A dysbiotic mycobiome dominated by <i>Candida albicans</i> is identified within oral squamous-cell carcinomas
Bibliographic record
Abstract
The aim of this study was to characterize the mycobiome associated with oral squamous-cell carcinoma (OSCC). DNA was extracted from 52 tissue biopsies (cases: 25 OSCC; controls: 27 intra-oral fibro-epithelial polyps [FEP]) and sequenced for the fungal internal transcribed spacer 2 region using Illumina™ 2 x300bp chemistry. Merged reads were classified to species level using a BLASTN-algorithm with UNITE’s named species sequences as reference. Downstream analyses were performed using QIIME™ and linear discriminant analysis effect size. A total of 364 species representing 160 genera and two phyla (Ascomycota and Basidiomycota) were identified, with Candida and Malassezia making up 48% and 11% of the average mycobiome, respectively. However, only five species and four genera were detected in ≥50% of the samples. The species richness and diversity were significantly lower in OSCC. Genera Candida, Hannaella, and Gibberella were overrepresented in OSCC; Alternaria and Trametes were more abundant in FEP. Species-wise, Candida albicans, Candida etchellsii, and a Hannaella luteola–like species were enriched in OSCC, while a Hanseniaspora uvarum–like species, Malassezia restricta, and Aspergillus tamarii were the most significantly abundant in FEP. In conclusion, a dysbiotic mycobiome dominated by C. albicans was found in association with OSCC, a finding worth further investigation.
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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 | 0.000 |
| 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".