LncRNA UCA1/miR‐124 axis modulates TGFβ1‐induced epithelial‐mesenchymal transition and invasion of tongue cancer cells through JAG1/Notch signaling
Bibliographic record
Abstract
Tongue cancer remains a massive threat to public health due to the high rate of metastasis. Tumor cell epithelial-mesenchymal transition (EMT), which can be induced by transforming growth factor β1 (TGFβ1), has been regarded as a significant contributor to cancer invasion and migration. In our previous study, long noncoding RNA (lncRNA) MALAT1/miR-124/JAG1 axis modulates the growth of tongue cancer. In addition to metastasis-associated lung adenocarcinoma transcript 1 (MALAT1), another lncRNA, urothelial cancer associated 1 (UCA1), can promote EMT and cancer metastasis. In the present study, UCA1 was overexpressed in tongue cancer tissues and cell lines. UCA1 overexpression was correlated to the poorer prognosis of patients with tongue cancer. UCA1 knockdown significantly suppressed TGFβ1-induced tongue cancer cell invasion and EMT by decreasing vimentin and increasing E-cadherin. Regarding the molecular mechanism, UCA1 could directly bind to microRNA-124 (miR-124) and negatively regulate each other. UCA1 knockdown ameliorated, whereas miR-124 inhibition exacerbated TGFβ1-induced EMT and invasion in tongue cancer cells through miR-124 downstream jagged 1 (JAG1) and Notch signaling. Moreover, miR-124 inhibition partially impaired the effect of UCA1 knockdown. In tongue cancer tissues, miR-124 expression was remarkably decreased, whereas JAG1 mRNA expression was increased. miR-124 was negatively correlated with UCA1 and JAG1. UCA1 and JAG1 were positively correlated. In summary, we provided a novel mechanism by which the EMT process and cancer cell invasion in tongue cancer could be modulated from the perspective of lncRNA-miRNA-mRNA regulation.
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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".