Comprehensive Analysis of the Transcriptional and Mutational Landscape of Follicular and Papillary Thyroid Cancers
Bibliographic record
Abstract
Follicular thyroid carcinoma (FTC) and benign follicular adenoma (FA) are indistinguishable by preoperative diagnosis due to their similar histological features. Here we report the first RNA sequencing study of these tumors, with data for 30 minimally invasive FTCs (miFTCs) and 25 FAs. We also compared 77 classical papillary thyroid carcinomas (cPTCs) and 48 follicular variant of PTCs (FVPTCs) to observe the differences in their molecular properties. Mutations in H/K/NRAS, DICER1, EIF1AX, IDH1, PTEN, SOS1, and SPOP were identified in miFTC or FA. We identified a low frequency of fusion genes in miFTC (only one, PAX8-PPARG), but a high frequency of that in PTC (17.60%). The frequencies of BRAFV600E and H/K/NRAS mutations were substantially different in miFTC and cPTC, and those of FVPTC were intermediate between miFTC and cPTC. Gene expression analysis demonstrated three molecular subtypes regardless of their histological features, including Non-BRAF-Non-RAS (NBNR), as well as BRAF-like and RAS-like. The novel molecular subtype, NBNR, was associated with DICER1, EIF1AX, IDH1, PTEN, SOS1, SPOP, and PAX8-PPARG. The transcriptome of miFTC or encapsulated FVPTC was indistinguishable from that of FA, providing a molecular explanation for the similarly indolent behavior of these tumors. We identified upregulation of genes that are related to mitochondrial biogenesis including ESRRA and PPARGC1A in oncocytic follicular thyroid neoplasm. Arm-level copy number variations were correlated to histological and molecular characteristics. These results expanded the current molecular understanding of thyroid cancer and may lead to new diagnostic and therapeutic approaches to the disease.
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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 | 0.000 |
| 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".