Tumor-resident microorganisms as clinical biomarkers in primary liver cancer: A systematic review of current evidence
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.017 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".