Fusobacterium nucleatum and Bacteroides fragilis detection in colorectal tumours: Optimal target site and correlation with total bacterial load
Bibliographic record
Abstract
BACKGROUND: Mucosal infiltration by certain bacterial species may contribute to the development and progression of colorectal cancer (CRC). There is considerable variation in reported detection rates in human CRC samples and the extent to which bacterial infiltration varies across regions of the primary tumour is unknown. This study aimed to determine if there is an optimal site for bacterial detection within CRC tumours. METHODS: Presence of target bacterial species was assessed by quantitative real-time PCR (qPCR) in 42 human CRC tumours. Abundance in primary tumour regions, normal epithelium and at metastatic sites was investigated in an expanded cohort of 51 patients. Species presence/absence was confirmed by diversity profiling in five patients. Correlation with total bacterial load and clinicopathological features was assessed. RESULTS: Fusobacterium nucleatum and Bacteroides fragilis were detected in tumours from 43% and 24% of patients, respectively (17% positive for both species). The optimal detection site was the tumour luminal surface (TLS). Patients testing positive at the TLS frequently tested negative at other sites, including central tumour and invasive margin. F. nucleatum was detected at a higher frequency in tumour versus normal epithelium (p < 0.01) and was associated with more advanced disease (p = 0.01). Detection of both species correlated with total bacterial load. However, corroboration of qPCR results via diversity profiling suggests detection of these species may indicate a specific microbial signature. CONCLUSIONS: This study supports a role for F. nucleatum in CRC development. Presence of F. nucleatum and B. fragilis varies across primary tumour regions, with the TLS representing the optimal site for bacterial detection.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".