Oncogenic activity of poly (ADP-ribose) glycohydrolase
Bibliographic record
Abstract
Poly (ADP-ribosylation), known as PARylation, is a post-translational modification catalyzed by poly (ADP-ribose) polymerases (PARP) and primarily removed by the enzyme poly (ADP-ribose) glycohydrolase (PARG). While the aberrant removal of post-translation modifications including phosphorylation and methylation has known tumorigenic effects, deregulation of PARylation has not been widely studied. Increased hydrolysis of PARylation chains facilitates cancer growth through enhancing estrogen receptor (ER)-driven proliferation, but oncogenic transformation has not been linked to increased PARG expression. In this study, we find that elevated PARG levels are associated with a poor prognosis in breast cancers, especially in HER2-positive and triple-negative subtypes. Using both in vitro and in vivo models, we demonstrate that heightened expression of catalytically active PARG facilitates cell transformation and invasion of normal mammary epithelial cells. Catalytically inactive PARG mutants did not recapitulate these phenotypes. Consistent with clinical data showing elevated PARG predicts poor outcomes in HER2+ patients, we observed that PARG acts in synergy with HER2 to promote neoplastic growth of immortalized mammary cells. In contrast, PARG depletion significantly impairs the growth and metastasis of triple-negative breast tumors. Mechanistically, we find that PARG interacts with SMAD2/3 and significantly decreases their PARylation in non-transformed cells, leading to enhanced expression of SMAD target genes. Further linking SMAD-mediated transcription to the oncogenicity of PARG, we show that PARG-mediated anchorage-independent growth and invasion are dependent, at least in part, on SMAD expression. Overall, our study underscores the oncogenic impact of aberrant protein PARylation and highlights the therapeutic potential of PARG inhibition 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 | 0.000 | 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 | 0.000 |
| 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".