PDGFRβ modulates aerobic glycolysis in osteosarcoma HOS cells via the PI3K/AKT/mTOR/c-Myc pathway
Bibliographic record
Abstract
Osteosarcoma is a malignant tumor abundant in vascular tissue, and its rich blood supply may have a significant impact on its metabolic characteristics. PDGFRβ is a membrane receptor highly expressed in osteosarcoma cells and vascular wall cells, and its effect on osteosarcoma metabolism needs to be further studied. In this study, we discussed the effect and mechanism of action of PDGFRβ on glucose metabolism in human osteosarcoma (HOS) cells. GSEA, Pearson’s correlation test, and PPI correlation analysis indicated positive regulation of PDGFRβ on aerobic glycolysis in osteosarcoma. The results of qPCR and western blot further confirmed the prediction of bioinformatics. Glucose metabolism experiments proved that PDGF/PDGFRβ could effectively promote aerobic glycolysis in osteosarcoma cells. In addition, the mitochondrial membrane potential (ΔΨm) experiment proved that the metabolic change triggered by PDGFRβ was not caused by mitochondrial damage. The PI3K pathway inhibitor LY294002, MEK pathway inhibitor U0126, or Warburg effect inhibitor DCA was used to perform western blot and glucose metabolism experiments, and the results showed that PDGFBB/PDGFRβ mainly activated the PI3K/AKT/mTOR/c-Myc pathway to promote aerobic glycolysis in osteosarcoma HOS cells. The newly elucidated role of PDGFRβ provides a novel metabolic therapeutic target for osteosarcoma.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".