Members of the low‐density lipoprotein receptor‐related proteins provide a differential molecular signature between parental and CD133(+) DAOY medulloblastoma cells
Bibliographic record
Abstract
Members of the low-density lipoprotein receptor-related protein (LRP) family are involved in metabolic stress and resistance phenotypes of cancer cells. New breakthroughs in brain cancer therapy have exploited that molecular signature and proved that efficient delivery of therapeutic agents involve LRP-mediated mechanisms. We performed gene expression profiling of CD133, a cell surface cancer stem cell marker, and of LRP in response to in vitro nutrient deprivation. We found that CD133 was selectively induced in serum-starved DAOY medulloblastoma cells but not in U87MG glioblastoma cells. Such CD133 induction was correlated to increases in LRP-1 and LRP-1b gene and protein expression. When a specific CD133(+) DAOY cell population was sorted from parental DAOY, we found increases in LRP-5 and LRP-8. Uptake of alpha(2)-macroglobulin, a specific LRP-1/1b ligand, was increased in serum-starved parental DAOY cells but not in CD133(+) DAOY cells, and receptor-associated protein (RAP), which binds to all cell surface LRPs, was able to compete for that uptake. Conversely, RAP binding was increased in serum-starved parental DAOY but alpha(2)-macroglobulin was unable to compete for such uptake. Strategies aiming at targeting cancer stem cell metabolic adaptative responses, such as that through LRP differential expression within the brain tissue microenvironmental niche, can now be envisioned.
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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".