Regulation of cancer cell ferroptosis by PTRF/Cavin-1
Bibliographic record
Abstract
Ovarian cancer, marked by high rate of recurrence, novel therapeutic strategies are needed to improve patient outcome. One of the potential strategies is inducing ferroptosis in ovarian cancer cells. Ferroptosis is an iron-dependent, lipid peroxidation-driven mode of cell death primarily occurring on the cell membrane. PTRF, an integral component of the caveolae structures located on the cell membrane, is involved in a multitude of physiological processes, including but not limited to, endocytosis, signal transduction, and lipid metabolism. This study elucidates the relationship between PTRF and ferroptosis in ovarian cancer, offering a fresh perspective for the development of new therapeutic strategies. We knocked down PTRF employing siRNA in the ovarian cancer cell lines HEY and SKOV3, following which we stimulated ferroptosis with Erastin (Era). Our research indicates that the lack of PTRF sensitizes cancer cells to ferroptosis, likely by altering membrane stability and tension, thereby affecting signal pathways related to ferroptosis, such as lipid and atherosclerosis, fluid shear stress, and atherosclerosis. Our findings provide new insights for developing new treatments for ovarian 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".