Cross-talk between mutant p53 and p62/SQSTM1 augments cancer cell migration by promoting the degradation of cell adhesion proteins
Bibliographic record
Abstract
Missense mutations in the p53 tumor suppressor abound in human cancer. Common (“hotspot”) mutations endow mutant p53 (mutp53) proteins with oncogenic gain of function (GOF), including enhanced cell migration and invasiveness, favoring cancer progression. GOF is usually attributed to transcriptional effects of mutp53. To elucidate transcription-independent effects of mutp53, we characterized the protein interactome of the p53R273H mutant in cells derived from pancreatic ductal adenocarcinoma (PDAC), where p53R273H is the most frequent p53 mutant. We now report that p53R273H, but not the p53R175H hotspot mutant, interacts with SQSTM1/p62 and promotes cancer cell migration and invasion in a p62-dependent manner. Mechanistically, the p53R273H-p62 axis drives the proteasomal degradation of several cell junction–associated proteins, including the gap junction protein Connexin 43, facilitating scattered cell migration. Concordantly, down-regulation of Connexin 43 augments PDAC cell migration, while its forced overexpression blunts the promigratory effect of the p53R273H-p62 axis. These findings define a mechanism of mutp53 GOF.
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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".