MiR-154 inhibits the growth of laryngeal squamous cell carcinoma by targeting GALNT7
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
MicroRNAs are critical regulators of the development and progression of laryngeal squamous cell carcinoma (LSCC). However, the role of microRNA-154 (miR-154) in the development and progression of LSCC has not been clarified. We found that down-regulated miR-154 expression in LSCC tissues was associated with poorer prognosis in LSCC patients. MiR-154 over-expression inhibited the proliferation, clonogenicity, and migration of LSCC cells and induced cell cycle arrest, which were reversed by miR-154 inhibition. MiR-154 targeted GALNT7 expression by reducing GALNT7-regulated luciferase activity in LSCC cells while up-regulating GALNT7 mRNA transcription in LSCC tissues and cells. GALNT7 silencing significantly attenuated the proliferation, clonogenicity, and migration of LSCC cells and induced cell cycle arrest. Finally, intravenous treatment with lentivirus for miR-154, but not scrambled control miRNA, significantly restrained the growth of implanted LSCC Hep-2 tumors and decreased the tumor mass by reducing GALNT7 expression in mice. Therefore, miR-154 may serve as a novel prognostic marker and therapeutic target for LSCC.
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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 | 0.001 |
| 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".