Tumor metabolism regulating chemosensitivity in ovarian cancer
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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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".