Inhibiting polo-like kinase 1 enhances radiosensitization via modulating DNA repair proteins in non-small-cell lung cancer
Bibliographic record
Abstract
To assure faithful chromosome segregation, cells make use of the spindle assembly checkpoint, which can be activated in aneuploid cancer cells. In this study, the efficacies of inhibiting polo-like kinase 1 (PLK1) on the radiosensitization of non-small-cell lung cancer (NSCLC) cells were studied. Clonogenic survival assay was performed to identify the effects of the PLK1 inhibitor on radiosensitivity within NSCLC cells. Mitotic catastrophe assessment was used to measure the cell death and histone H2AX protein (γH2AX) foci were utilized to assess the DNA double-strand breaks (DSB). The transcriptome was analyzed via unbiased profiling of microarray expression. The results showed that the postradiation mitotic catastrophe induction and the DSB repair were induced by PLK1 inhibitor BI-6727, leading to an increase in the radiosensitivity of NSCLC cells. BI-6727 in combination with radiation significantly induced the delayed tumor growth. PLK1-silenced NSCLC cells showed an altered mRNA and protein expression related to DNA damaging, replication, and repairing, including the DNA-dependent protein kinase (DNAPK) and topoisomerase II alpha (TOPO2A). Furthermore, inhibition of PLK1 blocked 2 important DNA repair pathways. To summarize, our study showed PLK1 kinase as an option in the therapy of NSCLC.
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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".