The Human Microbiome in Relation to Cancer Risk: A Systematic Review of the Literature
Notice bibliographique
Résumé
Background: Dysbiosis in the human microbiome may play a role in a number of chronic diseases, such as obesity, diabetes and cancer. The potential link between the microbiome and cancer development might offer new opportunities for cancer prevention by understanding etiologic pathways as well as for screening, diagnosis and maybe even treatment. The objective of this systematic review was to evaluate strength of the current data on the relation between the human microbiome and cancer in human studies. Methods: Relevant published articles were identified up until 2017 in PubMed, Embase and the Cochrane Library. All on-topic case-control studies, cohort studies and RCT's written in English were screened by two independent investigators. Studies in children, in vitro and animal studies were excluded. Quality was assessed with the Newcastle-Ottawa scale. Results: Fifty-eight studies were case-control studies, including one nested case-control study, and one was a cohort study. The gut microbiome was the microbiome most frequently studied, followed by the oral microbiome and then some studies on the bile duct, cervical and intrauterine, esophagus and gastric, laryngeal, lung, skin and urinary microbiome. All articles, besides one article from 1983, showed specific differences in microbiome distribution between cases and controls either based on presence or abundance of certain microbiota. Some findings were consistent between studies but many differences in results were noticed since various known and unknown factors influence the microbiome (eg ethnicity, smoking, diet, cancer stage & type). Twenty-seven studies linked differences in the gut microbiome with colorectal cancer. The most consistent findings were for Fusobacterium (most important species Fusobacterium nucleatum), Porphyromonas and Peptostreptococcus being significantly enriched in fecal and mucosal samples from colorectal cancer patients. Two studies on the cervical microbiome found a decrease in abundance of the beneficial Lactobacillus crispatus in cervical cancer cases. Concerning the oral microbiome, five articles observed a changed abundance of Streptococcus in oral cancer patients versus controls, four reported altered abundance of Prevotella and two an increase in Rothia. A decrease of Neisseria in the oral microbiome was found for pancreatic cancer in two articles and for oral cancer in one study. Three studies found a decrease in Neisseria and Haemophilus when investigating the tongue coating samples of patients with colorectal, lung and gastric cancer. Conclusion: This review provides important observations in potential changes in the gut micro-organisms in relation to (particularly colorectal) cancer. However, for most of the microbiome and cancers the evidence was still too weak to draw firm conclusions. Future prospective studies with prediagnostic specimen collection and with newer techniques and a more uniform sample collection are required to establish causal links.
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 | 0,007 | 0,033 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,008 | 0,008 |
| Bibliométrie | 0,016 | 0,018 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,002 | 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 ».