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Department of Health Risk Analysis and Toxicology, Maastricht University, Maastricht, The Netherlands and * Center for Statistics, Limburg University Centre, Diepenbeek, Belgium
2To whom correspondence should be addressed. E-mail: j.vandelft{at}grat.unimaas.nl.
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ABSTRACT |
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 TOP ABSTRACT MATERIALS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
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KEY WORDS: • vegetables • microarrays • gene expression • C57Bl/6 mice • colon
Colorectal cancer (CRC)3 is one of the most common cancers worldwide, with about 943,000 new cases per year (1). Mortality has declined steadily, indicating the beneficial effects of improved therapy and early detection. However, incidence of this type of cancer is generally increasing, pointing out the need for additional preventive measures (2).
It has been suggested that up to 90% of CRC cases might be prevented by changes in diet (3). There is abundant epidemiological evidence that CRC development is strongly influenced by food and nutrition. In particular, diets high in vegetables decrease the risk of CRC (4–6). The evidence from animal studies is less clear, but the majority of these studies report that consumption of vegetables or vegetable components reduces CRC risk (4,7–10).
Although investigation of the effects of diet on the colon has intensified during the past decade (8,10–12), the genetic pathways through which vegetable components exert their effects are mostly unknown. Gene expression modulation by dietary vegetables and vegetable components has only been investigated in laboratory animals for a limited number of genes and often in relation to carcinogen exposure. Many of the molecular targets at the genome level are unknown. Furthermore, the number of experimental studies examining the effect of whole vegetables, rather than individual micronutrients or other bioactive compounds, is limited. By investigating the effect of whole vegetables, the biological availability of the compounds from the food matrix and their possible interactions can be taken into account.
The present study investigated the effects of 4 vegetables (cauliflower, carrots, peas, and onions) on gene expression in the colonic mucosa of female C57Bl/6 mice using microarray technology. These 4 vegetables were chosen because each represents a subclass of vegetables that affects carcinogenesis via a different mechanism. For protection against CRC by fiber, which is present in all vegetables and is particularly high in pulses such as beans and peas, proposed mechanisms include dilution and binding of carcinogens in the digestive tract, reduction of the transit time of fecal bulk, and inhibition of cell proliferation by the dilution of bile acids. Allium vegetables such as onions and garlic contain organosulfur compounds like diallyl sulfide that can modify carcinogen activation by inhibition of phase I biotransformation enzymes and induction of phase II detoxification enzymes. Other phytochemicals known to affect the metabolic activation of procarcinogens include isothiocyanates and indoles, breakdown products of glucosinolates present in cruciferous vegetables such as cauliflower, broccoli, cabbage, and Brussels sprouts. Induction as well as inhibition of detoxification enzymes by these compounds has been reported (13–15). Another important mechanism by which vegetables can protect against DNA damage is scavenging of reactive oxygen species and other free radicals. Antioxidants such as ß- and 
-carotene, present in orange vegetables such as carrots and pumpkins, can protect DNA from free radical damage. Furthermore, these agents can suppress cell proliferation by upregulation of connexin 43, a gene responsible for maintaining intercellular gap junctional communication, which is associated with decreased proliferation (4,5). The C57Bl/6 mouse model was chosen because it is frequently used in studies investigating dietary modulation at the molecular level, including gene expression, and it provides the basis of multiple transgenic mouse models, which can be used in future studies to investigate the specific role of a particular gene (7,9,12).
The present study took two different approaches. The first approach examined the dose-dependent effects of a mixture of 4 vegetables (cauliflower, carrots, peas, and onions) on gene expression in the colon. The second approach investigated the role of the individual vegetables present in the vegetable mixture.
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MATERIALS AND METHODS |
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TOPABSTRACTMATERIALS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
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Diets were refreshed every 2 d and provided as powdered feed. The 20% casein control diet (Hope Farms) served as the basal diet. The vegetables were purchased as a single batch at the supermarket and separately cooked under household conditions. After freezing (–20°C), the vegetables were lyophilized, ground, and combined. To obtain 1 g of lyophilized individual vegetables, 14.3 g fresh cauliflower, 13.9 g fresh carrots, 4.5 g fresh peas, and 10.3 g fresh onions were processed, respectively. Before the vegetable mixture was mixed with the basal diet, it was analyzed for macronutrient content (Hope Farms). Small differences in macronutrient content among the different diet groups were observed (data not shown). The vegetables consisted of >50% carbohydrates. The vegetable mixture was added to the basal diet at the expense of carbohydrates. Diets were adjusted for the amount of carbohydrates with dextrose/cerelose and cellulose (dicacel; Hope Farms), resulting in similar energy densities. No antioxidants or preservatives were added. After preparation, the diets were stored at –20°C in airtight plastic bags until use. The composition of the different diets is presented in Table 1.
