PGRMC1 promotes triple-negative breast cancer cell growth via suppressing ferroptosis
Bibliographic record
Abstract
OBJECTIVE: Triple-negative breast cancer (TNBC) is the most malignant form of breast cancer with increasing incidence and mortality worldwide. The progesterone receptor membrane component-1 (PGRMC1) is a well-identified hormone receptor with unknown functions in TNBC. The current study aims to explore the involvement of PGRMC1 in regulation of glutathione metabolism and ferroptosis during development of TNBC, providing new therapy options for TNBC patients. METHODS: Bioinformatic analysis, cell proliferation assay, western blot assay and other biochemistry methods were performed in TNBC cells. RESULTS: Our results revealed that the expression of PGRMC1 is higher in TNBC than the other subtypes of breast cancer. Interestingly, as an iron binding protein, increased PGRMC1 expression in TNBC cells leads to resistance to ferroptosis inducer. On the contrary, silenced PGRMC1 expression enhanced sensitivity of MDA-MB231 cells to Erastin. Mechanistically, overexpression of PGRMC1 decreased the intracellular free iron concentration, which was reduced by AG205 treatment. CONCLUSIONS: PGRMC1 increases the possibility of TNBC development through binding to intracellular iron and suppressing ferroptosis, providing the molecular basis of combined treatment for TNBC.
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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) | 0.002 | 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".