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Enregistrement W3090602287 · doi:10.1101/2020.10.05.20206268

Using genetic variants to evaluate the causal effect of cholesterol lowering on head and neck cancer risk: a Mendelian randomization study

2020· preprint· en· W3090602287 sur OpenAlexaff
Mark Gormley, James Yarmolinsky, Tom Dudding, Kimberley Burrows, Richard M. Martin, Steven J. Thomas, Jessica Tyrrell, Paul Brennan, Miranda Pring, Stefania Boccia, Andrew F. Olshan, Brenda Diergaarde, Geoffrey Liu, Danny Legge, Eloíza H. Tajara, Patrícia Severino, Martin Lacko, George Davey Smith, Emma E. Vincent, Rebecca C. Richmond

Notice bibliographique

RevuemedRxiv · 2020
Typepreprint
Langueen
DomaineBiochemistry, Genetics and Molecular Biology
ThématiqueCancer, Lipids, and Metabolism
Établissements canadiensPrincess Margaret Cancer CentreSinai Health SystemLunenfeld-Tanenbaum Research InstitutePublic Health OntarioUniversity of Toronto
Organismes subventionnairesNational Institute of Dental and Craniofacial ResearchMedical Research CouncilWorld Cancer Research FundFundação de Amparo à Pesquisa do Estado de São PauloUniversity of BristolDiabetes UKNational Institute for Health and Care ResearchWorld Cancer Research Fund InternationalNational Cancer InstituteCancer Research UKWellcome Trust
Mots-clésMendelian randomizationEzetimibeMedicineOncologyInternal medicineHead and neck squamous-cell carcinomaGenome-wide association studyStatinCholesterolBioinformaticsCancerHead and neck cancerBiologyGeneticsSingle-nucleotide polymorphismGenetic variants

Résumé

récupéré en direct d'OpenAlex

Abstract Introduction Head and neck squamous cell carcinoma (HNSCC), which includes cancers of the oral cavity and oropharynx, is a cause of substantial global morbidity and mortality. Strategies to reduce disease burden include discovery of novel therapies and repurposing of existing drugs. Statins are commonly prescribed for lowering circulating cholesterol by inhibiting HMG-CoA reductase (HMGCR). Results from some observational studies suggest that statin use may reduce HNSCC risk. We appraised the relationship of genetically-proxied cholesterol-lowering drug targets and other circulating lipid traits with oral (OC) and oropharyngeal (OPC) cancer risk. Methods and findings We conducted two-sample Mendelian randomization (MR). For the primary analysis, germline genetic variants in HMGCR, NPC1L1, CETP, PCSK9 and LDLR were used to proxy the effect of low-density lipoprotein cholesterol (LDL-C) lowering therapies. In secondary analyses, variants were used to proxy circulating levels of other lipid traits in a genome-wide association study (GWAS) meta-analysis of 188,578 individuals. Both primary and secondary analyses aimed to estimate the downstream causal effect of cholesterol lowering therapies on OC and OPC risk. The second sample for MR was taken from a GWAS of 6,034 OC and OPC cases and 6,585 controls (GAME-ON). Analyses were replicated in UK Biobank, using 839 OC and OPC cases and 372,016 controls and the results of the GAME-ON and UK Biobank analyses combined in a fixed-effects meta-analysis. We found limited evidence of a causal effect of genetically-proxied LDL-C lowering using HMGCR, NPC1L1, CETP or other circulating lipid traits on either OC or OPC risk. Genetically-proxied PCSK9 inhibition equivalent to a 1 mmol/L (38.7 mg/dL) reduction in LDL-C was associated with an increased risk of OC and OPC combined (OR 1.8 95%CI 1.2, 2.8, p= 9.31 ×10 −05 ), with good concordance between GAME-ON and UK Biobank ( I 2 = 22%). Effects for PCSK9 appeared stronger in relation to OPC (OR 2.6 95%CI 1.4, 4.9) than OC (OR 1.4 95%CI 0.8, 2.4). LDLR variants, resulting in genetically-proxied reduction in LDL-C equivalent to a 1 mmol/L (38.7 mg/dL), reduced the risk of OC and OPC combined (OR 0.7, 95%CI 0.5, 1.0, p= 0.006). A series of pleiotropy-robust and outlier detection methods showed that pleiotropy did not bias our findings. Conclusion We found limited evidence for a role of cholesterol-lowering in OC and OPC risk, suggesting previous observational results may have been confounded. There was some evidence that genetically-proxied inhibition of PCSK9 increased risk, while lipid-lowering variants in LDLR, reduced risk of combined OC and OPC. This result suggests that the mechanisms of action of PCSK9 on OC and OPC risk may be independent of its cholesterol lowering effects, but further replication of this finding is required. Author summary Why was this study done? To determine if genetically-proxied cholesterol-lowering drugs (such as statins which target HMGCR) reduce oral and oropharyngeal cancer risk. To determine if genetically-proxied circulating lipid traits (e.g. low-density lipoprotein cholesterol) have a causal effect on oral and oropharyngeal cancer risk. What did the researchers do and find? There was little evidence that genetically-proxied inhibition of HMGCR (target of statins), NPC1L1 (target of ezetimibe) and CETP (target of CETP inhibitors) influences oral or oropharyngeal cancer risk. There was little evidence of an effect of circulating lipid traits on oral or oropharyngeal cancer risk. There was some evidence that genetically-proxied inhibition of PCSK9 increases, while lipid-lowering variants in LDLR reduces oral and oropharyngeal cancer risk. What do these findings mean? These findings suggest that the results of previous observational studies examining the effect of statins on oral and oropharyngeal risk may have been confounded. Given we found little evidence of an effect of other cholesterol lowering therapies, the mechanism of action of PCSK9 may be independent of cholesterol-lowering. Further replication of this finding in other head and neck cancer datasets is required.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,034
score de la tête « metaresearch » (Gemma)0,059
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,034
Score d'incertitude au seuil0,178

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0340,059
Méta-épidémiologie (sens strict)0,0020,001
Méta-épidémiologie (sens large)0,0020,004
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,001
Communication savante0,0010,001
Science ouverte0,0020,001
Intégrité de la recherche0,0020,001
Charge utile insuffisante (le modèle a refusé de juger)0,0030,000

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.

Tête enseignante Opus0,027
Tête enseignante GPT0,328
Écart entre enseignants0,300 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations2
Publié2020
Routes d'admission1
Résumé présentoui

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