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Tissue sampling. Mice were anesthetized with Nembutal (Sanofi Sante) and killed by bleeding the vena cava inferior. Nembutal was administered subcutaneously in the neck at a dose of 60 mg/kg body wt. The large intestine was removed and placed on a specially made plastic box, which was kept at 4°C. After removing the rectum, the colon was opened longitudinally with fine scissors, and mucus and feces were removed. Colonic mucosal cells were incubated in TrizolTM (GIBCO Life Technologies) for 3 min and scraped off the muscle layer with the edge of a sterile glass slide. Cells were transferred into 800 µL TrizolTM, homogenized by pipetting, and stored at –80°C until total RNA isolation.
Total RNA isolation and cDNA probe synthesis. Total RNA was purified from salts and residual DNA using the RNeasy® Mini Kit (Qiagen) together with a DNase treatment, according to the manufacturer’s instructions. The quantity of each RNA sample was measured with a spectrophotometer; samples ranged from 30 to 100 µg/mouse. Integrity was determined using a Bioanalyzer (Agilent Technologies). All samples contained intact total RNA with an rRNA ratio (28S:18S) > 1.5.
Total RNA pools (3/diet group) were prepared by pooling equal amounts of total RNA from 2 or 3 mice. Cyanine 3 (Cy3)- and cyanine 5 (Cy5)-labeled cDNA probes were prepared using 10 µg total RNA from each pool, by the method of Hasseman et al. (16)
cDNA microarray preparation. The present study was part of a project that is investigating the effects of vegetable consumption on gene expression in colon mucosa in humans and mice. For the human study, the expression levels of genes in colon mucosa were measured using the PHASE-1 Microarray Human-600 (PHASE-1 Molecular Toxicology) (17). A mouse cDNA microarray was constructed based on the genes present on the PHASE-1 Microarray Human-600. These genes represent a dedicated selection of biologically relevant gene sequences involved in inflammation, DNA damage and repair, oxidative stress, cell signaling, cell proliferation, metabolism, transcription, and apoptosis.
In total, 602 mouse cDNA clones from the IMAGE consortium were selected using the HomoloGene system of the National Center for Biotechnology Information (18). The largest group consisted of 358 cDNA clones from the National Institute on Aging, provided by the Genome Centre Maastricht. The remaining 244 cDNA clones were obtained from the Resource Centre/Primary Database. All cDNA clones were provided as bacterial glycerol stocks, and amplification of the cDNA inserts was performed by PCR in 96-well format in a Tgradient Thermocycler (Whatman Biometra). (See the Online Supporting Material for more information regarding cDNA microarray preparation.1)
cDNA microarray hybridizations. For the hybridizations, 3 pools were prepared for each diet group, each pool consisting of equal amounts of total RNA from 2 or 3 mice. By creating 3 pools per diet group instead of 1, biological variability could be taken into account.
For each of the 3 sets of pools from the vegetable mixture groups, a loop design was constructed with 4 microarrays, as follows: D0j 
D1j D2j D3j D0j (where D0j, D1j, D2j, and D3j denote the pools for the control, 10%, 20%, and 40% diet groups, respectively, and j = 1, 2, or 3; arrows join the samples put on the same array and indicate the sample labeled with Cy5). The loop is repeated 3 times, and then analyzed together, to reflect the possible variability in the experimental data. The statistical power of this design for estimating the dose-response profile is greater than that of the classical reference design in which each diet group is compared with the control group. Furthermore, fewer arrays are needed using this design (19). In total, 12 arrays were used. Cy3- and Cy5-labeled cDNA probes from 2 groups were mixed according to this design and hybridized to the cDNA microarray by the method of Hasseman et al. (16)
For each of the 3 sets of pools from the individual vegetable groups, a reference hybridization design was constructed (20). In this design, each vegetable group is compared with the same pool from the control group. Cy3- and Cy5-labeled cDNA probes from the vegetable group and the control group were mixed and hybridized to the cDNA microarray by the method of Hasseman et al. (16) The reference design was chosen because it allows for the same precision of all comparisons of vegetable groups against the control. To remove potential bias because of dye effects, a second microarray experiment was carried out for each couple, in which the dyes were switched (flip-dye experiment). In total, 24 cDNA microarray hybridizations were performed.
Slides were scanned on a GMS 418 Array Scanner (Affymetrix). The images obtained (resolution 10 µm; 16-bit tiff image) were processed with ImaGene 5.0 software (Biodiscovery) to measure mean signal intensities for spots and local background.
Statistical analysis.
