CDC40 suppression induces CDCA5 splicing defects and anti-proliferative effects in lung cancer cells
Bibliographic record
Abstract
High mortality and low response rates in lung cancer patients call for novel therapeutic targets. Data mining of whole-genome genetic dependency screens suggest Cell Division Cycle 40 (CDC40) to be an essential protein for lung cancer cell survival. We characterized CDC40 knockdown effects in multiple lung cancer cell lines, revealing induced cell cycle defects that resulted in strong growth inhibition and activation of apoptosis. Global transcriptional and splicing changes were also investigated, where CDC40 knockdown resulted in perturbation of splicing- and translation-related genes as well as more transcripts with intron retention. In the transcript of the cell cycle regulatory protein CDCA5, CDC40 knockdown was shown to induce retention of the first intron, leading to an increase in the unspliced CDCA5 transcript and subsequent decrease in CDCA5 protein expression. Additionally, protein-protein interactions of CDC40 were explored and spliceosome components were found to be its main binding partners, further highlighting the role of CDC40 in splicing. CDC40 mutation analysis suggests that it may be difficult to disrupt key interactions using small molecules within a large complex. Our results demonstrate that CDC40 is essential for lung cancer cell growth, and that its inhibition may represent a viable therapeutic strategy for lung 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".