A Review of Gut Microbiota‐Derived Metabolites in Tumor Progression and Cancer Therapy
Bibliographic record
Abstract
Gut microbiota-derived metabolites are key hubs connecting the gut microbiome and cancer progression, primarily by remodeling the tumor microenvironment and regulating key signaling pathways in cancer cells and multiple immune cells. The use of microbial metabolites in radiotherapy and chemotherapy mitigates the severe side effects from treatment and improves the efficacy of treatment. Immunotherapy combined with microbial metabolites effectively activates the immune system to kill tumors and overcomes drug resistance. Consequently, various novel strategies have been developed to modulate microbial metabolites. Manipulation of genes involved in microbial metabolism using synthetic biology approaches directly affects levels of microbial metabolites, while fecal microbial transplantation and phage strategies affect levels of microbial metabolites by altering the composition of the microbiome. However, some microbial metabolites harbor paradoxical functions depending on the context (e.g., type of cancer). Furthermore, the metabolic effects of microorganisms on certain anticancer drugs such as irinotecan and gemcitabine, render the drugs ineffective or exacerbate their adverse effects. Therefore, a personalized and comprehensive consideration of the patient's condition is required when employing microbial metabolites to treat cancer. The purpose of this review is to summarize the correlation between gut microbiota-derived metabolites and cancer, and to provide fresh ideas for future scientific research.
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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 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".