The Oncogenic Roles of DICER1 RNase IIIb Domain Mutations in Ovarian Sertoli-Leydig Cell Tumors
Bibliographic record
Abstract
DICER1, an endoribonuclease required for microRNA (miRNA) biogenesis, is essential for embryogenesis and the development of many organs including ovaries. We have recently identified somatic hotspot mutations in RNase IIIb domain of DICER1 in half of ovarian Sertoli-Leydig cell tumors, a rare class of sex-cord stromal cell tumors in young women. These hotspot mutations lost IIIb cleavage activity of DICER1 in vitro and failed to produce 5p-derived miRNAs in mouse Dicer1-null ES cells. However, the oncogenic potential of these hotspot DICER1 mutations has not been studied. Here, we further revealed that the global expression of 5p-derived miRNAs was dramatically reduced in ovarian Sertoli-Leydig cell tumors carrying DICER1 hotspot mutations compared with those without DICER1 hotspot mutation. The miRNA production defect was associated with the deregulation of genes controlling cell proliferation and the cell fate. Using an immortalized human granulosa cell line, SVOG3e, we determined that the D1709N-DICER1 hotspot mutation failed to produce 5p-derived miRNAs, deregulated the expression of several genes that control gonadal differentiation and cell proliferation, and promoted cell growth. Re-expression of let-7 significantly inhibited the growth of D1709N-DICER1 SVOG3e cells, accompanied by the suppression of key regulators of cell cycle control and ovarian gonad differentiation. Taken together, our data revealed that DICER1 hotspot mutations cause systemic loss of 5p-miRNAs that can both drive pseudodifferentiation of testicular elements and cause oncogenic transformation in the ovary.
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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 | 0.000 | 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".