Faculty Opinions recommendation of Numb inhibits epithelial-mesenchymal transition via RBP-Jκ-dependent Notch1/PTEN/FAK signaling pathway in tongue cancer.
Bibliographic record
Abstract
BACKGROUND: Oral cancer has been estimated as the sixth most frequent solid cancer all over the world, in which tongue squamous cell carcinoma (TSCC) is the most common type of oral cancers. However, the mechanism of TSCC metastasizing to lymph node and distant sites has not been completely understood.METHODS: In this study, RT-qPCR method was used to detect the mRNA level of Numb, PTEN and Notch1 genes, as well as EMT-associated genes. Western blot assay was utilized to detect protein level of these genes. In addition, we determined cell proliferation by MTT assay and employed transwell invasion assay and wound healing assay to probe the abilities of invasion and migration, respectively. To investigate the role of PTEN, its inhibitor VO-Ohpic trihydrate was used to treat SCC-4 and CAL27 cells.RESULTS: We found that Numb expression was downregulated in SCC-9 and CAL-27 cells compared to NHOK cells. Instead, Notch1 level in SCC-9 and CAL-27 cells were higher than that in NHOK cells. Furthermore, the results showed that Numb overexpression significantly suppressed proliferation, migration and invasion of SCC-9 and CAL-27 cells via regulating Notch1 signaling and EMT-related genes expression. By contrast, we observed that RBP-Jκ knockdown had an inhibitory role in proliferation, migration and invasion of SCC-9 and CAL-27 cells. In cells with Numb overexpression or RBP-Jκ knockdown, p-FAK and EMT-related genes were remarkably regulated.CONCLUSIONS: Our findings provide new mechanism of understanding the metastasis of TSCC and help develop therapeutic strategies for treating tongue cancer. PMID: 31023264
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.275 | 0.083 |
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".