Shank-interacting protein–like 1 promotes tumorigenesis via PTEN inhibition in human tumor cells
Bibliographic record
Abstract
Inactivation of phosphatase and tensin homolog (PTEN) is a critical step during tumorigenesis, and PTEN inactivation by genetic and epigenetic means has been well studied. There is also evidence suggesting that PTEN negative regulators (PTEN-NRs) have a role in PTEN inactivation during tumorigenesis, but their identity has remained elusive. Here we have identified shank-interacting protein-like 1 (SIPL1) as a PTEN-NR in human tumor cell lines and human primary cervical cancer cells. Ectopic SIPL1 expression protected human U87 glioma cells from PTEN-mediated growth inhibition and promoted the formation of HeLa cell-derived xenograft tumors in immunocompromised mice. Conversely, siRNA-mediated knockdown of SIPL1 expression inhibited the growth of both HeLa cells and DU145 human prostate carcinoma cells in vitro and in vivo in a xenograft tumor model. These inhibitions were reversed by concomitant knockdown of PTEN, demonstrating that SIPL1 affects tumorigenesis via inhibition of PTEN function. Mechanistically, SIPL1 was found to interact with PTEN through its ubiquitin-like domain (UBL), inhibiting the phosphatidylinositol 3,4,5-trisphosphate (PIP3) phosphatase activity of PTEN. Furthermore, SIPL1 expression correlated with loss of PTEN function in PTEN-positive human primary cervical cancer tissue. Taken together, these observations indicate that SIPL1 is a PTEN-NR and that it facilitates tumorigenesis, at least in part, through its PTEN inhibitory function.
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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) | 0.002 | 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".