Intratumoral microbiota composition in women’s cancers: a systematic review and meta-analysis
Notice bibliographique
Résumé
Background The intratumoral microbiota has attracted considerable interest in carcinogenesis, progression, and treatment owing to advancements in sequencing technology. This systematic review provides a comprehensive overview of the current literature regarding the diversity and compositional characteristics of the intratumoral microbiota in women’s cancers. Additionally, it also explores potential associations among intratumoral microbiota, estrogen, and anti-tumor therapies. Methods A comprehensive literature search was conducted using PubMed, Embase, Web of Science, and the Cochrane Library from their inception to May 1, 2024. The review protocol was pre-registered in PROSPERO (CRD 42024601213). Articles were assessed utilizing the Newcastle-Ottawa Scale (NOS). To estimate the effect size and variability in microbial diversity changes, the standardized mean difference (SMD) and 95% confidence intervals (CIs) were employed. The systematic review adhered to PRISMA reporting guidelines, and meta-analyses were performed using Review Manager version 5.4. Results This systematic review included 29 of 8,291 studies after a thorough screening process. Of the 22 studies investigating α-diversity in women’s cancers, disease-free controls, and those with benign conditions, notable changes in diversity indices were observed. Compared to adjacent normal tissues, the Simpson index significantly decreased in breast cancer (SMD = -0.75, 95% CI: [-0.94, -0.55]) and endometrial cancer (SMD = -0.83, 95% CI: [-1.37, -0.28]). The Chao1 index was reduced in endometrial cancer tumor tissues relative to normal tissues (SMD = -2.25, 95% CI: [-3.13, -1.36]), while the Shannon index decreased in ovarian cancer tumor tissues (SMD = -0.61, 95% CI: [-1.18, -0.04]). In comparisons between tumor and benign tissues, the Chao1 index was decreased (SMD = -0.64, 95% CI: [-1.20, -0.08], I² = 0%), while the Simpson index was increased (SMD = 0.36, 95% CI: [0.01, 0.71], I² = 0%) in patients with ovarian cancer. Other microbial diversity indices showed no significant differences between tumor and non-tumor tissues. At the phylum level, Fusobacteriota were enriched in tumor tissues, while Firmicutes and Actinobacteria predominated in non-tumor tissues. At the genus level, Pseudomonas, Porphyromonas, Atopobium, Peptoniphilus, and Acinetobacter were consistently more abundant in cancerous tissues. Microbial alterations were also linked to estrogen receptor (ER) status, with Alkanindiges negatively correlated with ER status in two studies. Furthermore, one study on the effect of antineoplastic therapy indicated that neoadjuvant chemotherapy reduced microbial diversity in breast cancer patients (n = 15 vs. n = 18) (Shannon index: SMD = -0.95, 95% CI: [-1.68, -0.22]). Conclusion This study highlights significant differences in microbiota composition between tumor and non-tumor tissues in women’s cancers, emphasizing changes in intratumoral microbiota influenced by estrogen and antineoplastic treatments. Further research is needed to explore the potential for developing targeted therapies based on estrogen-driven microbiota alterations. Investigations may yield insights into the enhancement of female reproductive health and the improvement of treatment efficacy for female cancers. Systematic review registration https://www.crd.york.ac.uk/PROSPERO/view/CRD42024601213 , identifier CRD 42024601213.
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 | 0,012 | 0,033 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,001 |
| Méta-épidémiologie (sens large) | 0,016 | 0,032 |
| Bibliométrie | 0,008 | 0,008 |
| 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 | 0,002 |
| 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 ».