Applications for Circulating Cell-Free DNA in Oral Squamous Cell Carcinoma: A Non-Invasive Approach for Detecting Structural Variants, Fusions, and Oncoviruses
Bibliographic record
Abstract
Background: Circulating cell-free DNA (cfDNA) has been widely used as a prognostic marker for different cancers. Objective: In this study, we used 30 cfDNA samples from oral squamous cell carcinoma (OSCC), 199 public OSCC samples, and 192 normal samples to study various correlation factors that could improve the early-stage diagnostics and/or prognosis of OSCC. Methods: The statistical correlation between healthy and OSCC patients was done and deep sequencing analyses was performed to study various genomic alterations likes copy number variation (CNV), and single nucleotide variants (SNVs), gene fusion and genomic integration of viruses. Results: We found that the 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 CNVs, fusion genes, and viral integrations. The CNV analysis suggested a correlation with amplification and deletion in chromosomes at loci 1q, 2q, 3p, 3q, and chromosome 8 at loci q22. Moreover, at these loci, amplification of TP53, PIK3CA, and other genes related to keratinization in OSCC patients was observed. In addition, we identified a novel abundant 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. We have identified SNVs in our OSCC cohort. Some of these SNVs, like KMT2C, MUC3A, and MUC6, have been identified as common cases in different cancer populations. Finally, we detected contigs integrations of human papillomavirus, simian virus, and enterovirus in the OSCC samples, which may point to the potential causes of OSCC. Conclusions: Our results indicate that the liquid biopsy technique may thus serve as a sensitive tool to study OSCC patient genomic alterations by exploring cfDNA circulating in the plasma, providing an easy-to-use blood test in the future.
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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 | 0.002 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".