• colorectal cancer;
  • chronic inflammation;
  • NSAIDs;
  • polymorphism


  1. Top of page
  2. Abstract
  3. Material and Methods
  4. Results
  5. Discussion
  6. Acknowledgements
  7. References
  8. Supporting Information

Chronic inflammation is an established risk factor for colorectal cancer (CRC), and polymorphisms in genes regulating inflammatory processes appear to alter the risk for neoplasia and the efficacy of nonsteroidal anti-inflammatory drugs in CRC chemoprevention. We examined the association between selected inflammation gene polymorphisms and CRC risk. In a large population-based case-control study with 1,795 CRC cases and 1,805 controls from the German DACHS study, we evaluated 5 putative functional inflammatory pathway polymorphisms in PRODH, PTGS1 and UBD genes. PTGS1 G213G was significantly associated with an increased CRC risk [odds ratio (OR), 1.19; 95% confidence interval (CI), 1.03–1.39; p = 0.02] comparing minor allele carriers with major allele homozygotes. This risk estimate was consistent across locations and stages of CRC (range of ORs, 1.15–1.20). Carriage of the minor allele of UBD I68T was significantly associated with advanced stages of CRC and with CRC below 65 years of age (OR, 1.23; 95% CI, 1.04–1.45; p = 0.02 and OR, 1.32; 95% CI, 1.05–1.67; p = 0.02, respectively). Our results support a role of variants in inflammatory pathway genes in CRC susceptibility and progression.

Colorectal cancer (CRC) is the 3rd most common malignant neoplasm and the 4th leading cause of cancer deaths worldwide.1–4 Colon and rectum cancers accounted for about 1 million new cases in 2002 (9.4% of all new cases of cancer).1 CRC incidence and mortality are higher in men than in women with age-standardized mortality rates adding up to 10.2 and 7.6 per 100,000, respectively, derived from 529,000 deaths in total.1 There are striking variations in the risk of different cancers by geographic area.1 Most of the international variation, however, is due to the exposure to known or suspected risk factors related to lifestyle, environment and genetics.4–6

Chronic inflammation is a well-established CRC risk factor.7 Various inflammatory bowel diseases, such as chronic ulcerative colitis and Crohn's disease, predispose to CRC.8, 9 Further evidence for the significance of inflammation during neoplastic progression, however, comes from studies of cancer risk among long-term users of aspirin and other nonsteroidal anti-inflammatory drugs (NSAIDs), which indicate that the regular use of NSAIDs significantly reduces risk of CRC and adenomas.10–18 In addition, sustained NSAID use is associated with a 30–50% lowered risk of sporadic adenomatous polyps, the presumed precancerous colorectal lesions.19 NSAIDs exert anti-inflammatory effects by inhibiting cyclooxygenase (COX) enzymes.11 COX enzymes or prostaglandin endoperoxide synthases (PTGS) are rate-limiting enzymes that convert free arachidonic acid into prostaglandins.7, 11, 20PTGS1 is constitutively expressed in various cells and tissues, fulfilling housekeeping functions, such as vascular homeostasis and platelet aggregation.11PTGS2 is an immediate–early response gene that is induced in response to mitogens, tumor promoters, cytokines, growth factors and inflammatory stimuli.11 Research has predominantly focused on PTGS2 polymorphisms,16, 21, 22 albeit PTGS1 polymorphisms have been suggested to alter the risk of colorectal neoplasia as well.16, 23 Homologous disruption of either Ptgs1 or Ptgs2 reduced polyp formation in Min/+ mice by about 80%, indicating a key role for PTGS1 and PTGS2 in intestinal tumorigenesis and identifying PTGS1 as chemotherapeutic target for NSAIDs.24 Genetic variation within the PTGS1 locus has been demonstrated to improve the effective use of acetylsalicylic acid by customizing dosage with individuals' genetic variation.25–28

