A Review of Gut Microbiota‐Derived Metabolites in Tumor Progression and Cancer Therapy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 it