Abstract 1898: MEG3 as a competing endogenous RNA binds with miR-27a to promote PHLPP2 protein translation, and consequently impairs bladder cancer invasion
Bibliographic record
Abstract
Abstract Muscle invasion and metastasis of bladder cancer dramatically decrease survival rate, compared to 77% of 5 year survival rate of all bladder cancer, metastasis bladder cancer is only 5%. This truth implies that one of the therapeutic kernels of bladder cancer is to decrease, even block, its invasiveness. In the current study, we find that MEG3 is significantly downregulate in human muscle-invasive bladder cancer tissues compared with non-muscle invasive tissues. We also demonstrate the MEG3 strongly inhibits human bladder cancer cell invasiveness by acting as a ceRNA. We find that MEG3 competes with PHLPP2 mRNA for miR-27a binding, which leads to a decreased miR-27a activity, and subsequently reducing the translation inhibition of miR-27a to PHLPP2. The upregulated PHLPP2 decreases the c-Jun phosphorylation at Ser63/73, and in turn inhibits the c-Myc transcription and its mediated invasiveness of the bladder cancer cells. This is the first demonstration that MEG3 inhibits the invasiveness of human bladder cancer cells via negative regulation of c-Myc as a ceRNA. These findings not only provides a novel insight into understanding the mechanisms behind the MEG3 inhibiting the cancer cell invasion, but also reveals a potential targets for invasive bladder cancer prevention and therapy. Citation Format: Chao Huang, Xin Liao, Jingxia Li, Chuanshu Huang. MEG3 as a competing endogenous RNA binds with miR-27a to promote PHLPP2 protein translation, and consequently impairs bladder cancer invasion [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 1898. doi:10.1158/1538-7445.AM2017-1898
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".