Inflammatory Bacteriome and Oral Squamous Cell Carcinoma
Bibliographic record
Abstract
Results from microbiome studies on oral cancer have been inconsistent, probably because they focused on compositional analysis, which does not account for functional redundancy among oral bacteria. Based on functional prediction, a recent study revealed enrichment of inflammatory bacterial attributes in oral squamous cell carcinoma (OSCC). Given the high relevance of this finding to carcinogenesis, we aimed here to corroborate them in a case-control study involving 25 OSCC cases and 27 fibroepithelial polyp (FEP) controls from Sri Lanka. DNA extracted from fresh biopsies was sequenced for the V1 to V3 region with Illumina's 2 × 300-bp chemistry. High-quality nonchimeric merged reads were classified to the species level with a prioritized BLASTN-based algorithm. Downstream compositional analysis was performed with QIIME (Quantitative Insights into Microbial Ecology) and linear discriminant analysis effect size, while PICRUSt (Phylogenetic Investigation of Communities by Reconstruction of Unobserved States) was utilized for bacteriome functional prediction. The OSCC tissues tended to have lower species richness and diversity. Genera Capnocytophaga, Pseudomonas, and Atopobium were overrepresented in OSCC, while Lautropia, Staphylococcus, and Propionibacterium were the most abundant in FEP. At the species level, Campylobacter concisus, Prevotella salivae, Prevotella loeschii, and Fusobacterium oral taxon 204 were enriched in OSCC, while Streptococcus mitis, Streptococcus oral taxon 070, Lautropia mirabilis, and Rothia dentocariosa among others were more abundant in FEP. Functionally, proinflammatory bacterial attributes, including lipopolysaccharide biosynthesis and peptidases, were enriched in the OSCC tissues. Thus, while the results in terms of species composition significantly differed from the original study, they were consistent at the functional level, substantiating evidence for the inflammatory nature of the bacteriome associated with OSCC.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".