Exploring the microbiome of oral epithelial dysplasia as a predictor of malignant progression
Bibliographic record
Abstract
A growing body of research associates the oral microbiome and oral cancer. Well-characterized clinical samples with outcome data are required to establish relevant associations between the microbiota and disease. The objective of this study was to characterize the community variations and the functional implications of the microbiome in low-grade oral epithelial dysplasia (OED) using 16S rRNA gene sequencing from annotated archival swabs in progressing (P) and non-progressing (NP) OED. We characterised the microbial community in 90 OED samples - 30 swabs from low-grade OED that progressed to cancer (cases) and 60 swabs from low-grade OED that did not progress after a minimum of 5 years of follow up (matched control subjects). There were small but significant differences between P and NP samples in terms of alpha diversity as well as beta diversity in conjunction with other clinical factors such as age and smoking status for both taxa and functional predictions. Across all samples, the most abundant genus was Streptococcus, followed by Haemophilus, Rothia, and Neisseria. Taxa and predicted functions were identified that were significantly differentially abundant with progression status (all Ps and NPs), when samples were grouped broadly by the number of years between sampling and progression or in specific time to progression for Ps only. However, these differentially abundant features were typically present only at low abundances. For example, Campylobacter was present in slightly higher abundance in Ps (1.72%) than NPs (1.41%) and this difference was significant when Ps were grouped by time to progression. Furthermore, several of the significantly differentially abundant functions were linked to the Campylobacteraceae family in Ps and may justify further investigation. Larger cohort studies to further explore the microbiome as a potential biomarker of risk in OED are warranted.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 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".