Abstract 4569: A simple method to screen patients for SNPs in NAT1 gene for prostate cancer risk
Notice bibliographique
Résumé
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
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,026 | 0,015 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».