A protein complex of SCRIB, NOS1AP and VANGL1 regulates cell polarity and migration, and is associated with breast cancer progression
Bibliographic record
Abstract
By analyzing public data sets of gene expression in human breast cancers we observed that increased levels of transcripts encoding the planar cell polarity (PCP) proteins SCRIB and VANGL1 correlate with increased risk of patient relapse. Experimentally, we found that reducing expression of SCRIB by short-hairpin RNAs (shRNAs) reduces the growth of human breast cancer cells in xenograft assays. To investigate SCRIB-associated proteins that might participate in the responses of breast cancer cells to altered levels of SCRIB, we used mass spectrometry and confocal microscopy. These studies reveal that SCRIB is present in at least two unique protein complexes: (1) a complex of SCRIB, ARHGEF, GIT and PAK (p21-activated kinase), and (2) a complex of SCRIB, NOS1AP and VANGL. Focusing on NOS1AP, we observed that NOS1AP colocalizes with both SCRIB and VANGL1 along cellular protrusions in metastatic breast cancer cells, but does not colocalize with either SCRIB or VANGL1 at cell junctions in normal breast cells. We investigated the effects of shRNA-mediated knockdown of NOS1AP and SCRIB in vitro, and found that reducing NOS1AP and SCRIB slows breast cancer cell migration and prevents the establishment of leading-trailing polarity. We also find that reduction of NOS1AP enhances anchorage-independent growth. Collectively these data point to the relevance of NOS1AP and SCRIB protein complexes in breast cancer.
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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 | 0.001 | 0.001 |
| 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".