FAM107A is a blocker of differentiation in neural and glioblastoma stem cells
Bibliographic record
Abstract
Background.Glioblastoma Multiforme (GBM) is an aggressive brain cancer.Despite optimal treatment, which consists of maximal safe resection, temozolomide chemotherapy, and radiotherapy, this disease remains uniformly lethal.Within each tumour, a subset of tumorigenic GBM stem cells (GSCs) has been suggested to drive tumor propagation and contribute to recurrence.GSCs bear resemblance to normal neural stem cells (NSCs) in their phenotypic expression of markers and characteristic features of self--renewal and differentiation capacities.Additionally, GBM development may depend on cellular hierarchies of GSCs, reminiscent of normal development but steered by underlying genetic mutations.FAM107A is a candidate marker of radial glia and is involved in brain development.It has been implicated in the maintenance of stem--like states in GSCs through regulation of neurodevelopmental transcriptional factors essential for GSC development. Method.Here, we evaluate the expression of FAM107A in fetal NSCs and patient--derived GSCs in vitro and in neurogenic regions of the mouse and human brain.The role of FAM107A is identified using anti--sense oligonucleotides (ASOs) effectively knocking down its expression and evaluating changes in lineage--specific expression profiles.Results.FAM107A is highly expressed in GSCs and fetal NSCs and is co--expressed with classical markers of early NSCs.When FAM107A expression is reduced using ASOs in GSCs, we observe a decrease in the early NSC and neuronal signature and an increase in the glial signature, consistent across 3 GSC lines.GSCs pushed to glial differentiation showed significant sensitivity to temozolomide compared to untreated conditions.In fetal NSCs, the
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".