Significance of Plk1 regulation by miR‐100 in human nasopharyngeal cancer
Bibliographic record
Abstract
Polo-like kinase 1 (Plk1) is a critical regulator of many stages of mitosis; increasing evidence indicates that Plk1 overexpression correlates with poor clinical outcome, yet its mechanism of regulation remains unknown. Hence, a detailed evaluation was undertaken of Plk1 expression in human nasopharyngeal cancer (NPC), the cellular effects of targeting Plk1 using siRNA in combination with ionizing radiation (RT) and potential upstream microRNAs (miRs) that might regulate Plk1 expression. Using immunohistochemistry, Plk1 was observed to be overexpressed in 28 of 40 (70%) primary NPC biopsies, which in turn was associated with a higher likelihood of recurrence (p = 0.018). SiPlk1 significantly inhibited Plk1 mRNA and protein expression, and decreased Cdc25c levels in NPC cell lines. This depletion resulted in cytotoxicity of C666-1 cells, enhanced by the addition of RT, mediated by G2/M arrest, increased DNA double-strand breaks, apoptosis, and caspase activation. Immunofluorescence demonstrated that the G2/M arrest was associated with aberrant spindle formation, leading to mitotic arrest. In vivo, transfection of C666-1 cells and systemic delivery of siPlk1 decreased tumour growth. MicroRNA-100 (miR-100) was predicted to target Plk1 mRNA, which was indeed underexpressed in C666-1 cells, inversely correlating with Plk1 expression. Using luciferase constructs containing the 3'-UTR of Plk1 sequence, we document that miR-100 can directly target Plk1. Hence, our data demonstrate for the first time that underexpressed miR-100 leads to Plk1 overexpression, which in turn contributes to NPC progression. Targeting Plk1 will cause mitotic catastrophe, with significant cytotoxicity both in vitro and in vivo, underscoring the important therapeutic opportunity of Plk1 in NPC.
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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".