The Human Microbiome in Relation to Cancer Risk: A Systematic Review of the Literature
Bibliographic record
Abstract
Background: Dysbiosis in the human microbiome may play a role in a number of chronic diseases, such as obesity, diabetes and cancer. The potential link between the microbiome and cancer development might offer new opportunities for cancer prevention by understanding etiologic pathways as well as for screening, diagnosis and maybe even treatment. The objective of this systematic review was to evaluate strength of the current data on the relation between the human microbiome and cancer in human studies. Methods: Relevant published articles were identified up until 2017 in PubMed, Embase and the Cochrane Library. All on-topic case-control studies, cohort studies and RCT's written in English were screened by two independent investigators. Studies in children, in vitro and animal studies were excluded. Quality was assessed with the Newcastle-Ottawa scale. Results: Fifty-eight studies were case-control studies, including one nested case-control study, and one was a cohort study. The gut microbiome was the microbiome most frequently studied, followed by the oral microbiome and then some studies on the bile duct, cervical and intrauterine, esophagus and gastric, laryngeal, lung, skin and urinary microbiome. All articles, besides one article from 1983, showed specific differences in microbiome distribution between cases and controls either based on presence or abundance of certain microbiota. Some findings were consistent between studies but many differences in results were noticed since various known and unknown factors influence the microbiome (eg ethnicity, smoking, diet, cancer stage & type). Twenty-seven studies linked differences in the gut microbiome with colorectal cancer. The most consistent findings were for Fusobacterium (most important species Fusobacterium nucleatum), Porphyromonas and Peptostreptococcus being significantly enriched in fecal and mucosal samples from colorectal cancer patients. Two studies on the cervical microbiome found a decrease in abundance of the beneficial Lactobacillus crispatus in cervical cancer cases. Concerning the oral microbiome, five articles observed a changed abundance of Streptococcus in oral cancer patients versus controls, four reported altered abundance of Prevotella and two an increase in Rothia. A decrease of Neisseria in the oral microbiome was found for pancreatic cancer in two articles and for oral cancer in one study. Three studies found a decrease in Neisseria and Haemophilus when investigating the tongue coating samples of patients with colorectal, lung and gastric cancer. Conclusion: This review provides important observations in potential changes in the gut micro-organisms in relation to (particularly colorectal) cancer. However, for most of the microbiome and cancers the evidence was still too weak to draw firm conclusions. Future prospective studies with prediagnostic specimen collection and with newer techniques and a more uniform sample collection are required to establish causal links.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".