PRMT1 inhibition perturbs RNA metabolism and induces DNA damage in clear cell renal cell carcinoma
Bibliographic record
Abstract
In addition to the ubiquitous loss of the VHL gene in clear cell renal cell carcinoma (ccRCC), co-deletions of chromatin-regulating genes are common drivers of tumorigenesis, suggesting potential vulnerability to epigenetic manipulation. A library of chemical probes targeting a spectrum of epigenetic regulators is screened using a panel of ccRCC models. MS023, a type I protein arginine methyltransferase (PRMT) inhibitor, is identified as an antitumorigenic agent. Individual knockdowns indicate PRMT1 as the specific critical dependency for cancer growth. Further analyses demonstrate impairments to cell cycle and DNA damage repair pathways upon MS023 treatment or PRMT1 knockdown. PRMT1-specific proteomics reveals an interactome rich in RNA binding proteins and further investigation indicates significant widespread disruptions in mRNA metabolism with both MS023 treatment and PRMT1 knockdown, resulting in R-loop accumulation and DNA damage over time. Our data supports PRMT1 as a target in ccRCC and informs a mechanism-based strategy for translational development. Here, the authors show that PRMT1 targeting inhibits clear cell renal cell carcinoma cells through perturbation of RNA metabolism and down-regulation of DNA repair pathways, resulting in the accumulation of unresolved R-loops and DNA damage.
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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".