Targeting RhoC by Way of Ribozyme Trangene in Human Breast Cancer Cells and its Impact on Cancer Invasion
Bibliographic record
Abstract
Background: Cell motility and migration are known to be regulated by the Rho family of GTPases through their effects on the actin cytoskeleton. In breast cancer studies, RhoC has been identified as a highly specific marker in detecting tumors that developed metastases. This study aims to investigate the impact of targeting RhoC in human breast cancer cells by utilising ribozyme transgene technology and to assess its effect on cancer cell invasion. Methods: Retroviral hammerhead ribozyme transgenes, regulated by doxycycline, were designed to specifically target human RhoC mRNA. The breast cancer cell line MDA-MB-231 was transfected with either a retroviral RhoC transgene or a control retroviral transgene. Stably transfected cells were tested for their invasiveness and migratory properties in vitro . Results: In vitro testing of the invasiveness of wild type, plasmid control and the RhoC knockdown cells showed that MDA-MB-231 D RHOC cells had significantly reduced invasiveness compared with MDA-MB-231 WT (p < 0.038 RHOC2 knockdown cells; p < 0.006 RHOC3 knockdown cells) and MDA-MB-231 pRevTRE control plasmid cells (p < 0.07 RHOC2 knockdown cells; p < 0.002 RHOC3 knockdown cells). An even greater reduction in invasiveness of the MDA-MB-231 D RHO C cells compared with the MDA-MB-231 WT cells was seen in response to hepatocyte growth factor (HGF/SF) (p < 0.009 RHOC1 knockdown; p = 0.004 RHOC2 knockdown; p = 0.00007 RHOC3 knockdown). The addition of doxycycline significantly improved the effectiveness of the ribozyme transgenes (p < 0.04 for all three Rho ribozymes), but did not improve the effectiveness of these knockdown cells when treated with HGF/SF (p > 0.1 for all three ribozymes). Conclusions: This data would indicate that targeting RhoC may be an effective way to reduce the invasive potential of human breast cancer cells. World J Oncol. 2010;1(1):7-13 doi: https://doi.org/10.4021/wjon2010.01.1202
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".