Suppressed oncogenic molecules involved in the treatment of colorectal cancer by fecal microbiota transplantation
Bibliographic record
Abstract
Dysbiosis of the intestinal microbiota is prevalent among patients with colorectal cancer (CRC). This study aims to explore the anticancer roles of the fecal microbiota in inhibiting the progression of colorectal cancer and possible mechanisms. The intestinal microbial dysbiosis in CRC mice was significantly ameliorated by fecal microbiota transplantation (FMT), as indicated by the restored ACE index and Shannon index. The diameter and number of cancerous foci were significantly decreased in CRC mice treated with FMT, along with the restoration of the intestinal mucosal structure and the lessening of the gland arrangement disorder. Key factors in oxidative stress (TXN1, TXNRD1, and HIF-1α); cell cycle regulators (IGF-1, BIRC5, CDK8, HDAC2, EGFR, and CTSL); and a critical transcription factor of the innate immune signal pathway (IRF5) were among the repressed oncogenic targets engaged in the FMT treatment of CRC. Correlation analysis revealed that their expressions were positively correlated with uncultured_bacterium_o_Mollicutes_RF39, Rikenellaceae_RC9_gut_group, and negatively correlated with Bacillus, Marvinbryantia, Roseburia, Angelakisella, Enterorhabdus, Bacteroides, Muribaculum, and genera of uncultured_bacterium_f_Eggerthellaceae, uncultured_bacterium_f_Xanthobacteraceae, Prevotellaceae_UCG-001, uncultured_bacterium_f_Erysipelotrichaceae, uncul-tured_bacterium_f_Lachnospiraceae, uncultured_bacterium_f_Ruminococcaceae, Eubacterium_coprostanoligenes_group, Ruminococcaceae_UCG-005, and uncultured_bacterium_f_Peptococcaceae. This study provides more evidence for the application of FMT in the clinical treatment of CRC.
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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) | 0.000 | 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 | 0.000 |
| 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".