Modeling of Hypo/Hyperglycemia and Their Impact on Breast Cancer Progression Related Molecules
Bibliographic record
Abstract
Breast cancer (BC) arises commonly in women with metabolic dysfunction. The underlying mechanism by which glycemic load can exert its action on tumor metastasis is under investigated. In this study we showed that glycemic microenvironment alters the expression of three classes of proteins, VEGF and its receptors, cell to cell, and cell to extracellular matrix (ECM) adhesion proteins in MDA-MB-231 parental cells and its two metastatic variants to the bone and brain (MDA-MB-231BO and MDA-MB-231BR, respectively). Using western blotting, we showed that VEGFR2 levels were higher in these variant cells and persisted in the cells under extreme hypoglycemia. Hypoglycemia did not alter VEGFR2 expression per se but rather suppressed its posttranslational glycosylation. This was reversed rapidly upon the restoration of glucose, and cyclohexamide (CHX) treatment demonstrated that this deglycosylated VEGFR2 was not a product of de-novo protein synthesis. VEGFR2 co-receptor Neuropilin-1 was up-regulated four-fold in all MDA-MB-231 cells (parental and two variants) compared to VEGFR2 expression, and was also susceptible to glycemic changes but resistant to CHX treatment for up to 72 hrs. Hypoglycemia also resulted in a significant decrease in specific catenin, cadherin, and integrin proteins, as well as cellular proliferation and colony forming ability. However, MDA-MB-231BR cells showed a unique sensitivity to hypo/hyperglycemia in terms of morphological changes, colony formation ability, integrin β3 expression and secreted VEGF levels. In conclusion, this study can be translated clinically to provide insight into breast cancer cell responses to glycemic levels relevant for our understanding of the interaction between diabetes and 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".