Bibliographic record
Abstract
Breast cancer is the most diagnosed and lethal cancer globally in women. Presently, there is a need for novel targets for the development of improved therapeutics. Data mining through genetic screens suggests that the ribosome biogenesis protein WDR12 is essential for breast cancer cell proliferation. In this study, we characterize the effects of WDR12 knockdown in a panel of breast cancer cells. We show that WDR12 knockdown induces ribosomal RNA (rRNA) processing defects resulting in strong growth inhibition in all breast cancer cells tested, however only a subset of lines displayed short-term cell cycle defects. To further understand the cellular consequences of WDR12 suppression, we investigated the global proteomic landscape and found that WDR12 knockdown results in a perturbation of translational and ribosome biogenesis-related networks. Furthermore, we performed RNA-sequencing to assess the global transcriptional changes upon WDR12 knockdown and found dysregulation of the endoplasmic reticulum (ER) stress response pathway. Finally, we characterized the effect of long-term WDR12 suppression and found that growth inhibition was in part due to cellular senescence. Our results demonstrate that WDR12 is implicated in breast cancer cell growth and senescence, and its suppression may represent a viable therapeutic target for breast cancer.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".