miR-146a-5p mediates epithelial–mesenchymal transition of oesophageal squamous cell carcinoma via targeting Notch2
Bibliographic record
Abstract
BACKGROUND: Our previous study found that dysregulated microRNA-146a-5p (miR-146a-5p) is involved in oesophageal squamous cell cancer (ESCC) proliferation. This article aimed to evaluate its detailed mechanisms in ESCC epithelial-mesenchymal transition (EMT) progression. METHODS: Invasion assay, qRT-PCR and western blotting were used to validate the roles of miR-146a-5p and Notch2 in EMT progression. miRNA target gene prediction databases and dual-luciferase reporter assay were used to validate the target gene. RESULTS: miR-146a-5p inhibitor led to increase of invaded ESCC cells, while miR-146a-5p mimics inhibited invasion ability of ESCC cells. Protein level of E-cadherin decreased, whereas those of Snail and Vimentin increased in the anti-miR-146a-5p group, which demonstrated that miR-146a-5p inhibits EMT progression of ESCC cells. miRNA target gene prediction databases indicated the potential of Notch2 as a direct target gene of miR-146a-5p and dual-luciferase reporter assay validated it. Importantly, shRNA-Notch2 restrained EMT and partially abrogated the inhibiting effects of miR-146a-5p on EMT progression of ESCC cells. CONCLUSIONS: miR-146a-5p functions as a tumour-suppressive miRNA targeting Notch2 and inhibits the EMT progression of ESCC.
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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 | 0.001 |
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".