Cutaneous human papillomaviruses in head and neck cancers: risk factors or innocent bystanders
Notice bibliographique
Résumé
In high-income countries, human papillomavirus (HPV) is a major cause of head and neck cancers (HNC). While high-risk types from the α-genus like HPV16 have been studied extensively in the HNC literature, the role of other genera (β and γ), also called cutaneous HPV, is still poorly understood. However, recent studies have shown that β- and γ-HPV could be related to cancers in the skin, esophagus, and head and neck. There are few studies investigating their role in HNC, and none in the Canadian population. This dissertation research aims to address limitations in previous work and advance the research on the relation between cutaneous HPV and HNC. The data for this project come from the Head and Neck Cancer (HeNCe) Life study. HeNCe investigators recruited incident HNC cases (460) and controls (458), frequency-matched by age and sex, from four main referral hospitals in Montreal, Canada. HeNCe collected information on sociodemographic and behavior characteristics using in-person interviews, and tested rinse and brush specimens for HPV genotyping. Tumor samples were retrieved from hospital archives for a subsample of cases (n=121) to investigate HPV in tumor tissues. Samples were tested for all three genera of HPV using several molecular techniques. First, we describe the prevalence of HPV genera and genotypes in oral and tumor samples and examine the distribution according to age, sex, sexual behavior, smoking, alcohol consumption, and oral health indicators. Similar to the α-genus, γ-HPV distribution varied by smoking and sexual behavior. However, β-HPV did not show a difference in distribution by any of the typical cancer risk factors except for age. Second, we estimated confounding-adjusted odds ratios (aOR) and 95% confidence intervals (CI) for the effect of HPV on HNC using logistic regression. α-HPV genus had a strong effect on HNC, particularly HPV16 (aOR=22.6; 95% CI: 10.8, 47.2). We found weaker evidence for γ-HPV (aOR= 1.29; 95% CI: 0.80, 2.08) and β-HPV was more common among controls than cases (aOR=0.80; 95% 0.57, 1.11). We conducted a quantitative bias analysis for the relation between HPV16 and HNC and found the effect would be underestimated when not accounting for the three epidemiologic biases: unmeasured confounding, selection bias, and measurement error. Multiple bias analyses for HPV16 increased the strength of the point estimate but also increased uncertainty (aOR=54.2, 95%CI 10.7, 385.9).Finally, we estimated the interaction between HPV genera in HNC, particularly the interaction between HPV16 and infection with any β- or γ-HPV. Infection with HPV16 alone had a strong effect on HNC. The effect of coinfection between HPV16 and any cutaneous HPV was stronger than the effect of either one alone, but we did not find strong evidence for an additive interaction as the study was underpowered. However, the point estimate for interaction between HPV16 and any cutaneous HPV infection was positive with relative excess risk due to interaction (RERI) = 2.44 (95% CI -23.27, 28.15). Likewise, we did not find strong evidence for the interaction between HPV16 and β-HPV or γ-HPV, but the point estimate was in a negative direction with any β-HPV and a positive direction for any γ-HPV infection. Because of the limited sample size, results were imprecise and definite conclusions cannot be made
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 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 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 ».