Cancer‐associated somatic <i><scp>DICER1</scp></i> hotspot mutations cause defective <scp>miRNA</scp> processing and reverse‐strand expression bias to predominantly mature 3p strands through loss of 5p strand cleavage
Bibliographic record
Abstract
Our group recently described recurrent somatic mutations of the miRNA processing gene DICER1 in non-epithelial ovarian cancer. Mutations appeared to be clustered around each of four critical metal-binding residues in the RNase IIIB domain of DICER1. This domain is responsible for cleavage of the 3' end of the 5p miRNA strand of a pre-mRNA hairpin. To investigate the effects of these cancer-associated 'hotspot' mutations, we engineered mouse DICER1-deficient ES cells to express wild-type and an allelic series of the mutant DICER1 variants. Global miRNA and mRNA profiles from cells carrying the metal-binding site mutations were compared to each other and to wild-type DICER1. The miRNA and mRNA profiles generated through the expression of the hotspot mutations were virtually identical, and the DICER1 hotspot mutation-carrying cells were distinct from both wild-type and DICER1-deficient cells. Further, miRNA profiles showed that mutant DICER1 results in a dramatic loss in processing of mature 5p miRNA strands but were still able to create 3p strand miRNAs. Messenger RNA (mRNA) profile changes were consistent with the loss of 5p strand miRNAs and showed enriched expression for predicted targets of the lost 5p-derived miRNAs. We therefore conclude that cancer-associated somatic hotspot mutations of DICER1, affecting any one of four metal-binding residues in the RNase IIIB domain, are functionally equivalent with respect to miRNA processing and are hypomorphic alleles, yielding a global loss in processing of mature 5p strand miRNA. We further propose that this resulting 3p strand bias in mature miRNA expression likely underpins the oncogenic potential of these hotspot mutations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".