The applications of circulating cell-free DNA for oral squamous cell carcinoma patients as non-invasive diagnostics of structural variants and fusions
Bibliographic record
Abstract
Abstract Circulating cell-free DNA (cfDNA) has been widely used as a prognostic marker for different cancers. In this study, we used cfDNA from oral squamous cell carcinoma (OSCC) patients to study various correlation factors that could improve the disease early-stage diagnostics and/or prognosis. We found that OSCC patient cfDNA concentration can serve as an indicator of tumor stage, malignancy, and survival prognosis. Deep genome sequencing of cfDNA revealed genomic alterations, such as copy number variations, fusion gene identification, and viral integrations. Copy number variation analysis suggested correlation with amplification and deletion in chromosome 1 at loci 1q, 2q, 3p, 3q and 8q22. Moreover, at these loci, amplification of TP53, PIK3CA and other genes related to keratinization in OSCC patients were observed. In addition, we identified the novel fusion gene, TRMO-TRNT1 ‘chimera’, in seven high-grade tumor samples. The parental genes of this chimera, TRMO and TRNT1, are known to play roles in tRNA modification and DNA repair, respectively. Therefore, our study indicates that liquid biopsy may thus serve as a sensitive tool to study OSCC patient genomic alterations by exploring cfDNA circulating in the plasma, by means of an easy-to-use blood test. Finally, we detected integrations of human papilloma virus, simian virus, and enterovirus in the OSCC samples, that may point to the origins of 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".