siRNA directed against c-Myc inhibits proliferation and downregulates human telomerase reverse transcriptase in human colon cancer Colo 320 cells
Post-publication record
Source: Retraction Watch, joined by DOI. OpenAlex records retraction as is_retracted, a boolean over a state space with at least four values, so it cannot express an expression of concern, a correction or a reinstatement; it reports them as false, which reads as “fine”.
Bibliographic record
Abstract
The c-Myc and human telomerase reverse transcriptase gene (hTERT) gene are frequently deregulated and overexpressed in malignancy. hTERT activity is induced by c-Myc and strategies designed to inhibit c-Myc expression in cancer cells may have considerable therapeutic value. We designed and used a short hairpin RNA to inhibit c-Myc expression in Colo 320 cells and validated its effect on cell proliferation. In this study, four c-Myc-shRNA expression vectors were constructed and introduced into Colo 320 cells. The effects of c-Myc silencing on tumor cell growth was assessed by soft agar assay and DNA synthesis experiments. The expressions of c-Myc and hTERT were also assessed by real-time reverse transcription-polymerase chain reaction and Western blot analysis. Upon transient transfection with plasmid encoding shRNA, it was found that expression of c-Myc and hTERT decreased in shRNA-transfected cells. The downregulation of c-Myc and hTERT inhibited cell growth, shortened telomere lengths, and suppressed telomerase activity. In conclusion, our findings demonstrate that shRNA of c-Myc can inhibit the DNA replication in Colo 320 cells effectively and reduce telomere length and telomerase activity, therefore, it could be used as a new potential anticancer tool for therapy of human colon 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".