FABP7 Facilitates Uptake of Docosahexaenoic Acid in Glioblastoma Neural Stem-like Cells
Bibliographic record
Abstract
Glioblastoma (GBM) is an aggressive tumor with a dismal prognosis. Neural stem-like cells contribute to GBM's poor prognosis by driving drug resistance and maintaining cellular heterogeneity. GBM neural stem-like cells express high levels of brain fatty acid-binding protein (FABP7), which binds to polyunsaturated fatty acids (PUFAs) ω-6 arachidonic acid (AA) and ω-3 docosahexaenoic acid (DHA). Similar to brain, GBM tissue is enriched in AA and DHA. However, DHA levels are considerably lower in GBM tissue compared to adult brain. Therefore, it is possible that increasing DHA content in GBM, particularly in neural stem-like cells, might have therapeutic value. Here, we examine the fatty acid composition of patient-derived GBM neural stem-like cells grown as neurosphere cultures. We also investigate the effect of AA and DHA treatment on the fatty acid profiles of GBM neural stem-like cells with or without FABP7 knockdown. We show that DHA treatment increases DHA levels and the DHA:AA ratio in GBM neural stem-like cells, with FABP7 facilitating the DHA uptake. We also found that an increased uptake of DHA inhibits the migration of GBM neural stem-like cells. Our results suggest that increasing DHA content in the GBM microenvironment may reduce the migration/infiltration of FABP7-expressing neural stem-like cancer cells.
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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".