PTP4A2 Promotes Glioblastoma Progression and Macrophage Polarization under Microenvironmental Pressure
Bibliographic record
Abstract
Phosphatase of regenerating liver 2 (also known as PTP4A2) has been linked to cancer progression. Still, its exact role in glioblastoma (GBM), the most aggressive type of primary brain tumor, remains elusive. In this study, we report that pharmacologic treatment using JMS-053, a pan-phosphatase of regenerating liver inhibitor, inhibits GBM cell viability and spheroid growth. We also show that PTP4A2 is associated with a poor prognosis in gliomas, and its expression correlates with GBM aggressiveness. Using a GBM orthotopic xenograft model, we show that PTP4A2 overexpression promotes tumor growth and reduces mouse survival. Furthermore, PTP4A2 deletion leads to increased apoptosis and proinflammatory signals. Using a syngeneic GBM model, we show that depletion of PTP4A2 reduces tumor growth and induces a shift in the tumor microenvironment (TME) toward an immunosuppressive state. In vitro assays show that cell proliferation is not affected in PTP4A2-deficient or -overexpressing cells, highlighting the importance of the microenvironment in PTP4A2 functions. Collectively, our results indicate that PTP4A2 promotes GBM growth in response to microenvironmental pressure and support the rationale for targeting PTP4A2 as a therapeutic strategy against GBM. SIGNIFICANCE: High levels of PTP4A2 are associated with poor outcomes in patients with glioma and in mouse models. PTP4A2 depletion increases apoptosis and proinflammatory signals in GBM xenograft models, significantly impacts tumor growth, and rewires the TME in an immunocompetent host. PTP4A2 effects in GBM are dependent on the presence of the TME.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".