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1.  Hepatocellular Carcinoma Risk Factors and Disease Burden in a European Cohort: A Nested Case–Control Study 
Background
To date, no attempt has been made to systematically determine the apportionment of the hepatocellular carcinoma burden in Europe or North America among established risk factors.
Methods
Using data collected from 1992 to 2006, which included 4 409 809 person-years in the European Prospective Investigation into Cancer and nutrition (EPIC), we identified 125 case patients with hepatocellular carcinoma, of whom 115 were matched to 229 control subjects. We calculated odds ratios (ORs) for the association of documented risk factors for hepatocellular carcinoma with incidence of this disease and estimated their importance in this European cohort.
Results
Chronic hepatitis B virus (HBV) or hepatitis C virus (HCV) infection (OR = 9.10, 95% confidence interval [CI] = 2.10 to 39.50 and OR = 13.36, 95% CI = 4.11 to 43.45, respectively), obesity (OR = 2.13, 95% CI = 1.06 to 4.29), former or current smoking (OR = 1.98, 95% CI = 0.90 to 4.39 and OR = 4.55, 95% CI = 1.90 to 10.91, respectively), and heavy alcohol intake (OR = 1.77, 95% CI = 0.73 to 4.27) were associated with hepatocellular carcinoma. Smoking contributed to almost half of all hepatocellular carcinomas (47.6%), whereas 13.2% and 20.9% were attributable to chronic HBV and HCV infection, respectively. Obesity and heavy alcohol intake contributed 16.1% and 10.2%, respectively. Almost two-thirds (65.7%, 95% CI = 50.6% to 79.3%) of hepatocellular carcinomas can be accounted for by exposure to at least one of these documented risk factors.
Conclusions
Smoking contributed to more hepatocellular carcinomas in this Europe-wide cohort than chronic HBV and HCV infections. Heavy alcohol consumption and obesity also contributed to sizeable fractions of this disease burden. These contributions may be underestimates because EPIC volunteers are likely to be more health conscious than the general population.
doi:10.1093/jnci/djr395
PMCID: PMC3216968  PMID: 22021666
2.  Genetic Variability of the mTOR Pathway and Prostate Cancer Risk in the European Prospective Investigation on Cancer (EPIC) 
PLoS ONE  2011;6(2):e16914.
The mTOR (mammalian target of rapamycin) signal transduction pathway integrates various signals, regulating ribosome biogenesis and protein synthesis as a function of available energy and amino acids, and assuring an appropriate coupling of cellular proliferation with increases in cell size. In addition, recent evidence has pointed to an interplay between the mTOR and p53 pathways. We investigated the genetic variability of 67 key genes in the mTOR pathway and in genes of the p53 pathway which interact with mTOR. We tested the association of 1,084 tagging SNPs with prostate cancer risk in a study of 815 prostate cancer cases and 1,266 controls nested within the European Prospective Investigation into Cancer and Nutrition (EPIC). We chose the SNPs (n = 11) with the strongest association with risk (p<0.01) and sought to replicate their association in an additional series of 838 prostate cancer cases and 943 controls from EPIC. In the joint analysis of first and second phase two SNPs of the PRKCI gene showed an association with risk of prostate cancer (ORallele = 0.85, 95% CI 0.78–0.94, p = 1.3×10−3 for rs546950 and ORallele = 0.84, 95% CI 0.76–0.93, p = 5.6×10−4 for rs4955720). We confirmed this in a meta-analysis using as replication set the data from the second phase of our study jointly with the first phase of the Cancer Genetic Markers of Susceptibility (CGEMS) project. In conclusion, we found an association with prostate cancer risk for two SNPs belonging to PRKCI, a gene which is frequently overexpressed in various neoplasms, including prostate cancer.
doi:10.1371/journal.pone.0016914
PMCID: PMC3044148  PMID: 21373201
3.  The Micronutrient Genomics Project: a community-driven knowledge base for micronutrient research 
Genes & Nutrition  2010;5(4):285-296.
