Expression of PGK1 in Breast Cancers Alters Their Sensitivity to Ferroptosis Induction via Metabolic Reprogramming
Bibliographic record
Abstract
Abstract Therapeutic resistance and recurrence are among the major contributors to poor outcomes for patients with breast cancer. Induction of ferroptosis, a form of cellular death characterized by toxic lipid peroxide overload, has emerged as a promising therapeutic strategy against breast cancers including triple-negative breast cancer(TNBC). Nevertheless, certain types of cancer are impervious to induction of ferroptosis and the underlying mechanisms remain incompletely clear. In this study, we show that phosphoglycerate kinase 1 (PGK1), an important enzyme in glycolysis, is highly expressed in breast tumors, and the elevated levels of PKG expression correlate with advanced tumor stages, poor prognosis and ferroptosis insensitivity, particularly in TNBCs. Using genetic or pharmacological inhibition, we demonstrate that knockdown or inhibition of PGK1 enhances ferroptosis sensitivity in both TNBC and luminal breast cancer cell lines. We further demonstrate that depletion of PGK1 destabilizes glutathione peroxidase 4 (GPX4), an anti-ferroptotic defense peroxidase, thereby disturbing cellular redox homeostasis and promoting lipid peroxidation. Moreover, targeting PGK1 disrupts glycolytic metabolism and sensitizes breast cancer cells to ferroptosis induction in tumor cells subjected to glucose deprivation or treated with glycolytic inhibitors. In orthotopic TNBC models, loss of tumoral PGK1 augments the action of the ferroptosis inducer, imidazole ketone erastin (IKE), in inhibiting tumor growth and metastasis, and enhances CD8 + T cell-mediated anti-tumor immunity. These results indicate that PGK1 has a critical role in modulating breast cancer invulnerability to induction of ferroptosis, implying that this kinase may be exploited as a therapeutic target to sensitize breast cancers, especially, TNBC, to ferroptosis inducers.
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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) | 0.003 | 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".