There is strong evidence that a multitude of inflammatory pathway genes play a role in CRC development.29–37 As a member of the ubiquitin-like modifier family, UBD (diubiquitin or ubiquitin D, also referred to as FAT10) has been associated with antigen presentation, cytokine response, apoptosis and mitosis.34 A consistent overexpression of UBD in 90% of hepatocellular carcinoma, 55–80% of gynecological cancers, 73% of gastric cancer and over 85% in colon and rectal cancers was reported.30–33UBD regulation, however, was shown to be diverse.30–34 First, UBD expression is negatively regulated by p53.34 As UBD is capable of binding MAD2, a spindle assembly checkpoint protein, Zhang et al.34 hypothesize that the repression of UBD may facilitate the interaction of MAD2 with cdc20 to induce mitotic arrest. As a consequence of inappropriate p53 function during tumorigenesis, UBD is overexpressed,33–35 which results in excess MAD2-binding UBD, leading to the deregulation of mitosis by reducing kinetochore localization of MAD2 in the prometaphase.33, 34 Second, cytokines tumor necrosis factor alpha (TNF-α) and interferon gamma (IFN-γ) synergistically upregulate UBD expression in liver and colon cancer cells from 10- to 100-fold, pointing toward a function of UBD in the proinflammatory immune response.30

Recently, Castellanos-Rubio et al.35 were the first to show a correlation between the regulatory UBD single nucleotide polymorphism (SNP) rs11724 and disease development. SNP rs11724 was associated with both increased UBD mRNA levels and an increased risk of celiac disease, a chronic, immune-mediated enteropathy.35

The expression of the mitochondrial membrane flavoenzyme proline dehydroxygenase PRODH gene [also designated proline oxidase (POX)] has been shown to be upregulated under 3 stress situations: it is induced by p53, the major mechanism for signaling genotoxic stress, by modulating the mammalian target of rapamycin mTOR protein, the primary signaling mechanism for nutrient stress and by peroxisomal PPARγ and by its pharmacologic ligands, a signaling system responding to inflammatory stress.36 Thus, PRODH has been observed to suppress PTGS2/prostaglandin E2 (PGE2) expression and activities, causing apoptotic cell death, and to inhibit tumor growth in CRCs, suggesting that PRODH may play a tumor suppressor role.36, 37 According to Bender et al.,38 PRODH missense mutations have been reported to provoke reduction in PRODH activity and to be associated with schizophrenia risk.38–41

To test the hypothesis that polymorphisms in PTGS1, UBD and PRODH affect CRC risk, we analyzed selected putative functional candidate SNPs, PRODH R185W (rs4819756), PTGS1 R8W (rs1236913), PTGS1 G213G (rs5788), UBD I68T (rs2076485) and UBD S160C (rs8337) in a large population-based case-control study.

Material and Methods

  1. Top of page
  2. Abstract
  3. Material and Methods
  4. Results
  5. Discussion
  6. Acknowledgements
  7. References
  8. Supporting Information

Study population

CRC cases and controls were drawn from the German Darmkrebs: Chancen der Verhütung durch Screening (DACHS) study, a large population-based case-control study carried out in the Rhine-Neckar-Odenwald and Heilbronn regions in the southwest of Germany.42, 43 The analyses comprised 1,795 unrelated male and female case patients (33–94 years of age, median = 69) with a first histologically confirmed diagnosis of CRC (ICD-10 codes C18–C20) diagnosed between January 2003 and December 2007. Controls consisted of 1,805 individuals (34–98 years of age, median = 70) who were randomly selected from lists of residents supplied by population registries and frequency-matched to cases by 5-year age groups, sex and county of residence. Case patients and control individuals were eligible if they were ≥30 years of age, had lived in the study region, were German-speaking and were mentally and physically able to participate in a personal interview of about 1 hr. All of them gave written informed consent.

The study was approved by the ethics committees of the University of Heidelberg and the State Medical Boards of Baden-Wuerttemberg and Rhineland-Palatinate, Germany.

