Phosphatase Dysregulation in Cancer: Signaling Pathways and Therapeutic Opportunities
Bibliographic record
Abstract
ABSTRACT Phosphatases are increasingly recognized as critical regulators of cancer biology, with important roles in both tumor cells and the tumor immune microenvironment (TIME). These enzymes modulate intracellular signaling pathways that control tumor growth, immune evasion, and metastasis. Although phosphatases were once considered undruggable, recent advances have highlighted their therapeutic potential. Despite growing evidence, phosphatases remain underexplored as drug targets, with no approved therapies to date. This review presents an in‐depth overview of phosphatase classification based on catalytic domain similarities and explores their diverse functions as tumor suppressors, oncogenic drivers, or context‐dependent regulators. We describe how phosphatases such as PTPN6, PTPN22, and DUSPs regulate key pathways like RAS/MAPK and PI3K/AKT in both tumor and immune cells. Additionally, we discuss the role of phosphatases in shaping the tumor microenvironment through exosome secretion. This review highlights current therapeutic strategies, including small molecules and antibodies, and their synergistic effects with kinase inhibitors and immune checkpoint blockade. By summarizing recent advances, this paper underscores the need for deeper mechanistic insights into phosphatase function in cancer and immunity. Understanding these mechanisms will be key to unlocking their potential as novel therapeutic targets in oncology.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".