The microarray data were analyzed using ANOVA models (21), without background correction and using base-2 logarithmic transformation of the measured intensities. Due to computational limitations, the models were fit in 2 stages. The models included a normalization step, taking into account both the global (across-genes) and local (gene-specific) normalization (22). All pairwise differences between the diet groups were examined. For each gene, the Tukey procedure was used to correct for multiple comparisons (23). To control the overall (across-genes) probability of false-positive findings, at 
5%, P < 0.0001 was considered to indicate a significant individual pairwise comparison (expected P-value after Bonferroni correction). The critical values for the Tukey procedure were selected using an empirical distribution obtained by bootstrap (24).
Statistical analysis of the body weights of the mice was carried out using SPSS version 6.1.1 for Macintosh (SPSS). Data were analyzed by means of ANOVA (single factor) followed by Student’s t test. Differences with a 2-sided P-value < 0.05 were considered significant.
Real-time RT-PCR. To verify the cDNA microarray results, 45 gene expression differences, representing 10 genes (TNFRSF6, CASP4, GAPDH, STAT1, SULT1A1, OAT, EFHU1, HSPD1, ODC, and CDKN1A) that were responsive to vegetable consumption, were analyzed by real-time RT-PCR. First strand cDNA was generated according to the SuperscriptTM II RNase H- Reverse Transcriptase protocol (Invitrogen, Life Technology) using 1 µg total RNA. Forward and reverse primers for real-time PCR amplification were designed with Primer Express software version 1.5 (Applied Biosystem) using default settings. To avoid amplification of contaminating genomic DNA, primers were chosen either residing on different exons, thereby spanning at least 1 intron in the genomic sequence, or spanning the splicing junction site, therefore containing sequences from 2 adjacent exons (Supplemental Table 1). To normalize cDNA loading and PCR variations, the signals of the selected genes were corrected with the signals from 18S mRNA as an internal reference. Therefore, the amount of target gene was divided by the amount of 18S of the sample to obtain a unitless normalized target value.
PCR reactions consisted of 12.5 µL 2x SYBR Green I qPCR Mastermix Plus (Eurogentec), 0.75 µL SYBR Green I (Eurogentec), 2.5 µL 3-µmol/L forward and reverse primer (Qiagen), 1.75 µL milliQ water, and 5 µL cDNA template (total 10 ng template for validated genes; total 16 pg template for the normalization gene 18S).
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RESULTS |
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TOPABSTRACTMATERIALS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
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Gene expression. The expression of 602 genes was measured simultaneously by means of cDNA microarrays. The genes that were differentially expressed due to the dietary treatments are presented in Table 2.
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upregulation or 
downregulation) was the same for one or more of the individual vegetables and the vegetable mixture.
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DISCUSSION |
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TOPABSTRACTMATERIALS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
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CRC develops as the result of the progressive accumulation of genetic and epigenetic alterations that lead to the transformation of normal colonic epithelium into colon adenocarcinoma, as presented in the genetic model by Vogelstein (49). At each of the presented stages, various genetic mechanisms are possible by which dietary vegetables and/or vegetable components can inhibit progression to the next stage.
The first line of defense against the initiation of CRC is the ability of the colon tissue to intercept and detoxify potentially DNA damaging xenobiotic or endogenous substances. Three genes involved in xenobiotic metabolism were affected by the vegetable mixture: SULT1A1 (sulfotransferase 1A1), GSTA2 glutathione S-transferase, 2); and ALDH1A1 (aldehyde dehydrogenase 1A1). SULT1A1 and GSTA2 both encode for phase-II biotransformation enzymes, but these enzymes differ in their mode of action. Sulfotransferase enzymes play an important role in the metabolism and bioactivation of many dietary and environmental mutagens, including heterocyclic aromatic amines (HCAs) implicated in the pathogenesis of colorectal and other cancers (25,26). In contrast to SULT1A1, the GSTA2 enzyme detoxifies HCAs by coupling to glutathion (27). In addition to these effects on phase-II biotransformation genes, dietary vegetables induced another detoxifying gene, ALDH1A1. This gene oxidizes acetaldehyde to acetetic acid and thereby protects cells from the adverse effects of acetaldehyde. There is increasing evidence that acetaldehyde is highly toxic, mutagenic, and carcinogenic (28). In contrast to the effect of the vegetable mixture on the expression of this gene, an opposite effect on gene expression occurred in the group consuming the cauliflower diet. Possibly, the total amount of vegetables rather than a particular vegetable is of importance in modulating this gene. The effects of the vegetable mixture on the expression of SULT1A1, GSTA2, and ALDH1A1 support the hypothesis that high vegetable consumption decreases colon cancer risk by inhibiting the formation and detoxification of colon carcinogens.
