Ubiquitin specific peptidase 33 promotes cell proliferation and reduces apoptosis through regulation of the SP1/PI3K/AKT pathway in retinoblastoma
Bibliographic record
Abstract
Ubiquitin-specific protease 33 (USP33), a deubiquitinating enzyme (DUB), has been identified to serve as a tumor suppressor or an oncogene in different cancers. However, its role in retinoblastoma (RB) remains unknown. Here, we aimed to uncover USP33 expression profile and function in RB, and disclose the underlying mechanism. USP33 levels in RB tissues and cells were determined using RT-qPCR and western blotting assays. USP33 effects on cell growth, cycle, apoptosis and tumorigenesis were studied using MTT, Edu, cycle and western blotting and in vivo assays. The results showed that USP33 expression levels were elevated in RB tissues and cells as compared with normal retinal tissues and cells. Downregulation of USP33 in RB Y79 and WERI-RB1 cells leaded to significant increases in cell apoptosis, G1 phase arrest and tumorigenesis, and reductions in cell growth and G2 and S phase arrest, as well as inhibited the activation of the PI3K/AKT signaling. SP1 overexpression abolished the roles of USP33 downregulation in modulating the activation of PI3K/AKT signaling, cell growth, apoptosis, and cell cycle. This study uncovered that USP33 promoted the progression of RB through regulation of the SP1/PI3K/AKT pathway.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.011 |
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".