BRAF, TERT and HLA-G Status in the Papillary Thyroid Carcinoma: A Clinicopathological Association Study
Bibliographic record
Abstract
As BRAF, TERT, HLA-G, and microRNAs have been individually associated with papillary thyroid carcinoma (PTC), we aimed to evaluate the individual and collaborative role of these markers in PTC in the same patient cohort. HLA-G and BRAF tumor expression was evaluated by immunohistochemistry. Using molecular methods, BRAFV600E and TERT promoter mutations were evaluated in thyroid fine needle aspirates. MicroRNA tumor profiling was investigated using massively parallel sequencing. We observed strong HLA-G (67.96%) while BRAF (62.43%) staining was observed in PTC specimens. BRAF overexpression was associated with poor response to therapy. The BRAFV600E (52.9%) and TERTC228T (13%) mutations were associated with extrathyroidal extension, advanced-age, and advanced-stage cancer. The TERT rs2853669 CC+TC genotypes (38%) were overrepresented in metastatic tumors. Nine modulated microRNAs targeting the BRAF, TERT, and/or HLA-G genes were observed in PTC and involved with cancer-related signaling pathways. The markers were individually associated with PTC features, emphasizing the synergistic effect of BRAFV600E and TERTC228T; however, their collaborative role on PTC outcome was not fully demonstrated. The differentially expressed miRNAs targeting the BRAF and/or HLA-G genes may explain their increased expression in the tumor milieu.
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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 | 0.001 |
| 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".