Tumor Suppressor p53 Down-Regulates Programmed Cell Death Protein 4 (PDCD4) Expression
Bibliographic record
Abstract
The programmed cell death protein 4 (PDCD4), a well-known tumor suppressor, inhibits translation initiation and cap-dependent translation by inhibiting the helicase activity of EIF4A. The EIF4A tends to target mRNAs with a structured 5′-UTR. In addition, PDCD4 can also prevent tumorigenesis by inhibiting tumor promoter-induced neoplastic transformation, and studies indicate that PDCD4 binding to certain mRNAs inhibits those mRNAs’ translation. A previous study demonstrated that PDCD4 inhibits the translation of p53 mRNA and that treatment with DNA-damaging agents down-regulates PDCD4 expression but activates p53 expression. The study further demonstrated that treatment with DNA-damaging agents resulted in the downregulation of PDCD4 expression and an increase in p53 expression, suggesting a potential mechanism by which p53 regulates the expression of PDCD4. However, whether p53 directly regulates PDCD4 remains unknown. Herein, we demonstrate for the first time that p53 regulates PDCD4 expression. Firstly, we found that overexpression of p53 in p53-null cells (H1299 and Saos2 cells) decreased the PDCD4 protein level. Secondly, p53 decreased PDCD4 promoter activity in gene reporter assays. Moreover, we demonstrated that mutations in p53 (R273H: contact hotspot mutation, and R175H: conformational hotspot mutation) abolished p53-mediated PDCD4 repression. Furthermore, mutations in the DNA-binding domain, but not in the C-terminal regulatory domain, of p53 disrupted p53-mediated PDCD4 repression. Finally, the C-terminal regulatory domain truncation study showed that the region between aa374 and aa370 is critical for p53-mediated PDCD4 repression. Taken together, our results suggest that p53 functions as a novel regulator of PDCD4, and the relationship between p53 and PDCD4 may be involved in tumor development and progression.
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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) | 0.001 | 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".