ADAM17 promotes epithelial-mesenchymal transition via TGF-β/Smad pathway in gastric carcinoma cells
Bibliographic record
Abstract
Although a disintegrin and metalloproteinase-17 (ADAM17) overexpression has been demonstrated in numerous human tumors including gastric cancer, its role in gastric cancer development remains to be clarified. In the present study, we identify that ADAM17 activates TGF-β/Smad signaling to promote epithelial-mesenchymal transition (EMT) in gastric cancer cells. We found that ADAM17 promotes proliferation, migration and invasion in gastric carcinoma cells. Subsequently, we revealed that silencing ADAM17 induces the expression of epithelial marker of E-cadherin and downregulates expression of mesenchymal markers including N-cadherin, vimentin and Snail in MGC803 and MKN45 cells, whereas ADAM17 overexpression reverses these changes in BGC823 and HGC27 cells. Furthermore, ADAM17 knockdown significantly inhibits the expression of TGF-β and its downstream signaling molecules p-Smad2 and p-Smad3 in MGC803 and MKN45 cells. Consistently, ADAM17 overexpression reversed these changes in BGC823 and HGC27 cells. These results suggest that ADAM17 promotes epithelial-mesenchymal transition via the TGF-β/Smad pathway. Collectively, the present study demonstrates that ADAM17 plays a critical role in the development of gastric cancer and provides a potential therapeutic target for gastric cancer.
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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".