Telomere-related Genomic Instability in Papillary Thyroid Cancers: A Preliminary Study
Bibliographic record
Abstract
Papillary thyroid carcinoma (PTC) has two main histologic variants: classical-PTC (CL-PTC) and follicular variant PTC (FV-PTC). Recently, due to its similar features to benign lesions, the encapsulated FV-PTC variant was reclassified as noninvasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTP). Nonetheless, specific molecular signatures are not yet available. It is well known that telomere-related genome instability is caused by inappropriate DNA repair of dysfunctional telomeres and that mechanisms involved in the damaged telomere repair processing may led to detrimental outcomes, altering the 3D nuclear telomere and genome organization in cancer cells. This pilot study aimed to evaluate whether a specific nuclear telomere architecture might characterize NIFTP, potentially distinguishing it from other PTC histologic variants. Our findings demonstrate that 3D telomere profiles of CL-PTC and FV-PTC were different from NIFTP and that NIFTP more closely resembles follicular thyroid adenoma (FTA). NIFTP has longer telomeres than CL-PTC and FV-PTC samples and telomere length overlaps in NIFTP and FTA. There was no association between BRAF expression and telomere length in all tested samples. Our data showing that 3D nuclear telomere organization is altered differently in thyroid cancer variants, suggest that this parameter might guide clinical management of NIFTP. Although further investigations in a larger cohort of patients are necessary to corroborate our observations, telomere-related genomic instability might be of value in the diagnosis of NIFTP and allow for a more appropriate selection of the correct treatment.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".