The Role of LncRNAs in Radio- and Chemoresistance of Glioblastoma: Prognostic or Therapeutic?
Bibliographic record
Abstract
Malignant brain tumors remain highly challenging to treat due to intrinsic and acquired therapy resistance and limited therapeutic options, consequently contributing to poor prognosis. Advancing the understanding of resistance mechanisms alongside novel treatment strategies is essential to improve clinical outcomes. Altered gene expression is common in tumors, and a specific class of non-coding RNAs, particularly long non-coding RNAs (lncRNAs), is frequently deregulated. LncRNAs play critical roles in processes such as cell proliferation, cell cycle arrest, and metastasis in brain cancer, functioning either as tumor promoters or suppressors. They exert their effects through transcriptional and post-transcriptional regulatory mechanisms. Understanding the functional roles of lncRNAs in malignant brain tumors has become a priority, as they are differentially expressed in tumors compared to healthy tissue. These molecules are studied for their potential as therapeutic targets and biomarkers in oncology. This review provides an overview of current research on brain cancer and lncRNAs, emphasizing the need for further investigation into their specific roles in therapy resistance and their involvement in various pathways. A better understanding of lncRNAs and their role in brain cancer could offer valuable insights into their prognostic and therapeutic potential, with the promise of improving early diagnosis and treatment outcomes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".