A Dysbiotic Mycobiome Dominated by <em>Candida albicans</em> is Identified within Oral Squamous Cell Carcinomas
Bibliographic record
Abstract
Background: Studies employing next-generation sequencing (NGS) show that the oral fungal community (mycobiome) is far more complex than hitherto thought. However, the role of the oral mycobiome in health and disease, including oral carcinogenesis, has not been explored. Objective: To characterize the mycobiome associated with oral squamous cell carcinoma (OSCC). Methods: Tissue biopsies [cases: 25 OSCC; controls: 27 intra-oral fibro-epithelial polyp (FEP)] were collected from oral and maxillofacial units in Sri Lanka. Total DNA was extracted and subjected to sequencing of the fungal ITS2 region using Illumina’s 2x300 bp chemistry. High quality, non-chimeric 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 LEfSe. Results: 364 species representing 160 genera and 2 phyla (Ascomycota and Basidiomycota) were identified, with Candida and Malassezia making up 48% and 11% of the average mycobiome, respectively. However, only 5 species and 4 genera were detected in ≥50% of the samples. The species richness and diversity were significantly lower in OSCC. At the genus level, Candida, Hannaella and Gibberella were overrepresented in OSCC while Alternaria and Trametes were more abundant in FEP. Species-wise, C. albicans, C. etchellsii and Hannaella luteola-like species were enriched in OSCC while Malassezia restricta, Aspergillus tamarii, Alternaria alternate, Cladosporium halotolerans, and Hanseniaspora uvarum-like species were the most significantly abundant in FEP. Conclusions: A dysbiotic mycobiome dominated by C. albicans was found in association with OSCC. Whether this dysbiosis plays a role in oral carcinogenesis warrants 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) | 0.002 | 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".