MétaCan
Menu
← Back to cohort
Record W2561481549 · doi:10.1158/1538-7445.am2015-4569

Abstract 4569: A simple method to screen patients for SNPs in NAT1 gene for prostate cancer risk

2015· article· en· W2561481549 on OpenAlexaff
James Gomes, Melody Emaimem, Maitland Long

Bibliographic record

VenueCancer Research · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer, Hypoxia, and Metabolism
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsGenotypingSingle-nucleotide polymorphismSNP genotypingSNPComputational biologyMolecular Inversion ProbePrimer (cosmetics)GeneticsGenedbSNPBiologyGenotypeMolecular biologyChemistry

Abstract

fetched live from OpenAlex

Abstract Single nucleotide polymorphisms (SNPs) are single base differences in DNA and are suitable for genotyping markers of risk in human disease. With emerging bio-technology and a cross-platform application of techniques improvements can be made in methodology. Although a number of high-throughput SNP genotyping systems are available the technology is expensive for it requires both trained personnel and dedicated platforms. The method reported here is a simple qPCR based High Resolution Method (HRM) based method and can be cost effectively used in screening patients in a general molecular biology and clinical laboratories. The NAT1 gene directs N-acetylation and O-acetylation of toxins including heterocyclic amines through the phase II xenobiotic metabolizing enzymes toxicity pathway. The purpose was to develop a screening tool to screen individuals with SNPs at locations 190, 445, 560 and 640. A modified qPCR approach was used with HRM analyses. Primers were designed using an in-house protocol; the rs sequence for the site-specific mutation was obtained from SNP database and fed to Primer Quest and having diligently identified the location of the mutation primers were designed and assessed using OligoAnalyzer Tool. The Tm for the forward and reverse were kept as close as possible and G was kept between -1 and -9 and the GC content greater than 50%. The following sequences were used rs58379106 (190C>T); rs4987076 (445G>A); rs4986782 (560G>A) and rs4986783 (640T>G). A RT qPCR 10μl reaction was designed using iTAQ Supermix (BioRad, CA), forward and reverse primers and genomic DNA obtained from prostate cancer cases and controls. Standard cycling conditions described for iTAQ were observed for 44 cycles. The data collected was analysed using HRM software (BioRad, CA) as per the guidelines described by the software. The data shows that when all the conditions are normalized and benchmarked the shape of the HRM curves are typical and characteristic of the SNP. The figures show the symmetry and similarity in the HRM. The similarity from the standard curve (Fig.1) and standard curve with unknown SNP (Fig. 2) and curves with a complementary SNP at 445 along with SNP at 640 (Fig. 3). Fig. 1 Std. curve with NAT1640 Fig. 2 Std. curve with unknown samples Fig. 3 Unknown samples with 640 and 445 This data shows that with this approach it is possible to identify site specific mutation and the presence of other mutation in the vicinity of the SNP of interest. The presence or absence of mutations among cases and controls can be assessed using this approach. Note: This abstract was not presented at the meeting. Citation Format: James Gomes, Melody Emaimem, Maja Zuric, Maitland Long. A simple method to screen patients for SNPs in NAT1 gene for prostate cancer risk. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 4569. doi:10.1158/1538-7445.AM2015-4569

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.026
Threshold uncertainty score0.087

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0260.015

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 head0.085
GPT teacher head0.452
Teacher spread0.367 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueCancer Research→Same topicCancer, Hypoxia, and Metabolism→French-language works237,207→