Bibliographic record
Abstract
AbstractBrain tumour stem cells (BTSCs) are a rare population of glioblastoma cells that have properties to evade ionizing radiation (IR) and chemotherapy, survive and replenish themselves, and spur the growth of new tumour cells. Galectin1, encoded by LGALS1 gene, is a carbohydrate-binding protein with its expression highly upregulated in BTSCs. Here, we report that galectin1 plays important cell intrinsic roles in BTSCs via regulation of cell cycle, proliferation, and self-renewal. We also show that deletion of galectin1 sensitizes the response of chemoresistant BTSCs to chemotherapy. Beginning with mRNA-Seq analysis on patient-derived BTSC73 and LGALS1 KO BTSC73, we identified a large panel of genes involved in the regulation of cell cycle and cell division. I thus, employed RT-qPCR analysis in multiple patient-derived BTSCs to validate the gene expression profile of galectin1. Next, to study the impact of galectin1 on cell proliferation, I performed immunostaining on LGALS1 KO BTSCs and BTSCs using the proliferation markers, KI67 and phospho-Histone H3 (PH3). Interestingly, I found a significant decrease in percentage of KI67 and PH3 positive BTSCs upon deletion of LGALS1. Importantly, in limiting dilution assays, I found that galectin1 regulates BTSC self-renewal and confers resistance of BTSCs to chemotherapy with Temozolomide. Finally, in follow up studies, I identified a large panel of galectin1 downstream candidate genes that are involved in the regulation of mitotic spindle assembly, chromosome segregation, multinucleation, and cytokinesis. This proposes that galectin1 may promote cell cycle and self-renewal via regulating mitosis. Taken together, our data suggests that targeting of galectin1 could be a new avenue in overcoming therapeutic resistance in brain tumour stem 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".