A gene involved in the generation of DNA reactive compounds is the HPGD gene, which was downregulated in the highest vegetable mixture group. It encodes an enzyme that metabolizes a number of prostaglandins and nonprostanoid compounds. The products of the nonprostanoid compounds are generally highly reactive ,ß-unsaturated aldehydes and ketones, which may cause carcinogenesis (29,30). The mechanism of chemoprevention by vegetables could be by inhibition of HPGD mRNA, resulting in diminished HPGD enzyme activity, leading to decreased production of the highly reactive ,ß-unsaturated aldehydes and ketones in the colon tissues.
If a genotoxic reaction with DNA has occurred, cancer initiation can still be prevented by repairing this lesion. One DNA repair gene was upregulated, namely RAD51 (31). Vegetable consumption has not previously been shown to induce this DNA repair gene, but the upregulation observed in this study suggests that vegetables could protect cells from DNA damage by increasing the DNA repair capacity.
Apoptosis (programmed cell death) is a common process in the intestinal crypt, removing differentiated cells that form the surface epithelium. In the stem cell and proliferation zone of the colorectal crypt, apoptosis occurs to a lesser extent. Here, genetically damaged stem cells are removed before they can undergo clonal expansion. Seven genes known to be involved in apoptosis were affected by vegetable consumption in the present study: TNFRSF6, CASP4, CASP7, CASP 3, CTSB, TMSB10, and STAT1. These genes were all upregulated in the colon mucosa in mice fed the 40% vegetable mixture diet, and vegetables could thereby provide a protective mechanism against neoplasia by removing through apoptosis genetically damaged stem cells before they can proliferate (32). CASP4 and TMSB10 were also modulated by the individual vegetable diets. CASP4 induction was observed in mice fed the cauliflower diet, and the effect of the vegetable mixture could be explained by the presence of cauliflower. However, TMSB10 gene expression was downregulated in the group fed the onion diet, in contrast to the inductive effect of the vegetable mixture. Possibly, the total amount of vegetables, rather than a particular vegetable, is of importance in modulating this gene.
In addition to genes involved in apoptosis, genes concerned with cell proliferation and invasion were modulated by vegetable consumption. Three genes involved in polyamine metabolism were modulated. Spermidine/spermine N1-acetyl transferase 1 (SAT), upregulated in the 40% vegetable mixture diet group, is the key enzyme in polyamine catabolism (33), causing polyamine levels to drop. Increased polyamine content is correlated with higher CRC risk. Mutations in the adenomatous polyposis coli gene result in higher polyamine levels in the intestinal mucosa (33,38,39). Ornithine aminotransferase (OAT), downregulated in the group fed the 40% vegetable mixture, is also involved in polyamine metabolism. The protein product of this gene converts ornithine to glutamate semialdehyde, thereby reducing intracellular ornithine levels. Ornithine is the substrate of ornithine decarboxylase (ODC), which is the rate-limiting enzyme in polyamine synthesis (40). A decrease in OAT activity could contribute to an increase in ornithine available for ODC, leading to increased polyamine synthesis. However, ODC gene expression in the colon was inhibited in the mice fed cauliflower or carrots. This has already been shown for ß-carotene (50) and indole-3-carbonol (51), present in carrots and cauliflower, respectively, which could explain the reduction in these two diet groups. Because of this possible bidirectional effect of vegetable consumption on polyamine metabolism and subsequent cell growth, the effect on CRC risk is not clear and requires further investigation.
Several genes involved in tumor suppression were modulated by vegetable consumption: RRM1, SLC26A3, and MYBBP1a. RRM1 provides a balanced supply of precursors for DNA synthesis and repair (34) and functions as a metastasis suppressor gene by suppressing invasion, migration, and in vivo metastasis formation (35). SLC26A3 or DRA (downregulated in adenoma) is an important tumor-suppressor gene associated with CRC risk and was upregulated in the group fed the 40% vegetable mixture. This gene is expressed exclusively in normal colon tissue (52), and it induces growth-suppression in colon cells, which is correlated with inhibition of colon tumor progression (36). However, in addition to these beneficial effects on the RRM1 and DRA tumor-suppressor genes, MYBBP1a was downregulated in the group fed the 40% vegetable mixture, which inhibits the c-myb proto-oncogene protein (37). The effect of this modulation on colon cancer development remains unclear and needs further investigation.