Data collection

Details of the data collection procedures for the DACHS study are reported elsewhere.42, 43 Briefly, the study subjects were asked to participate in an in-person interview and to donate a blood sample. In rare cases in which blood samples were not available, a mouthwash was taken. Information on demographic factors, anthropometric measures, medical history, including medication and screening, CRC family history, reproductive history and lifestyle factors (such as smoking, nutrition and physical activity) was collected by trained interviewers using a standardized questionnaire.

SNP selection

Candidate SNPs were selected by means of well-defined methods and criteria (Supporting Information Table 1). In brief, public literature resources and databases—NCBI PubMed, dbSNP and GeneCards—were searched for epigenetically modulated and/or CRC-related candidate genes and previous epidemiologic findings, indicating associations with cancer susceptibility. SNPs were tested for evolutionary conservation at least among human, mouse and rat (WU-BLAST2)44 and putative microRNA targets (PicTar).45 Putative functional effects of the nonsynonymous SNPs were predicted by PolyPhen, SIFT, SNPs3D and FastSNP.46–50

SNPs with a minor allele frequency ≥0.05 in the HapMap CEU population [Utah residents with northern and western European ancestries from the Centre d'Etude du Polymorphisme Humain (CEPH) collection] were included in the study. Further basic selection criterion was r2 ≤ 0.8, excluding strong linkage disequilibrium (LD) between adjacent variants.51

The final selection included 1 synonymous and 4 nonsynonymous inflammatory pathway SNPs, PRODH R185W (rs4819756), PTGS1 R8W (rs1236913), PTGS1 G213G (rs5788), UBD I68T (rs2076485) and UBD S160C (rs8337).

DNA preparation and genotyping

Genetic analysis was performed as described before.43, 52 Genomic DNA was isolated from blood and mouthwash samples with FlexiGene DNA and QIAamp DNA Mini Kits (Qiagen GmbH, Hilden, Germany), respectively. Sequenom's MassARRAY® system (Sequenom, San Diego, CA) was applied for genotyping, performing iPLEX® single base primer extension and matrix-assisted laser desorption ionization time-of-flight mass spectrometry as described elsewhere.52 Genotyping calls were made in real time with the MassARRAY® RT software.

A random selection of >5% of all samples was genotyped twice for quality control. Successfully genotyped duplicate samples displayed an average concordance rate of 98.9% for the 5 SNPs.


Each SNP was tested for deviation from Hardy-Weinberg equilibrium (HWE) in controls by comparing the observed and expected genotype frequencies using Pearson's χ2 tests with 1 degree of freedom. Unconditional logistic regression was applied to estimate odds ratios (ORs) and corresponding 95% confidence intervals (95% CIs), adjusted for matching factors age and sex, using dominant and co-dominant models, respectively. Tests for linear trend were additionally used. All tests were 2-sided and considered statistically significant with p < 0.05. Subsite analyses were conducted for colon and rectal cancers and CRC stages.

The aforementioned analyses were carried out using Statistical Analysis Software (SAS) version 9.1 (SAS Institute, Cary, NC).

Haploview was used to examine measures of LD (D′ and r2) between adjacent SNPs and to define haplotype block structures based on the definition by Gabriel et al.51 Power calculations were used with the power and sample size software PS version


  1. Top of page
  2. Abstract
  3. Material and Methods
  4. Results
  5. Discussion
  6. Acknowledgements
  7. References
  8. Supporting Information

Table 1 shows the main characteristics of the DACHS study population. The majority of cases and controls were males between 60 and 79 years of age (median ages: 69 and 70 years, respectively). A family history of CRC was slightly more common among cases than among controls. Two-thirds of cancers were located in the sigmoid colon and rectum and slightly more than half were diagnosed at Stages I or II.

Table 1. Characteristics of the DACHS study population
inline image

The average call rate for the 5 SNPs analyzed was 97.2%, and it did not differ between cases and controls for any individual assay. Using Pearson's goodness-of-fit χ2-tests with 1 degree of freedom, allele frequencies among controls were consistent with HWE for all the SNPs (p ≥ 0.05).

