P.038 Investigating the role of long non-coding RNAs in glioblastoma multiforme
Bibliographic record
Abstract
Background: Malignant gliomas are the most common and deadly brain tumors. Mean survival rate for a patient diagnosed with a glioblastoma multiforme (GBM) remains slightly over one year. Standard of care consists of treatment with temozolomide (TMZ) and radiotherapy. Recent work has highlighted functions of long non-coding RNAs (lncRNAs) in GBM progression and TMZ response even though the information regarding these newly discovered molecules is sparse. The overarching objective of this project was thus to assess the expression of select lncRNAs in GBM tumor samples and in models of TMZ resistance. Methods: A qRT-PCR-based approach was undertaken to measure six lncRNAs in 19 primary GBM samples, four GBM cell lines and in-house developed TMZ-resistant GBM cells. Results: Elevated levels of Hotair and H19 were observed in primary GBM tumors while decreased expression of MEG3 was recorded in the same samples. Interestingly, levels of PANDA increased 3.4-fold in GBM cells resistant to TMZ when compared with their sensitive counterparts. Conclusions: Overall, this work provides evidence of lncRNA deregulation in GBM tumors and reveals a previously unexplored lncRNA potentially involved in TMZ resistance. Modulation of lncRNA targets via RNAi-mediated approaches is envisioned to clarify their function and to strengthen their position as therapeutic options in GBMs.
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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 | 0.001 |
| 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) | 0.009 | 0.002 |
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".