Comparing the results of the vegetable mixture diets and the specific vegetable diets shows that 11 similar genes were significantly modulated. (Fig. 3A–D). For 7 of these 11 genes, the effects of the vegetable mixture could be expected from the effects of the individual vegetables. The results reveal which vegetables were (mainly) accountable for these same effects. However, 6 of these 7 genes are currently not known to be involved in CRC. However, individual vegetables can modulate specific genes in favor of lower CRC risk, and this occurred in the present study in mice fed individual vegetable diets relatively low in vegetable content. Based on a literature review (33,38–48), 5 genes modulated by a particular vegetable could be involved in CRC protective mechanisms: HSPD1 (chaperonin), ODC (already discussed), CDKN1A, TOP2A, and IL18. HSPD1 was the only gene that was significantly modulated (downregulated) by all vegetables. This gene encodes for a member of the family of chaperones, which can be defined as proteins that assist other proteins to reach their final active forms (41). It could be that vegetables provide the cell with compounds that maintain homeostasis, thereby reducing the call for stress-induced proteins, resulting in their decreased transcription. CDKN1A encodes for a potent cyclin-dependent kinase (CDK) inhibitor, which binds to and inhibits the activity of cyclin-CDK2 or -CDK4 complexes, thereby preventing G1-S (42) and G2-M (43) transition, which results in cell cycle arrest. TOP2A catalyzes DNA topological reactions via a DNA breakage/reunion mechanism. The breakage/reunion reaction of TOP2A can be interrupted by many anticancer drugs, resulting in the accumulation of a topoisomerase II-DNA covalent intermediate, the cleavage complex, resulting in tumor cell death (44). IL18 was significantly upregulated in the peas group compared to the control and carrots groups. IL18 encodes for a type-1 T-helper cytokine that exhibits antitumor activities by increasing apoptosis (45,46), inhibiting angiogenesis (47), and maintaining homeostasis (48).
In summary, the vegetable mixture affected gene expression in the colon mucosa of female mice in a dose-dependent manner. The expression of only one gene (CASP4) that could be involved in CRC prevention was affected by a particular vegetable (cauliflower). The vegetable mixture modulated genes that may be involved in protective mechanisms at various stages of CRC development. A diet with a high content of the vegetable mixture modulated genes involved in inhibiting carcinogen formation, increasing DNA repair capacity, inducing apoptosis, and reducing cell growth and tumor invasion. In addition to genes that are known to be induced by vegetables (or their components), new genes were identified: the detoxification gene ALDH1A1; the DNA repair gene RAD51; the apoptosis genes TNFRSF6, CASP4, CASP7, CASP3, CTSB, TMSB10, and STAT1; the metastasis-suppressor gene RRM1; and the tumor-suppressor gene SLC26A3. These genes may play important roles in the prevention of CRC by dietary vegetables and may provide new molecular targets for CRC prevention. Several genes are of particular interest because they were modulated both in mice fed the vegetable mixture and in mice fed one of the individual vegetable diets, although their role in CRC prevention is not clear. Furthermore, individual vegetables can modulate specific genes in favor of lower CRC risk, and this occurred in the present study in mice fed individual vegetable diets relatively low in vegetable content. Almost half of the affected genes that were modulated are currently not known to be involved in CRC protective mechanisms or known to be modulated by vegetable consumption. Their possible role in CRC prevention and the modulation of their expression by vegetable consumption are not clear and require further investigation.
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FOOTNOTES |
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3 Abbreviations used: ACTB, ß actin, cytoplasmic; APC, adenomatous polyposis coli; ALDH1A1, aldehyde dehydrogenase family 1, subfamily A1; BNIP1, BCL2/adenovirus E1B 19-kDa-interacting protein 1; CASP4, caspase 4, apoptosis-related cysteine protease; CASP7, caspase 7; CASP3, caspase 3, apoptosis related cysteine protease; CDK, cyclin-dependent kinase; CDKN1A, cyclin-dependent kinase inhibitor 1A (p21); COXIV, cytochrome c oxidase subunit IV isoform 1; CRC, colorectal cancer; CTSB, cathepsin B; Cy3, cyanine 3; Cy5, cyanine 5; DRA, down-regulated in adenoma; EFHU1, translation elongation factor EF-1 -1 chain, GAPDH, glyceraldehyde-3-phosphate dehydrogenase; GSTA2, glutathione S-transferase, 2 (Yc2); HCA, heterocyclic aromatic amine; HIF1A, hypoxia inducible factor 1, subunit; HPGD, hydroxyprostaglandin dehydrogenase 15 (NAD); HSPD1, heat-shock 60-kDa protein 1 (chaperonin); IL18, interleukin 18 (interferon-gamma-inducing factor); MYBBP1a, MYB binding protein (P160) 1a; OAT, ornithine aminotransferase; ODC, ornithine decarboxylase; PMP22, peripheral myelin protein; RAD51, RAD51 homolog (Saccharomyces cerevisiae); RRM1, ribonucleotide reductase M1 polypeptide; SAT, spermidine/spermine N1-acetyl transferase 1; SCD2, stearoyl-coenzyme A desaturase 2; SEPP1, selenoprotein P, plasma 1; SLC26A3, solute carrier family 26, member 3; STAT1, signal transducer and activator of transcription 1; SULT1A1, sulfotransferase family 1A, phenol-preferring, member 1; TMSB10, thymosin ß 10; TNFRSF6, tumor necrosis factor receptor superfamily, member 6; TOP2A, topoisomerase (DNA) II 170 kDa.
