The Anti-neoplastic Effects of Probiotics and Prebiotics against Colorectal Cancer: A Systematic Review
Bibliographic record
Abstract
With the world’s incidence of non-communicable diseases (NCDs) increasing, colon and rectal cancers now form the 3rd most common form of cancer globally, the need to find new solutions to colorectal cancer (CRC) is paramount, as current treatment is limited and comes with many unfavourable side effects. Studies on probiotic bacteria and prebiotic compounds spanning the last ten years reveal promising results describing their ability to act against colorectal cancer development. After screening papers with a specific inclusion criterion, 23 papers were selected for this review. The primary endpoints, biomarkers, and other data were analysed. The results show that overall, the prebiotics and probiotic bacteria included in this study (predominantly the genera Lactobacillus and Bifidobacterium) have promising anti-neoplastic effects against colorectal cancer, although in varying amounts. Other prebiotics such as fructooligosaccharides, branched fructans, and other plant extracts, were shown to have equally positive effects. The concept of using probiotics/prebiotics in addition to established cancer treatment seems more feasible with the various benefits highlighted in this review. At the very least, probiotics/prebiotics may be useful adjuvants, to be used alongside pre-existing colorectal cancer treatment. Probiotics/prebiotics may help alleviate some undesirable side effects of pre-existing treatment (i.e., fluorouracil) such as dysbiosis. Thus, this review aims to build upon the foundations established in microbiome research and encourage the course of future prebiotic and probiotic testing, to further our understanding related to the effect of probiotics/prebiotics on gut health and help treat the growing burden of colorectal cancer.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".