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Record W2318293625 · doi:10.1158/1538-7445.am10-915

Abstract 915: Xenobiotic metabolizing gene variants, occupational chemical exposures, and renal cell cancer

2010· article· en· W2318293625 on OpenAlexaff
Julia E. Heck, Yuan-Chin A. Lee, James McKay, Sara Karami, Valérie Gaborieau, Neonila Szeszenia‐Dąbrowska, Давид Заридзе, Jolanta Lissowska, Péter Rudnai, Dana Mateș, Lenka Foretová, Vladimí­r Janout, Vladimír Bencko, Wong‐Ho Chow, Nathaniel Rothman, Lee E. Moore, Paul Brennan, Paolo Boffetta

Bibliographic record

VenueCancer Research · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGlutathione Transferases and Polymorphisms
Canadian institutionsLunenfeld-Tanenbaum Research InstituteMount Sinai Hospital
Fundersnot available
KeywordsMicrosomal epoxide hydrolaseCancerOdds ratioGenotypeMedicineKidney cancerBiologyInternal medicineOncologyGeneticsGeneMicrosomeEpoxide hydrolase

Abstract

fetched live from OpenAlex

Abstract The countries of Central and Eastern Europe have among the highest worldwide rates of renal cell cancer. Few studies have examined whether genetic variation in xenobiotic metabolic pathway genes may modify risk for this cancer. The Central and Eastern Europe Renal Cell Cancer study was a hospital-based case-control study conducted between 1998 and 2003 across seven centers in central and eastern Europe. The study centers were in the Czech Republic (Ceske, Prague, Olomouc, Brno), Poland (Lodz), Romania (Bucharest), and Russia (Moscow). Incident cases of primary kidney cancer were recruited and underwent an in-person interview in which detailed data were collected on demographics as well as work history and occupational exposure to chemical agents. Genes (cytochrome P-450 family, N-acetyltransferases, NAD(P)H:quinone oxidoreductase I (NQO1), microsomal epoxide hydrolase (mEH), catechol-O-methyltransferase (COMT)) were selected for the present analysis based on their putative role in xenobiotic metabolism. Genotyping (874 cases and 2396 controls) was conducted with a GoldenGate® Oligo Pool All (OPA) assay by Illumina® and the 5’ nuclease assay (Taqman, Applied Biosystems). Haplotypes were calculated using fastPhase. Odds ratios (OR) and 95% confidence intervals (CI) were estimated by unconditional logistic regression adjusted for country of residence, age, and sex. We observed an increased risk of renal cell cancer (RCC) with two NAT1 SNPs (NAT1A40T, OR=1.36, CI 1.00, 1.86; NAT1R187Q, OR=1.65, CI 1.01, 2.71) and with CYP1B1V432L (OR=1.14, CI 1.01, 1.28). The slow (NAT1*14) phenotype was associated with increased risk of RCC (OR=1.69, CI 1.00-2.86) in comparison to normal phenotype (NAT1*4, *3, *11). Among persons with NAT1*14 phenotype, occupational exposure to trichloroethylene (OR=9.94, CI 1.15, 86.1) and chlorinated solvents (OR=9.79, CI 1.13, 84.8) conferred an increased risk of RCC. Among those with the CYP1B1V432L variant, occupational exposure to trichloroethylene (OR=1.68, CI 1.21, 2.34) and chlorinated solvents (OR=1.58, CI 1.14, 2.19) also had increased risk of RCC. These results require replication, but provide evidence that the relationship between certain agents and RCC may be modified by particular variants in xenobiotic metabolism genes. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 915.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.683

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.037
GPT teacher head0.350
Teacher spread0.313 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2010
Admission routes1
Has abstractyes

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