Manuscript received 18 February 2005. Initial review completed 15 March 2005. Revision accepted 9 May 2005.
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LITERATURE CITED |
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1. World Health OrganizationInternational Agency for Research on Cancer (2003) World Cancer Report IARC Press Lyon, France.
2. Greenwald, P. (1992) Colon cancer overview. Cancer 70:1206-1215.[Medline]
3. Doll, R. & Peto, R. (1981) The causes of cancer: quantitative estimates of avoidable risks of cancer in the United States today. J. Natl. Cancer Inst. 66:1191-1308.
4. Steinmetz, K. A. & Potter, J. D. (1996) Vegetables, fruit, and cancer prevention: a review. J. Am. Diet Assoc. 96:1027-1039.[Medline]
5. World Cancer Research Fund, American Institute for Cancer Research (1997) Vegetables and fruits. Food, Nutrition and the Prevention of Cancer: A Global Perspective :436-446 BANTA Washington, DC.
6. World Cancer Research Fund, American Institute for Cancer Research (1997) Colon, rectum. Food, Nutrition and the Prevention of Cancer: A Global Perspective :216-251 BANTA Washington, DC.
7. Kim, D. J., Shin, D. H., Ahn, B., Kang, J. S., Nam, K. T., Park, C. B., Kim, C. K., Hong, J. T. & Kim, Y. B., et al (2003) Chemoprevention of colon cancer by Korean food plant components. Mutat. Res. 523–524:99-107.
8. Mahmoud, N. N., Carothers, A. M., Grunberger, D., Bilinski, R. T., Churchill, M. R., Martucci, C., Newmark, H. L. & Bertagnolli, M. M. (2000) Plant phenolics decrease intestinal tumors in an animal model of familial adenomatous polyposis. Carcinogenesis 21:921-927.
9. Kallay, E., Adlercreutz, H., Farhan, H., Lechner, D., Bajna, E., Gerdenitsch, W., Campbell, M. & Cross, H. S. (2002) Phytoestrogens regulate vitamin D metabolism in the mouse colon: relevance for colon tumor prevention and therapy. J. Nutr. 132:3490S-3493S.
10. Rijken, P. J., Timmer, W. G., van de Kooij, A. J., van Benschop, I. M., Wiseman, S. A., Meijers, M. & Tijburg, L. B. (1999) Effect of vegetable and carotenoid consumption on aberrant crypt multiplicity, a surrogate end-point marker for colorectal cancer in azoxymethane-induced rats. Carcinogenesis 20:2267-2272.
11. Rijnkels, J. M. & Alink, G. M. (1998) Effects of a vegetables-fruit mixture on liver and colonic 1,2-dimethylhydrazine-metabolizing enzyme activities in rats fed low- or high-fat diets. Cancer Lett. 128:171-175.[Medline]
12. van Kranen, H. J., van Iersel, P. W., Rijnkels, J. M., Beems, D. B., Alink, G. M. & van Kreijl, C. F. (1998) Effects of dietary fat and a vegetable-fruit mixture on the development of intestinal neoplasia in the ApcMin mouse. Carcinogenesis 19:1597-1601.
13. Wortelboer, H. M., de Kruif, C. A., van Iersel, A. A., Noordhoek, J., Blaauboer, B. J., van Bladeren, P. J. & Falke, H. E. (1992) Effects of cooked brussels sprouts on cytochrome P-450 profile and phase II enzymes in liver and small intestinal mucosa of the rat. Food Chem. Toxicol. 30:17-27.[Medline]
14. Sorensen, M., Jensen, B. R., Poulsen, H. E., Deng, X., Tygstrup, N., Dalhoff, K. & Loft, S. (2001) Effects of a brussels sprouts extract on oxidative DNA damage and metabolising enzymes in rat liver. Food Chem. Toxicol. 39:533-540.[Medline]
15. Clapper, M. L., Szarka, C. E., Pfeiffer, G. R., Graham, T. A., Balshem, A. M., Litwin, S., Goosenberg, E. B., Frucht, H. & Engstrom, P. F. (1997) Preclinical and clinical evaluation of broccoli supplements as inducers of glutathione S-transferase activity. Clin. Cancer Res. 3:25-30.[Abstract]
16. Hasseman, J., Chen, E., Yang, I. & Quackenbush, J. (2001) Preclinical and clinical evaluation of broccoli supplements as inducers of glutathione S-transferase activity. Aminoallyl labeling of RNA for microarrays. http://www.tigr.org/tdb/micoarray/protocolsTIGR.shtml [accessed June 21, 2001]..
