Sprouting Angiogenesis in Human Pituitary Adenomas
Bibliographic record
Abstract
Introduction: Angiogenesis in pituitary tumors is not fully understood, and a better understanding could help inform new pharmacologic therapies, particularly for aggressive pituitary tumors. Materials and Methods: in RbΔ19 mice. Results: 71 angiogenic genes, 40 of which are known to be involved in sprouting angiogenesis, were differentially expressed in pituitary tumors. Expression of endothelial markers CD31, CD34, and ENG was significantly higher in pituitary tumors, by 5.6, 22.3, and 8.2-fold, respectively, compared to in normal pituitary tissue. There was no significant difference in levels of the lymphatic endothelial marker LYVE1 in pituitary tumors compared with normal pituitary gland tissue. Pituitary tumors also expressed significantly higher levels of angiogenesis growth factors, including VEGFA (4.2-fold), VEGFB (2.2), VEGFC (19.3), PGF (13.4), ANGPT2 (9.2), PDGFA (2.7), PDGFB (10.5) and TGFB1 (3.8) compared to normal pituitary tissue. Expression of VEGFC and PGF was highly correlated with the expression of endothelial markers in tumor samples, including CD31, CD34, and ENG (endoglin, a co-receptor for TGFβ). Furthermore, VEGFR inhibitors inhibited angiogenesis induced by human pituitary tumors and prolonged survival of RbΔ19 mice. Conclusion: Human pituitary tumors are characterized by more active angiogenesis than normal pituitary gland tissue in a manner consistent with sprouting angiogenesis. Angiogenesis in pituitary tumors is regulated mainly by PGF and VEGFC, not VEGFA and VEGFB. Angiogenesis inhibitors, such as the VEGFR2 inhibitor cabozantinib, may merit further investigation as therapies for aggressive human pituitary tumors.
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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".