 2000;101:789–800.PubMedView ArticleGoogle Scholar
  36. Khan SG, Muniz-Medina V, Shahlavi T, Baker CC, Inui H, Ueda T, et al. The human XPC DNA repair gene: arrangement, splice site information content and influence of a single nucleotide polymorphism in a splice acceptor site on alternative splicing and function. Nucleic Acids Res. 2002;30:3624–31.PubMed CentralPubMedView ArticleGoogle Scholar
  37. Xanthoulis A, Tiniakos DG. E2F transcription factors and digestive system malignancies: how much do we know? World J Gastroenterol. 2013;19:3189–98.PubMed CentralPubMedView ArticleGoogle Scholar
  38. Wong JV, Dong P, Nevins JR, Mathey-Prevot B, You L. Network calisthenics: control of E2F dynamics in cell cycle entry. Cell Cycle. 2011;10:3086–94.PubMed CentralPubMedView ArticleGoogle Scholar
  39. Matsuo K, Wakai K, Hirose K, Ito H, Saito T, Suzuki T, et al. A gene–gene interaction between ALDH2 Glu487Lys and ADH2 His47Arg polymorphisms regarding the risk of colorectal cancer in Japan. Carcinogenesis. 2006;27:1018–23.PubMedView ArticleGoogle Scholar
  40. Kanzaki H, Ouchida M, Hanafusa H, Sakai A, Yamamoto H, Suzuki H, et al. Single nucleotide polymorphism in the RAD18 gene and risk of colorectal cancer in the Japanese population. Oncol Rep. 2007;18:1171–5.PubMedGoogle Scholar
  41. Jaeger E, Webb E, Howarth K, Carvajal-Carmona L, Rowan A, Broderick P, et al. Common genetic variants at the CRAC1 (HMPS) locus on chromosome 15q13.3 influence colorectal cancer risk. Nat Genet. 2008;40:26–8.PubMedView ArticleGoogle Scholar
  42. Houlston RS, Webb E, Broderick P, Pittman AM, Di Bernardo MC, Lubbe S, et al. Meta-analysis of genome-wide association data identifies four new susceptibility loci for colorectal cancer. Nat Genet. 2008;40:1426–35.PubMedView ArticleGoogle Scholar
  43. Peters U, Jiao S, Schumacher FR, Hutter CM, Aragaki AK, Baron JA, et al. Identification of genetic susceptibility loci for colorectal tumors in a genome-wide meta-analysis. Gastroenterology. 2013;144:799–807.PubMed CentralPubMedView ArticleGoogle Scholar
  44. Khoury MJ, Adams MJ, Flanders WD. An epidemiologic approach to ecogenetics. Am J Hum Genet. 1988;42:89–95.PubMed CentralPubMedGoogle Scholar
  45. Amos CI, Caporaso NE, Weston A. Host factors in lung cancer risk: a review of interdisciplinary studies. Cancer Epidemiol Biomarkers Prev. 1992;1:505–13.PubMedGoogle Scholar
  46. Wang YC, Chen CY, Chen SK, Chang YY, Lin P. p53 codon 72 polymorphism in Taiwanese lung cancer patients: association with lung cancer susceptibility and prognosis. Clin Cancer Res. 1999;5:129–34.PubMedGoogle Scholar
  47. Arizono K, Osada Y, Kuroda Y. DNA repair gene hOGG1 codon 326 and XRCC1 codon 399 polymorphisms and bladder cancer risk in a Japanese population. Jpn J Clin Oncol. 2008;38:186–91.PubMedView ArticleGoogle Scholar


© The Japanese Society for Hygiene 2014