17. van Breda, S. G., van Agen, E., Engels, L. G., Moonen, E. J., Kleinjans, J. C. & van Delft, J. H. (2004) Altered vegetable intake affects pivotal carcinogenesis pathways in colon mucosa from adenoma patients and controls. Carcinogenesis 25:2207-2216.
18. HomoloGene National Center for Biotechnology Information http://www.ncbi.nlm.nih.gov/HomoloGene/.
19. Kerr, M. K., Afshari, C. A., Bennett, L., Bushel, P., Martinez, J., Walker, N. J. & Churchill, G. A. (2002) Statistical analysis of a gene expression microarray experiment with replication. Statistica Sinica 12:203-217.
20. Kerr, M. K. (2003) Design considerations for efficient and effective microarray studies. Biometrics 59:822-828.[Medline]
21. Kerr, M. K., Martin, M. & Churchill, G. A. (2000) Analysis of variance for gene expression microarray data. J. Comput. Biol. 7:819-837.[Medline]
22. Wolfinger, R. D., Gibson, G., Wolfinger, E. D., Bennett, L., Hamadeh, H., Bushel, P., Afshari, C. & Paules, R. S. (2001) Assessing gene significance from cDNA microarray expression data via mixed models. J. Comput. Biol. 8:625-637.[Medline]
23. Neter, J., Kutner, M. H., Nachtsheim, C. J. & Wasserman, W. (1996) Assessing gene significance from cDNA microarray expression data via mixed models. Applied Linear Statistical Models 4th ed. McGraw/Irwin New York, NY.
24. Kerr, M. K. & Churchill, G. A. (2001) Statistical design and the analysis of gene expression microarray data. Genet. Res. 77:123-128.[Medline]
25. Gamage, N. U., Duggleby, R. G., Barnett, A. C., Tresillian, M., Latham, C. F., Liyou, N. E., McManus, M. E. & Martin, J. L. (2003) Structure of a human carcinogen-converting enzyme, SULT1A1. Structural and kinetic implications of substrate inhibition. J. Biol. Chem. 278:7655-7662.
26. Wong, C. F., Liyou, N., Leggett, B., Young, J., Johnson, A. & McManus, M. E. (2002) Association of the SULT1A1 R213H polymorphism with colorectal cancer. Clin. Exp. Pharmacol. Physiol. 29:754-758.[Medline]
27. Coles, B., Nowell, S. A., MacLeod, S. L., Sweeney, C., Lang, N. P. & Kadlubar, F. F. (2001) The role of human glutathione S-transferases (hGSTs) in the detoxification of the food-derived carcinogen metabolite N-acetoxy-PhIP, and the effect of a polymorphism in hGSTA1 on colorectal cancer risk. Mutat. Res. 482:3-10.[Medline]
28. Seitz, H. K., Matsuzaki, S., Yokoyama, A., Homann, N., Vakevainen, S. & Wang, X. D. (2001) Alcohol and cancer. Alcohol Clin. Exp. Res. 25:137S-143S.[Medline]
29. Cho, H. & Tai, H. H. (2002) Inhibition of NAD+-dependent 15-hydroxyprostaglandin dehydrogenase (15-PGDH) by cyclooxygenase inhibitors and chemopreventive agents. Prostaglandins Leukot. Essent. Fatty Acids 67:461-465.[Medline]
30. Cho, H. & Tai, H. H. (2002) Thiazolidinediones as a novel class of NAD(+)-dependent 15-hydroxyprostaglandin dehydrogenase inhibitors. Arch. Biochem. Biophys. 405:247-251.[Medline]
31. Stark, J. M., Hu, P., Pierce, A. J., Moynahan, M. E., Ellis, N. & Jasin, M. (2002) ATP hydrolysis by mammalian RAD51 has a key role during homology-directed DNA repair. J. Biol. Chem. 277:20185-20194.
