Molecular markers of extracellular matrix remodeling in glioblastoma vessels: Microarray study of laser‐captured glioblastoma vessels
Bibliographic record
Abstract
Glioblastoma multiforme (GBM) are the most malignant and vascularized brain tumors. The aberrant vascular phenotype of GBM could be exploited for diagnosis or therapeutic targeting. This study identified new molecular markers of GBM vessels, using a combination of laser capture microdissection (LCM) microscopy, RNA amplification, and microarray analyses to compare vessels from nonmalignant human brain and GBM tumors. Forty-two genes were differentially expressed in GBM vessels compared to nonmalignant brain vessels. Validation of differentially expressed genes was performed by literature mining, Q-PCR, and immunohistochemistry. Among the differentially expressed genes, only 64% were previously associated with vessels, angiogenesis, gliomas, and/or cancer. The upregulation of genes encoding secreted extracellular proteins IGFBP7 and SPARC was confirmed by Q-PCR in LCM-captured vessels. Whereas SPARC and IGFBP7 protein were absent in nonmalignant brain vessels, a distinct immunoreactivity patterns were observed in GBM sections whereby SPARC was strongly expressed in perivascular cells adjacent to GBM vessels while GBM endothelial cells were immunostained for IGFBP7. IGFBP7 immunoreactivity was also detected on the abluminal side of GBM vessels deposited between strands of vascular basal lamina. The study discerns unique molecular characteristics of GBM vessels compared with nonmalignant brain vessels that could potentially be used for diagnostic or therapeutic purposes.
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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".