Hexokinase 2 Regulates Ovarian Cancer Cell Migration, Invasion and Stemness via FAK/ERK1/2/MMP9/NANOG/SOX9 Signaling Cascades
Bibliographic record
Abstract
Metabolic reprogramming is a common phenomenon in cancers. Thus, glycolytic enzymes could be exploited to selectively target cancer cells in cancer therapy. Hexokinase 2 (HK2) converts glucose to glucose-6-phosphate, the first committed step in glucose metabolism. Here, we demonstrated that HK2 was overexpressed in ovarian cancer and displayed significantly higher expression in ascites and metastatic foci. HK2 expression was significantly associated with advanced stage and high-grade cancers, and was an independent prognostic factor. Functionally, knockdown of HK2 in ovarian cancer cell lines and ascites-derived tumor cells hindered lactate production, cell migration and invasion, and cell stemness properties, along with reduced FAK/ERK1/2 activation and metastasis- and stemness-related genes. 2-DG, a glycolysis inhibitor, retarded cell migration and invasion and reduced stemness properties. Inversely, overexpression of HK2 promoted cell migration and invasion through the FAK/ERK1/2/MMP9 pathway, and enhanced stemness properties via the FAK/ERK1/2/NANOG/SOX9 cascade. HK2 abrogation impeded in vivo tumor growth and dissemination. Notably, ovarian cancer-associated fibroblast-derived IL-6 contributed to its up-regulation. In conclusion, HK2, which is regulated by the tumor microenvironment, controls lactate production and contributes to ovarian cancer metastasis and stemness regulation via FAK/ERK1/2 signaling pathway-mediated MMP9/NANOG/SOX9 expression. HK2 could be a potential prognostic marker and therapeutic target 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".