Micronutrients influence multiple metabolic pathways including oxidative and inflammatory processes. Optimum micronutrient supply is important for the maintenance of homeostasis in metabolism and, ultimately, for maintaining good health. With advances in systems biology and genomics technologies, it is becoming feasible to assess the activity of single and multiple micronutrients in their complete biological context. Existing research collects fragments of information, which are not stored systematically and are thus not optimally disseminated. The Micronutrient Genomics Project (MGP) was established as a community-driven project to facilitate the development of systematic capture, storage, management, analyses, and dissemination of data and knowledge generated by biological studies focused on micronutrient–genome interactions. Specifically, the MGP creates a public portal and open-source bioinformatics toolbox for all “omics” information and evaluation of micronutrient and health studies. The core of the project focuses on access to, and visualization of, genetic/genomic, transcriptomic, proteomic and metabolomic information related to micronutrients. For each micronutrient, an expert group is or will be established combining the various relevant areas (including genetics, nutrition, biochemistry, and epidemiology). Each expert group will (1) collect all available knowledge, (2) collaborate with bioinformatics teams towards constructing the pathways and biological networks, and (3) publish their findings on a regular basis. The project is coordinated in a transparent manner, regular meetings are organized and dissemination is arranged through tools, a toolbox web portal, a communications website and dedicated publications.
doi:10.1007/s12263-010-0192-8
PMCID: PMC2989004  PMID: 21189865
Micronutrient; Bioinformatics; Database; Genomics
4.  The Micronutrient Genomics Project: a community-driven knowledge base for micronutrient research 
Genes & Nutrition  2010;5(4):285-296.
Micronutrients influence multiple metabolic pathways including oxidative and inflammatory processes. Optimum micronutrient supply is important for the maintenance of homeostasis in metabolism and, ultimately, for maintaining good health. With advances in systems biology and genomics technologies, it is becoming feasible to assess the activity of single and multiple micronutrients in their complete biological context. Existing research collects fragments of information, which are not stored systematically and are thus not optimally disseminated. The Micronutrient Genomics Project (MGP) was established as a community-driven project to facilitate the development of systematic capture, storage, management, analyses, and dissemination of data and knowledge generated by biological studies focused on micronutrient–genome interactions. Specifically, the MGP creates a public portal and open-source bioinformatics toolbox for all “omics” information and evaluation of micronutrient and health studies. The core of the project focuses on access to, and visualization of, genetic/genomic, transcriptomic, proteomic and metabolomic information related to micronutrients. For each micronutrient, an expert group is or will be established combining the various relevant areas (including genetics, nutrition, biochemistry, and epidemiology). Each expert group will (1) collect all available knowledge, (2) collaborate with bioinformatics teams towards constructing the pathways and biological networks, and (3) publish their findings on a regular basis. The project is coordinated in a transparent manner, regular meetings are organized and dissemination is arranged through tools, a toolbox web portal, a communications website and dedicated publications.
doi:10.1007/s12263-010-0192-8
PMCID: PMC2989004  PMID: 21189865
Micronutrient; Bioinformatics; Database; Genomics
5.  Comparative gene expression profiling in two congenic mouse strains following Bordetella pertussis infection 
BMC Microbiology  2007;7:88.
Background
Susceptibility to Bordetella pertussis infection varies widely. These differences can partly be explained by genetic host factors. HcB-28 mice are more resistant to B. pertussis infection than C3H mice, which could partially be ascribed to the B. pertussis susceptibility locus-1 (Bps1) on chromosome 12. The presence of C57BL/10 genome on this locus instead of C3H genome resulted in a decreased number of bacteria in the lung. To further elucidate the role of host genetic factors, in particular in the Bps1 locus, in B. pertussis infection, and to identify candidate genes within in this region, we compared expression profiles in the lungs of the C3H and HcB-28 mouse strains following B. pertussis inoculation. Twelve and a half percent of the genomes of these mice are from a different genetic background.
Results
Upon B. pertussis inoculation 2,353 genes were differentially expressed in the lungs of both mouse strains. Two hundred and six genes were differentially expressed between the two mouse strains, but, remarkably, none of these were up- or down-regulated upon B. pertussis infection. Of these 206 genes, 17 were located in the Bps1 region. Eight of these genes, which showed a strong difference in gene expression between the two mouse strains, map to the immunoglobulin heavy chain complex (Igh).
Conclusion
Gene expression changes upon B. pertussis infection are highly identical between the two mouse strains despite the differences in the course of B. pertussis infection. Because the genes that were differentially regulated between the mouse strains only showed differences in expression before infection, it appears likely that such intrinsic differences in gene regulation are involved in determining differences in susceptibility to B. pertussis infection. Alternatively, such genetic differences in susceptibility may be explained by genes that are not differentially regulated between these two mouse strains. Genes in the Igh complex, among which Igh-1a/b, are likely candidates to explain differences in susceptibility to B. pertussis. Thus, by microarray analysis we significantly reduced the number of candidate susceptibility genes within the Bps1 locus. Further work should establish the role of the Igh complex in B. pertussis infection.
doi:10.1186/1471-2180-7-88
PMCID: PMC2174938  PMID: 17935610

Results 1-5 (5)