miR‐5191 acts as a tumor suppressor in salivary adenoid cystic carcinoma by targeting Notch‐2
Bibliographic record
Abstract
OBJECTIVE: This study sought to investigate the effect of miR-5191 on proliferation, invasion and metastasis in salivary adenoid cystic carcinoma (SACC). MATERIALS AND METHODS: The differential expression level of miR-5191 between 5 primary tumor and adjacent non-neoplastic samples, and in two SACC cell lines was detected by quantitative real-time PCR. Cell proliferation, invasion, and migration were performed, followed by luciferase reporter assay and western analysis. The effect of miR-5191 on cell proliferation and apoptosis was evaluated by cell growth and apoptosis assay. The function of miR-5191 in SACC tumorigenesis and metastasis in vivo was investigated by nude mice experiment. The associations between miR-5191/Notch-2 expression and clinicopathological features were analyzed. RESULTS: miR-5191 was downregulated in primary tumor tissues and SACC-LM cells. By targeting Notch-2, miR-5191 expression level affected the migration, invasion, and proliferation of SACC cells. Overexpression of miR-5191 inhibited the expression levels of Notch-2, followed by the decreased expression of c-Myc, Bcl-2, Hes-1, Hey-1, and Cyclin D1. In vivo, miR-5191 overexpression suppressed the SACC tumorigenesis and pulmonary metastasis in mice. In SACC patients, higher expression of miR-5191 was related to better prognoses and lower possibility of metastasis. CONCLUSIONS: miR-5191 acts as a tumor suppressor in SACC by targeting Notch-2.
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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".