<i>Helicobacter pylori</i>‐induced YAP1 nuclear translocation promotes gastric carcinogenesis by enhancing IL‐1β expression
Bibliographic record
Abstract
Gastric cancer (GC) is one of the most common and malignant pathologies, and a significant portion of GC incidences develops from Helicobacter pylori (Hp)-induced chronic gastritis. Although the exact mechanisms of GC are complex and poorly understood, gastric carcinogenesis is a good model to investigate how inflammation and infection collaboratively promote tumorigenesis. Yes-associated protein 1 (YAP1) is the key effector of the Hippo pathway, which is silenced in most human cancers. Herein, we verified the tumor-promoting effect of YAP1 in vitro, in vivo, and in human specimens. We revealed that YAP1 displays nuclear translocation and works with TEAD to activate transcription of the crucial inflammatory cytokine IL-1β in gastric cells infected with Hp. As IL-1ß accounts for inflammation-associated tumorigenesis, this process can lead to gastric carcinogenesis. Thus, in addition to activating proliferation genes, YAP1 also plays a major role in inflammation amplification by activating inflammatory cytokine genes. Excitingly, our research demonstrates that transfection of mutant plasmid YAP-5SA/S94A or addition of the drug verteporfin, both of which are thought to disrupt the YAP1-TEAD interaction, can arrest the carcinogenesis process. These findings can provide new approaches to GC treatment.
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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) | 0.002 | 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".