p53 Promotes chemoresponsiveness by regulating hexokinase II gene transcription and metabolic reprogramming in epithelial ovarian cancer
Bibliographic record
Abstract
Metabolic reprogramming (including the Warburg effect) is a hallmark of cancer, yet the association between the altered metabolism and chemoresistance remains elusive. Hexokinase II (HKII) is a key metabolic enzyme and is upregulated in multiple cancers. In this study, we examined the impact of targeting metabolism via silencing of HKII on chemoresistance in ovarian cancer (OVCA). In addition, the regulatory molecular mechanism of tumor metabolism was examined using gain- and loss-of-function approaches in epithelial OVCA cell lines of various histological subtypes. We demonstrated that cisplatin (CDDP)-induced p53-mediated HKII downregulation is a determinant of chemosensitivity in OVCA. Silencing of HKII sensitized chemoresistant OVCA cells to apoptosis in a p53-dependent manner. As a negative regulator, p53 suppressed HKII transcription by promoter binding and decreased glycolysis. Pyruvate dehydrogenase kinase-1 (PDK1) is a key regulator of cell proliferation involved in Akt signaling axis. Our Gene Expression Profiling Interactive Analysis (GEPIA) and molecular studies also revealed that PDK1, an upstream activator strongly correlates with HKII expression and regulates its metabolic activity. Finally, we demonstrated that the clinically approved drug metformin sensitizes chemoresistant OVCA cells to CDDP via PDK1-HKII pathway. Collectively, our data implicate that p53--PDK1-HKII axis is a central regulatory component of metabolism conferring chemoresistance in 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) | 0.000 | 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".