Mechanisms underlying p53 regulation of PIK3CA transcription in ovarian surface epithelium and in ovarian cancer
Bibliographic record
Abstract
Inactivation of the transcription factor and tumor suppressor p53, and overexpression or mutational activation of PIK3CA, which encodes the p110alpha catalytic subunit of phosphatidylinositol-3-kinase (PI3K), are two of the most common deleterious genomic changes in cancer, including in ovarian carcinomas. We investigated molecular mechanisms underlying interactions between these two mediators and their possible roles in ovarian tumorigenesis. We identified two alternate PIK3CA promoters and showed direct binding of and transcriptional inhibition by p53 to one of these promoters. Conditional suppression of functional p53 increased p110alpha transcripts, protein levels and PI3K activity in immortalized, non-tumorigenic ovarian surface epithelial (OSE) cells, the precursors of ovarian carcinoma. Conversely, overexpression of p53 by adenoviral infection and activation of p53 by gamma-irradiation both diminished p110alpha protein levels in normal OSE and ovarian cancer cells. The demonstration that p53 binds directly to the PIK3CA promoter and inhibits its activity identifies a novel mechanism whereby these two mediators regulate cellular functions, and whereby inactivation of p53 and subsequent upregulation of PIK3CA might contribute to the pathophysiology of ovarian cancer.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".