Synuclein-γ suppression mediated by RNA interference inhibits the clonogenicity and invasiveness of MCF-7 cells
Bibliographic record
Abstract
The aim of the present study was to investigate the effects of synuclein-γ (SNCG) downregulation by RNA interference (RNAi) on the clonogenicity and invasiveness of MCF-7 breast cancer cells. This study used four pairs of SNCG-specific siRNAs which were designed and cloned into the pGPU6 plasmid for introduction into an MCF-7 cell line. The SNCG knockdown efficacies of the four siRNAs were compared using the reverse transcription polymerase chain reaction (RT-PCR) and immunocytochemistry. The cells' clonogenic and invasive phenotypes were examined with clonogenic and Boyden chamber assays. In comparison with the non-specific siRNA and empty vector controls, all four SNCG siRNAs were observed to significantly inhibit SNCG expression at the mRNA and protein levels (F=481.06, P<0.001; F=147.42, P<0.0001). SNCG suppression mediated by RNAi successfully inhibited the clonogenicity (P=0.002) and invasiveness (P<0.001) of transfected MCF-7 cells. According to the results of the present study, we concluded that SNCG suppression mediated by RNAi significantly suppressed SNCG expression at the mRNA and protein levels, suggesting that SNCG suppression mediated by an RNAi strategy may become a novel approach for treating advanced breast 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) | 0.001 | 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".