32. Johnson, I. T. (2002) Anticarcinogenic effects of diet-related apoptosis in the colorectal mucosa. Food Chem. Toxicol. 40:1171-1178.[Medline]
33. Babbar, N. & Gerner, E. W. (2003) Polyamines as modifiers of genetic risk factors in human intestinal cancers. Biochem. Soc. Trans. 31:388-392.[Medline]
34. Wright, J. A., Chan, A. K., Choy, B. K., Hurta, R. A., McClarty, G. A. & Tagger, A. Y. (1990) Regulation and drug resistance mechanisms of mammalian ribonucleotide reductase, and the significance to DNA synthesis. Biochem. Cell Biol. 68:1364-1371.[Medline]
35. Gautam, A., Li, Z. R. & Bepler, G. (2003) RRM1-induced metastasis suppression through PTEN-regulated pathways. Oncogene 22:2135-2142.[Medline]
36. Antalis, T. M., Reeder, J. A., Gotley, D. C., Byeon, M. K., Walsh, M. D., Henderson, K. W., Papas, T. S. & Schweinfest, C. W. (1998) Down-regulation of the down-regulated in adenoma (DRA) gene correlates with colon tumor progression. Clin. Cancer Res. 4:1857-1863.[Abstract]
37. Keough, R., Woollatt, E., Crawford, J., Sutherland, G. R., Plummer, S., Casey, G. & Gonda, T. J. (1999) Molecular cloning and chromosomal mapping of the human homologue of MYB binding protein (P160) 1A (MYBBP1A) to 17p13.3. Genomics 62:483-489.[Medline]
38. Martinez, M. E., O’Brien, T. G., Fultz, K. E., Babbar, N., Yerushalmi, H., Qu, N., Guo, Y., Boorman, D. & Einspahr, J., et al (2003) Pronounced reduction in adenoma recurrence associated with aspirin use and a polymorphism in the ornithine decarboxylase gene. Proc. Natl. Acad. Sci. U.S.A. 100:7859-7864.
39. Wang, W., Liu, L. Q. & Higuchi, C. M. (1996) Mucosal polyamine measurements and colorectal cancer risk. J. Cell Biochem. 63:252-257.[Medline]
40. Han, X., Kazarinoff, M. N., Seiler, N. & Stanley, B. A. (2001) Rat colon ornithine and arginine metabolism: coordinated effects after proliferative stimuli. Am. J. Physiol. Gastrointest. Liver Physiol. 280:G389-399.
41. Garrido, C., Gurbuxani, S., Ravagnan, L. & Kroemer, G. (2001) Heat shock proteins: endogenous modulators of apoptotic cell death. Biochem. Biophys. Res. Commun. 286:433-442.[Medline]
42. el-Deiry, W. S., Tokino, T., Velculescu, V. E., Levy, D. B., Parsons, R., Trent, J. M., Lin, D., Mercer, W. E., Kinzler, K. W. & Vogelstein, B. (1993) WAF1, a potential mediator of p53 tumor suppression. Cell 75:817-825.[Medline]
43. Bunz, F., Dutriaux, A., Lengauer, C., Waldman, T., Zhou, S., Brown, J. P., Sedivy, J. M., Kinzler, K. W. & Vogelstein, B. (1998) Requirement for p53 and p21 to sustain G2 arrest after DNA damage. Science 282:1497-1501.
44. Liu, L. F. (1989) DNA topoisomerase poisons as antitumor drugs. Annu. Rev. Biochem. 58:351-375.[Medline]
45. Dinarello, C. A. (1999) Interleukin-18. Methods 19:121-132.[Medline]
46. Marino, E. & Cardier, J. E. (2003) Differential effect of IL-18 on endothelial cell apoptosis mediated by TNF-alpha and Fas (CD95). Cytokine 22:142-148.[Medline]
47. Coughlin, C. M., Salhany, K. E., Wysocka, M., Aruga, E., Kurzawa, H., Chang, A. E., Hunter, C. A., Fox, J. C., Trinchieri, G. & Lee, W. M. (1998) Interleukin-12 and interleukin-18 synergistically induce murine tumor regression which involves inhibition of angiogenesis. J. Clin. Invest. 101:1441-1452.[Medline]
48. Pages, F., Berger, A., Henglein, B., Piqueras, B., Danel, C., Zinzindohoue, F., Thiounn, N., Cugnenc, P. H. & Fridman, W. H. (1999) Modulation of interleukin-18 expression in human colon carcinoma: consequences for tumor immune surveillance. Int. J. Cancer 84:326-330.[Medline]
49. Fearon, E. R. & Vogelstein, B. (1990) A genetic model for colorectal tumorigenesis. Cell 61:759-767.[Medline]
50. Phillips, R. W., Kikendall, J. W., Luk, G. D., Willis, S. M., Murphy, J. R., Maydonovitch, C., Bowen, P. E., Stacewicz-Sapuntzakis, M. & Wong, R. K. (1993) beta-Carotene inhibits rectal mucosal ornithine decarboxylase activity in colon cancer patients. Cancer Res. 53:3723-3725.
51. Shertzer, H. G. & Senft, A. P. (2000) The micronutrient indole-3-carbinol: implications for disease and chemoprevention. Drug Metabol. Drug. Interact. 17:159-188.[Medline]
52. Schweinfest, C. W., Henderson, K. W., Suster, S., Kondoh, N. & Papas, T. S. (1993) Identification of a colon mucosa gene that is down-regulated in colon adenomas and adenocarcinomas. Proc. Natl. Acad. Sci. U.S.A. 90:4166-4170.
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