Long Non-Coding RNAs As Epigenetic Regulators in Cancer
Bibliographic record
Abstract
Long noncoding RNAs (lncRNAs) constitute large portions of the mammalian transcriptome which appeared as a fundamental player, regulating various cellular mechanisms. LncRNAs do not encode proteins, have mRNA-like transcripts and frequently processed similar to the mRNAs. Many investigations have determined that lncRNAs interact with DNA, RNA molecules or proteins and play a significant regulatory function in several biological processes, such as genomic imprinting, epigenetic regulation, cell cycle regulation, apoptosis, and differentiation. LncRNAs can modulate gene expression on three levels: chromatin remodeling, transcription, and post-transcriptional processing. The majority of the identified lncRNAs seem to be transcribed by the RNA polymerase II. Recent evidence has illustrated that dysregulation of lncRNAs can lead to many human diseases, in particular, cancer. The aberrant expression of lncRNAs in malignancies contributes to the dysregulation of proliferation and differentiation process. Consequently, lncRNAs can be useful to the diagnosis, treatment, and prognosis, and have been characterized as potential cancer markers as well. In this review, we highlighted the role and molecular mechanisms of lncRNAs and their correlation with some of the cancers.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".