Tumor metabolism regulating chemosensitivity in ovarian cancer
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Elevated metabolism is a key hallmark of multiple cancers, serving to fulfill high anabolic demands. Ovarian cancer (OVCA) is the fifth leading cause of cancer deaths in women with a high mortality rate (45%). Chemoresistance is a major hurdle for OVCA treatment. Although substantial evidence suggests that metabolic reprogramming contributes to anti-apoptosis and the metastasis of multiple cancers, the link between tumor metabolism and chemoresistance in OVCA remains unknown. While clinical trials targeting metabolic reprogramming alone have been met with limited success, the synergistic effect of inhibiting tumor-specific metabolism with traditional chemotherapy warrants further examination, particularly in OVCA. This review summarizes the role of key glycolytic enzymes and other metabolic synthesis pathways in the progression of cancer and chemoresistance in OVCA. Within this context, mitochondrial dynamics (fission, fusion and cristae structure) are addressed regarding their roles in controlling metabolism and apoptosis, closely associated with chemosensitivity. The roles of multiple key oncogenes (Akt, HIF-1α) and tumor suppressors (p53, PTEN) in metabolic regulation are also described. Next, this review summarizes recent research of metabolism and future direction. Finally, we examine clinical drugs and inhibitors to target glycolytic metabolism, as well as the rationale for such strategies as potential therapeutics to overcome chemoresistant OVCA.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it