Applying the dominant model (comparing carriers of the variant allele with wild type homozygous subjects), ORs were 0.99 (95% CI, 0.86–1.13) for rs4819756, 0.93 (95% CI, 0.77–1.12) for rs1236913, 1.19 (95% CI, 1.03–1.39) for rs5788, 1.06 (95% CI, 0.93–1.21) for rs2076485 and 0.99 (95% CI, 0.87–1.13) for rs8337 (Table 2). ORs for PTGS1 G213G (rs5788) were of similar magnitude for colon and rectum cancers (Table 3) and early- and late-stage cancers (Table 4); yet, statistical significance was reached only for colon cancer in the subsite analyses.

Table 2. Inflammatory polymorphisms and their associations with colorectal cancer (CRC)
inline image
Table 3. Inflammatory polymorphisms and their associations with colon and rectal cancers
inline image
Table 4. Inflammatory polymorphisms and their associations with CRC UICC stages
inline image

Notably, UBD I68T (rs2076485) was significantly associated with advanced stages of CRC (OR, 1.23; 95% CI, 1.04–1.45; p = 0.02; Table 4) and with CRC in participants below 65 years of age ([CC + TC vs. TT]: OR, 1.32; 95% CI, 1.05–1.67; p = 0.02; data not shown). Furthermore, marginal associations for both UBD SNPs and risk of rectal cancer were detected (Table 3).


  1. Top of page
  2. Abstract
  3. Material and Methods
  4. Results
  5. Discussion
  6. Acknowledgements
  7. References
  8. Supporting Information

In this large case-control study, we evaluated the association of a number of inflammation-related SNPs with CRC risk. We observed associations of PTGS1 G213G with risk of CRC which were consistent across subgroups, and of UBD I68T with risk of late-stage CRC and CRC in the age group of below 65 years. These findings support our hypothesis that genetic variability in inflammation-related genes may modify the risk of CRC.

The causal relationship between chronic inflammation and the development and progression of several gastrointestinal cancers is widely accepted.7, 20 Several inflammatory conditions like ulcerative colitis and Crohn's disease confer an overall risk of small bowel cancer and CRC.8, 9 Clinical and epidemiologic studies supply best evidence for the link between inflammation and tumor progression, showing that regular use of NSAIDs favors the reduction of colorectal neoplasia.7, 10–18 The capability of NSAIDs, such as aspirin or flurbiprofen, to suppress inflammatory reactions is assigned to the inhibition of PTGS (PTGS1 and PTGS2).7 PTGS1 and PTGS2 are rate-limiting enzymes that catalyze the formation of prostaglandins from arachidonic acid.7, 16 While PTGS1 is ubiquitously expressed and sustains housekeeping functions and platelet aggregation,7, 11PTGS2 is an early-response gene that is normally absent from most cells but induced at sites of inflammation in response to inflammatory stimuli, including mitogens, tumor promoters, growth factors, tumor development, stress-inducing agents and cytokines.7, 11, 20 Because PTGS2 has been localized to both tumor epithelial cells and adjacent stromal cells, PTGS2-derived prostaglandins are supposed to act on malignant epithelial cells or on the surrounding stroma, promoting tumor progression.20 Therefore, the majority of epidemiologic studies has focused on PTGS2 polymorphisms, investigating both their effects on colorectal neoplasia and their ability to alter the efficacy of NSAIDs in chemoprevention and disease recurrence.29 The results, however, have remained controversial.21, 54–59

Interestingly, deficiencies of both Ptgs1 and Ptgs2, orthologs of the human PTGS1 and PTGS2 genes, cause a decrease of intestinal tumorigenesis in the Min/+ mouse by 77 and 84%, respectively,24 considering PTGS1 as chemotherapeutic target for NSAIDs as well.26, 60 To date, however, few PTGS1 polymorphisms have been evaluated for an interaction with NSAID exposure on the risk for colorectal neoplasia.26–29, 60, 61 Remarkably, NSAID use was shown to associate with adenoma risk reduction among PTGS1 P17L wild type NSAID users compared to wild type nonusers,61 and a haplotype containing PTGS1 842A>G, R8W and L237M was associated with differential aspirin response,27 emphasizing the role of PTGS1 in colorectal neoplasia.

