Fibrinogen in the glioblastoma microenvironment contributes to the invasiveness of brain tumor‐initiating cells
Bibliographic record
Abstract
Glioblastomas (GBMs) are highly aggressive, recurrent, and lethal brain tumors that are maintained via brain tumor-initiating cells (BTICs). The aggressiveness of BTICs may be dependent on the extracellular matrix (ECM) molecules that are highly enriched within the GBM microenvironment. Here, we investigated the expression of ECM molecules in GBM patients by mining the transcriptomic databases and also staining human GBM specimens. RNA levels for fibronectin, brevican, versican, heparan sulfate proteoglycan 2 (HSPG2), and several laminins were high in GBMs compared to normal brain, and this was corroborated by immunohistochemistry. While fibrinogen transcript was at normal level in GBM, its protein immunoreactivity was prominent within GBM tissues. These ECM molecules in tumor specimens were in proximity to, and surrounding BTICs. In culture, fibronectin and pan-laminin induced the adhesion of BTICs onto the plastic substratum. However, fibrinogen increased the size of the BTIC spheres by facilitating the adhesive property, motility, and invasiveness of BTICs. These features of elevated invasiveness were corroborated in resected GBM specimens by the close proximity of fibrinogen with matrix metalloproteinase (MMP)-2 and-9, which are proteases implicated in metastasis. Moreover, the effect of fibrinogen-induced invasiveness was attenuated in BTICs where MMP-2 and -9 have been inhibited with siRNAs or pharmacological inhibitors. Our results implicate fibrinogen in GBM as a mediator of the invasive properties of BTICs, and as a target for therapy to reduce BTIC tumorigenecity.
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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".