Mechanistic Roles of Noncoding RNAs in Lung Cancer Biology and Their Clinical Implications
Bibliographic record
Abstract
Lung cancer biology has traditionally focused on genomic and epigenomic deregulation of protein-coding genes to identify oncogenes and tumor suppressors diagnostic and therapeutic targets. Another important layer of cancer biology has emerged in the form of noncoding RNAs (ncRNAs), which are major regulators of key cellular processes such as proliferation, RNA splicing, gene regulation, and apoptosis. In the past decade, microRNAs (miRNAs) have moved to the forefront of ncRNA cancer research, while the role of long noncoding RNAs (lncRNAs) is emerging. Here we review the mechanisms by which miRNAs and lncRNAs are deregulated in lung cancer, the technologies that can be applied to detect such alterations, and the clinical potential of these RNA species. An improved comprehension of lung cancer biology will come through the understanding of the interplay between deregulation of non-coding RNAs, the protein-coding genes they regulate, and how these interactions influence cellular networks and signalling pathways.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".