Tumor-resident microorganisms as clinical biomarkers in primary liver cancer: A systematic review of current evidence
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Notice bibliographique
Résumé
BACKGROUND Hepatic malignancies represent the sixth most prevalent cancer globally, with emerging evidence revealing that intratumoral microbes actively modulate carcinogenesis through immunomodulation and metabolic reprogramming. Recent high-throughput sequencing technologies have identified taxonomically diverse microbial communities within tumor tissues, challenging traditional sterility paradigms. Germ-free mouse models have established causal relationships between gut microbiota and hepatocarcinogenesis. However, comprehensive evaluation of intratumoral microbiota as clinical biomarkers remains limited, necessitating systematic analysis of their diagnostic, prognostic, and therapeutic applications in hepatic malignancies. AIM To systematically analyze intratumoral microbes as biomarkers for hepatic malignancies diagnosis, prognosis, and treatment response. METHODS We conducted a systematic literature search in PubMed from inception to July 2025 using keywords combining hepatic malignancies, intratumoral microbiota, and biomarkers. Inclusion criteria encompassed human studies examining intratumoral microbial communities with biomarker applications. Exclusion criteria included animal-only studies, reviews, and research focusing solely on gut microbiota. Data extraction focused on diagnostic accuracy, prognostic significance, therapeutic predictions, and underlying mechanisms. Study quality was assessed using Newcastle-Ottawa Scale, with scores ≥ 7 indicating high quality. RESULTS Twenty studies (sample sizes: 18-925 patients) examining hepatocellular carcinoma (80%) and intrahepatic cholangiocarcinoma (20%) were included. All studies achieved Newcastle-Ottawa Scale scores ≥ 6, with 60% scoring the maximum 9 points, indicating moderate-to-high quality. Studies predominantly employed 16S rRNA sequencing (100%) targeting V3-V4 regions, with complementary validation techniques including fluorescence in situ hybridization, quantitative PCR, and immunohistochemistry. Specific bacterial taxa demonstrated exceptional diagnostic accuracy [area under the curve (AUC) > 0.9] for tumor discrimination. Notably, Bacilli showed AUC = 0.943 in validation cohorts. Microbial diversity and specific genera (Methylobacterium , Akkermansia , Intestinimonas ) showed consistent prognostic associations with survival outcomes, though relationships varied across cancer subtypes. Advanced risk stratification models incorporating multiple bacterial biomarkers showed independent predictive capacity through multivariable Cox regression. Mechanistic investigations revealed microbe-mediated oncogenic pathway activation, particularly NF-κB signaling, immune modulation through M2 macrophage polarization, and drug resistance mechanisms via autophagy regulation. Germ-free mouse models established causal relationships, demonstrating that specific bacterial communities, particularly Klebsiella pneumoniae , can autonomously initiate hepatocarcinogenesis through TLR4-dependent pathways. CONCLUSION Intratumoral microbes represent promising clinical biomarkers for hepatic malignancies across diagnostic, prognostic, and therapeutic applications. While standardization and multicenter validation remain essential prerequisites, mechanistic evidence from human and experimental studies positions microbiome-based biomarkers at the threshold of clinical translation.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 découle