MicroRNA-378-3p/5p suppresses the migration and invasiveness of oral squamous carcinoma cells by inhibiting KLK4 expression
Bibliographic record
Abstract
Distant metastasis frequently occurs in oral squamous cell carcinoma (OSCC) and contributes to the adverse prognosis for patients with OSCC. However, the potential mechanisms behind the metastasis have not yet been clarified. This study investigated the role of miR-378 in the migration and invasiveness of OSCC in vitro and in vivo. According to our results, the migration and invasiveness of OSCC cells were increased in cells overexpressing miR-378, and reduced in cells where miR-378-3p/5p was silenced. In addition, overexpression of miR-378 suppressed the expressions and activities of matrix metalloproteinase 9 (MMP-9) and MMP-2. Epithelial-mesenchymal transition (EMT) was restrained by overexpression of miR-378, as evidenced by an increase in E-cadherin expression and decrease in N-cadherin and uPA expression. However, knockdown of miR-378-3p/5p produced the opposite results. Moreover, kallikrein-related peptidase 4 (KLK4) was confirmed to be a target gene of miR-378. Overexpression of KLK4 reversed the induced decrease in migration and invasiveness of cells overexpressing miR-378 by upregulating the levels of MMP-9, MMP-2, and N-cadherin, and downregulating the level of E-cadhrin. Finally, the number of metastasis nodules in the lung tissues of nude mice was reduced by overexpression of miR-378, whereas the number of metastases increased with knockdown of miR-378. Taken together, our results suggest that the miR-378-KLK4 axis is involved in the mechanisms behind the migration and invasiveness of OSCC cells. Targeting the miR-378-KLK4 axis may be an effective measure for treating OSCC.
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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) | 0.001 | 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".