Abstract 533: XIAP BIR domain suppresses miR-200a expression and subsequently promotes EGFR protein translation and anchorage-independent growth of bladder cancer cell
Bibliographic record
Abstract
Abstract The X-linked inhibitor of apoptosis protein (XIAP) is a well-known potent apoptosis suppressor, and also participates in cancer cell biological behaviors, therefore attracting great attentions as a potential antineoplastic therapeutic target for past years. We found here that lacking of XIAP expression resulted in a remarkable suppression of EGFR expression, consequently leading to the deficient of anchorage-independent cell growth. Further study demonstrated that BIR domain of XIAP was crucial for regulating the EGFR translation by suppressing the transcription and expression of miR-200a. Mechanistic studies indicated that BIR domain activated the PP2A activity by decreasing the phosphorylation of PP2A at Tyr307 in its catalytic subunit, PP2A-C. Such activated PP2A prevented the deviant phosphorylation and activation of MAPK kinases/MAPKs, their downstream effector c-Jun, and in turn inhibiting transcription of c-Jun-regulated the miR-200a. Collectively, our study uncovered a novel function of BIR domain of XIAP in regulating the EGFR translation, providing significant insight into the understanding of the XIAP overexpression in the cancer development and progression, further offering a new theoretical support for using XIAP BIR domain and EGFR as targets for cancer therapy. Citation Format: Xin Liao, Chao Huang, Jingxia Li, Chuanshu Huang. XIAP BIR domain suppresses miR-200a expression and subsequently promotes EGFR protein translation and anchorage-independent growth of bladder cancer cell [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 533. doi:10.1158/1538-7445.AM2017-533
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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) | 0.003 | 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".