The Correlation Between <i>Fusobacterium nucleatum</i> and Colorectal Cancer: A Comprehensive Review
Bibliographic record
Abstract
Colorectal cancer (CRC) continues to be a significant health concern worldwide, and recent research has highlighted an intriguing association with Fusobacterium nucleatum. The prevalence of F. nucleatum in CRC tissues varies significantly across studies, with estimates ranging from 13% to 80%, complicating efforts to define the bacterium’s precise role in CRC development. Although the involvement of F. nucleatum in the initiation of CRC is still debated, there is a broad consensus regarding its role in cancer progression and metastasis. Elucidating the molecular mechanisms of F. nucleatum-mediated carcinogenesis could provide new avenues for managing CRC. F. nucleatum significantly facilitates the growth of CRC via its FadA adhesion. It attaches to E-cadherin (CDH1) and activates Wnt/β-catenin signaling. Consequently, inflammatory genes, Wnt-related genes, and oncogenes, such as c-Myc and Cyclin D1 (CCND1), are overexpressed. Various preventive and therapeutic strategies against F. nucleatum in CRC have been investigated, including the Fn-AhpC recombinant protein vaccine in mice and the use of metronidazole to reduce intratumoral bacterial load. Additionally, bacteriophages and nanoparticles are emerging as potential therapeutic tools. This review aims to provide a comprehensive overview of the role of F. nucleatum in CRC and to explore its potential as a target for novel therapeutic strategies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".