Abstract 938: Inflammation and immune response pathway analysis of lung cancer using hierarchal modeling based on GWAS data
Notice bibliographique
Résumé
Abstract Background. Inflammation was suggested to play a pivotal role in the lung carcinogenesis through increased genetic mutations, anti-apoptotic signaling and increased angiogenesis. In this study, we investigate the association between genetic variants in inflammation and immune response pathway and lung cancer risk based on the data from the genomewide scans. The conventional approach of GWAS analysis includes ranking all variants by the p-values or bayes factor, which does not take into account the growing information available for the variants. In this analysis, we incorporate the prior information about the variants in inflammation and immune response pathway into the hierarchical modeling framework. Methods. GWAS data from 6 studies (Toronto, Central Europe(CE), MDACC, INSERM, CARET and Germany) were used to extract information on the inflammation pathway, with a total of 4379 cases and 5093 controls. All samples were genotyped by Illumina 317K chip. A thorough keyword search was conducted using Genecards, Gene ontology, and KEGG pathway databases and a total of 725 genes were identified to be associated with inflammation and/or immune response pathways. Data of 8033 variants were available from these 725 genes in the GWAS database. We designed the prior matrix based on the gene function (immune function, ROS, etc), and the variant significance (conservation, nonsynonmous, etc). We also analyzed the association between the top hits and the level of N-tyrosine, an indicator of inflammation severity, based on a subset of 126 individuals from CE study. Results. Two top hits were identified in the inflammation and immune response pathway: located in Ch5p15 in telomerase reverse transcriptase (TERT), and 6p21 in major histocompatibility complex class I, B (HLA-B) with p-value of 9.4×10−9 and 4.11×10−8 in the first stage, respectively. The OR of rs2736100 in TERT based on hierarchical modeling was 1.08 (1.05-1.11), and OR of rs2523554 in HLA-B was 1.07 (1.04-1.10). There as no meaningful change in the ranking of majority of the top 20 markers based on the hierarchical estimates versus conventional estimates. However, rs6985243 in FABP4 with p-value of 6× 10−5 was no longer significant based on the hierarchal estimate due to low prior and wide confidence interval. We did not see an association between any top hits and the level of N-tyrosine. None of the second stage covariates, such as cell signaling or immune function, appear to have a stronger association with lung cancer risk than the others. Discussion. Hierarchical modeling allows the prior knowledge such as bioinformatics to be incorporated into the analysis simultaneously. It provides an alternative analytical approach to the conventional GWAS analysis. Our results confirmed the association between TERT and lung cancer risk. The HLA-B loci is not in LD with the previous loci identified in chromosome 6p, thus further replication is being planned. 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 938.
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 | 0,005 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,004 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
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 ».