Modulating Gut Microbiota Prevents Anastomotic Leak to Reduce Local Implantation and Dissemination of Colorectal Cancer Cells after Surgery
Bibliographic record
Abstract
PURPOSE: Anastomotic leak (AL) is a major complication in colorectal cancer surgery and consists of the leakage of intestinal content through a poorly healed colonic wound. Colorectal cancer recurrence after surgery is a major determinant of survival. We hypothesize that AL may allow cancer cells to escape the gut and lead to cancer recurrence and that improving anastomotic healing may prevent local implantation and metastatic dissemination of cancer cells. EXPERIMENTAL DESIGN: We investigated the association between AL and postoperative outcomes in patients with colorectal cancer. Using mouse models of poor anastomotic healing, we assessed the processes of local implantation and dissemination of cancer cells. The effect of dietary supplementation with inulin and 5-aminosalicylate (5-ASA), which activate PPAR-γ in the gut, on local anastomotic tumors was assessed in mice undergoing colonic surgery. Inulin and 5-ASA were also assessed in a mouse model of liver metastasis. RESULTS: Patients experiencing AL displayed lower overall and oncologic survival than non-AL patients. Poor anastomotic healing in mice led to larger anastomotic and peritoneal tumors. The microbiota of patients with AL displays a lower capacity to activate the antineoplastic PPAR-γ in the gut. Modulation of gut microbiota using dietary inulin and 5-ASA reinforced the gut barrier and prevented anastomotic tumors and metastatic spread in mice. CONCLUSIONS: Our findings reinforce the hypothesis that preventing AL is paramount to improving oncologic outcomes after colorectal cancer surgery. Furthermore, they pave the way toward dietary targeting of PPAR-γ as a novel way to enhance healing and diminish cancer recurrence.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".