Identification of PTPN23 as a novel regulator of cell invasion in mammary epithelial cells from a loss-of-function screen of the ‘PTP-ome'
Bibliographic record
Abstract
We used an RNAi-mediated loss-of-function screen to study systematically the role of the protein tyrosine phosphatase (PTP) superfamily of enzymes in mammary epithelial cell motility in the absence or presence of the oncoprotein tyrosine kinase ERBB2. We report that although shRNAs directed against most of the PTP family were without effect, suppression of three PTPs-PRPN23, PTPRG, and PTPRR-enhanced cell motility. Furthermore, we found that suppression of PTPN23, but not PTPRG or PTPRR, induced cell invasion. Suppression of PTPN23 increased E-cadherin internalization, impaired early endosome trafficking of E-cadherin, induced the expression of mesenchymal proteins, and caused cell scattering. The activity of SRC and β-catenin was elevated when PTPN23 was suppressed. Moreover, we identified SRC, E-cadherin, and β-catenin as direct substrates of PTPN23. Inhibition of SRC with the small molecular inhibitor SU6656 blocked the effects of PTPN23 depletion. These findings suggest that loss of PTPN23 may increase the activity of SRC and the phosphorylation status of the E-cadherin/β-catenin signaling complex to promote tumor growth and invasive behavior in breast cancer. In addition, our studies highlight functional specificity among PTPs and reveal new roles for PTPs in mammary epithelial cell biology.
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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) | 0.001 | 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".