DICER1 RNase IIIb domain mutations trigger widespread miRNA dysregulation and MAPK activation in pediatric thyroid cancer
Bibliographic record
Abstract
DICER1 is a highly conserved RNase III endoribonuclease essential for the biogenesis of single-stranded mature microRNAs (miRNAs) from stem-loop precursor miRNAs. Somatic mutations in the RNase IIIb domain of DICER1 impair its ability to generate mature 5p miRNAs and are believed to drive tumorigenesis in DICER1 syndrome-associated and sporadic thyroid tumors. However, the DICER1 -driven specific changes in miRNAs and resulting changes in gene expression are poorly understood in thyroid tissue. In this study, we profiled the miRNA (n=2,083) and mRNA (n=2,559) transcriptomes of 20 non-neoplastic, 8 adenomatous and 60 pediatric thyroid cancers (13 follicular thyroid cancers [FTC] and 47 papillary thyroid cancers [PTC]) of which 8 had DICER1 RNase IIIb mutations. All DICER1- mutant differentiated thyroid cancers (DTC) were follicular patterned (six follicular variant PTC and two FTC), none had lymph node metastasis. We demonstrate that DICER1 pathogenic somatic mutations were associated with a global reduction of 5p-derived miRNAs, including those particularly abundant in the non-neoplastic thyroid tissue such as let-7 and mir-30 families, known for their tumor suppressor function. There was also an unexpected increase of 3p miRNAs, possibly associated with DICER1 mRNA expression increase in tumors harboring RNase IIIb mutations. These abnormally expressed 3p miRNAs, which are otherwise low or absent in DICER1 -wt DTC and non-neoplastic thyroid tissues, make up exceptional markers for malignant thyroid tumors harboring DICER1 RNase IIIb mutations. The extensive disarray in the miRNA transcriptome results in gene expression changes, which were indicative of positive regulation of cell-cycle. Moreover, differentially expressed genes point to increased MAPK signaling output and loss of thyroid differentiation comparable to the RAS-like subgroup of PTC (as coined by The Cancer Genome Atlas), which is reflective of the more indolent clinical behavior of these tumors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".