Selective Calcium Sensitivity in Immature Glioma Cancer Stem Cells
Bibliographic record
Abstract
Tumor-initiating cells are a subpopulation in aggressive cancers that exhibit traits shared with stem cells, including the ability to self-renew and differentiate, commonly referred to as stemness. In addition, such cells are resistant to chemo- and radiation therapy posing a therapeutic challenge. To uncover stemness-associated functions in glioma-initiating cells (GICs), transcriptome profiles were compared to neural stem cells (NSCs) and gene ontology analysis identified an enrichment of Ca2+ signaling genes in NSCs and the more stem-like (NSC-proximal) GICs. Functional analysis in a set of different GIC lines regarding sensitivity to disturbed homeostasis using A23187 and Thapsigargin, revealed that NSC-proximal GICs were more sensitive, corroborating the transcriptome data. Furthermore, Ca2+ drug sensitivity was reduced in GICs after differentiation, with most potent effect in the NSC-proximal GIC, supporting a stemness-associated Ca2+ sensitivity. NSCs and the NSC-proximal GIC line expressed a larger number of ion channels permeable to potassium, sodium and Ca2+. Conversely, a higher number of and higher expression levels of Ca2+ binding genes that may buffer Ca2+, were expressed in NSC-distal GICs. In particular, expression of the AMPA glutamate receptor subunit GRIA1, was found to associate with Ca2+ sensitive NSC-proximal GICs, and decreased as GICs differentiated along with reduced Ca2+ drug sensitivity. The correlation between high expression of Ca2+ channels (such as GRIA1) and sensitivity to Ca2+ drugs was confirmed in an additional nine novel GIC lines. Calcium drug sensitivity also correlated with expression of the NSC markers nestin (NES) and FABP7 (BLBP, brain lipid-binding protein) in this extended analysis. In summary, NSC-associated NES+/FABP7+/GRIA1+ GICs were selectively sensitive to disturbances in Ca2+ homeostasis, providing a potential target mechanism for eradication of an immature population of malignant 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".