Silencing of the CCNB1, Her2, and PKC genes by small interfering RNA differently retards the division of different human cancer cell lines
Bibliographic record
Abstract
Deregulation of genes encoding proteins responsible for cell cycle control frequently accompanies cell malignization and switches the cell program from differentiation and apoptosis to uncontrollable proliferation. We used siRNAs targeted to HER2 , protein kinase C ( PKC ), and cyclin B1 ( CCNB1 ) mRNAs to evaluate the therapeutic potential of the suppression of genes coding for key cell cycle regulators in different human cancer cells. The CCNB1, HER2 , or PKC mRNA levels were efficiently reduced within 48 h after transfection with siCycB1, siHER2 or siPKC, respectively. Silencing of HER2, PKC , and CCNB1 substantially reduced the growth rates of all cell lines under study except HL-60 but did not affect cell death or apoptosis. The most pronounced inhibition of cell division was induced by siCycB1 in SK-N-MC cells and by siPKC in MCF-7 cells. We conclude that the selected siRNAs inhibit tumor cell division, and the investigated genes can be promising targets in cancer treatment.
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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".