The present population-based case-control study revealed a significant association of PTGS1 G213G (rs5788) and CRC risk (OR, 1.19; 95% CI, 1.03–1.39; p = 0.02; Table 2). The finding is in line with the result by Küry et al., showing PTGS1 G213G to be associated with an increased CRC risk in a French case-control genetic association study.54 The effect of the synonymous PTGS1 G213G on protein function remains elusive and requires functional studies. According to the definition by Gabriel et al.,51PTGS1 G213G is located within an LD block that spans 9 kb (37.5% of the gene). Whether this SNP exhibits LD with a causative variant within this haplotype block awaits further investigation. Given the architecture of the genomic region surrounding of the PTGS1 locus, however, it appears unlikely that G213G is associated with a functional SNP in a neighboring gene. Yet, one might presume that an aberration from the preferred codon usage influences translational and/or transcriptional efficiency as well as translational accuracy and splicing regulation.50, 62, 63

Besides, we are the first to analyze the effects of polymorphisms in PRODH and UBD with CRC risk. Although we found no association of PRODH R185W (rs4819756), UBD I68T (rs2076485) was identified to be associated with advanced CRC stages (Table 4) and with a CRC diagnosis below 65 years of age. Furthermore, marginal associations for UBD I68T and S160C with rectal cancer risk were found (Table 3), pointing to the potential role of UBD and its variants in carcinogenesis.30–33, 35 These observations need to be handled carefully, and they require further investigation.

Our study has several strengths and limitations. The strengths include its population-based design, the well-defined, homogeneous study population and a sound sample size. In addition, we included a number of well-founded SNPs for which an association with CRC is biologically plausible and/or has been previously reported for any forms of cancer. With the present sample size, we had a power of 80% at a significance level of 0.05 to detect ORs ≥ 1.23 (PRODH R185W), 1.30 (PTGS1 R8W), 1.24 (PTGS1 G213G), 1.22 (UBD I68T) and 1.21 (UBD S160C).53 Thus, we had sufficient power to detect associations of the magnitude observed by Küry et al. (PTGS1 G213G and CRC risk, OR = 1.24).54 Although interaction analyses regarding regular NSAID use may be of great value, the statistical power—even in this large case-control study—would be limited. At this point, we are aware that statistical significance of the observed associations may not hold on stringent correction for multiple testing. Because of the explorative nature of our study, we purposely did not perform multiple testing corrections not to eliminate potentially important results from the investigation. Therefore, our data need to be interpreted with some caution, rendering a replication of the findings in independent data sets indispensable. Further limitations of our study include the restricted number of SNPs selected and their largely unknown functional significance.

In conclusion, this large population-based case-control study supports a potential role of genetic variability in inflammatory pathway genes and colorectal carcinogenesis. Replication in further large epidemiologic studies and functional analyses are warranted to confirm and extend this preliminary evidence.


  1. Top of page
  2. Abstract
  3. Material and Methods
  4. Results
  5. Discussion
  6. Acknowledgements
  7. References
  8. Supporting Information

The authors thank the clinicians who supported the study, all patients and control individuals who participated in the study and Ute Handte-Daub and Belinda-Su Kaspereit for excellent technical assistance.


  1. Top of page
  2. Abstract
  3. Material and Methods
  4. Results
  5. Discussion
  6. Acknowledgements
  7. References
  8. Supporting Information
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Supporting Information

  1. Top of page
  2. Abstract
  3. Material and Methods
  4. Results
  5. Discussion
  6. Acknowledgements
  7. References
  8. Supporting Information

Additional Supporting Information may be found in the online version of this article.

IJC_25299_sm_SuppTable1.doc29KSupporting Information Table 1. Criteria for SNP selection.

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