Targeting Ephrin Receptor Tyrosine Kinase A2 with a Selective Aptamer for Glioblastoma Stem Cells
Bibliographic record
Abstract
Despite the benefits associated with radiotherapy and chemotherapy for glioblastoma (GBM) treatment, most patients experience a relapse following initial therapy. Recurrent or progressive GBM usually does not respond anymore to standard therapy, and this is associated with poor patient outcome. GBM stem cells (GSCs) are a subset of cells resistant to radiotherapy and chemotherapy and play a role in tumor recurrence. The targeting of GSCs and the identification of novel markers are crucial issues in the development of innovative strategies for GBM eradication. By differential cell SELEX (systematic evolution of ligands by exponential enrichment), we have recently described two RNA aptamers, that is, the 40L sequence and its truncated form A40s, able to bind the cell surface of human GSCs. Both aptamers were selective for stem-like growing GBM cells and are rapidly internalized into target cells. In this study, we demonstrate that their binding to cells is mediated by direct recognition of the ephrin type-A receptor 2 (EphA2). Functionally, the two aptamers were able to inhibit cell growth, stemness, and migration of GSCs. Furthermore, A40s was able to cross the blood-brain barrier (BBB) and was stable in serum in in vitro experiments. These results suggest that 40L and A40s represent innovative potential therapeutic tools for GBM.
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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) | 0.000 | 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".