61 Functional characterization of ribosomal RNA methyltransferase NSUN5 in glioblastoma
Bibliographic record
Abstract
Glioblastoma is the most common and malignant brain tumor with a median overall survival of 20.5 months. There is an urgent need to develop novel therapeutic strategies. Using a glioblastoma TCGA dataset, we have determined that high NSUN5 mRNA expression is strongly associated with poor survival in glioblastoma patients. NSUN5 is a ribosomal RNA (rRNA) cytosine methyltransferase. Human NSUN5 is located in chromosome 7 and is completely deleted in the Williams-Beurren syndrome, a complex neurodevelopmental disorder. However, RNA targets of NSUN5 in mammals and its role in cancer are unknown. The objective of this project is to determine whether elevated NSUN5 changes rRNA methylation pattern and thereby leads to pro-tumorigenic translational reprogramming and pro-tumorigenic phenotypes in glioblastoma. Western blotting showed that NSUN5 is expressed in 7 out of 9 established glioblastoma cell lines and in 8 out of 12 primary patient-derived glioblastoma cell lines. Bisulfite sequencing confirmed that NSUN5 methylates C3782 of human 28S rRNA in glioblastoma cells. Functionally, overexpression of NSUN5 increases, whereas NSUN5 knockout decreases global protein synthesis and sphere formation in glioblastoma cells. More importantly, mice bearing intracranial NSUN5-expressing U87 tumors survived for a shorter time than mice bearing tumors derived from U87 control cells. Our results suggest that NSUN5 methylates 28S rRNA and may enhance cancer stem cell phenotypes and tumor formation and/or progression in glioblastoma. Experiments are ongoing to determine whether NSUN5 promotes tumor formation and/or progression through translational reprogramming in glioblastoma. This study may help identify novel therapeutic targets for glioblastoma.
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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 | 0.001 | 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".