Implications of noncoding RNAs for cancer therapy: Are we aiming at the right targets?
Bibliographic record
Abstract
The discovery of oncogenes and tumor suppressor genes led to a better understanding of tumorigenesis, and prompted the development of molecularly targeted therapy. Over the past 30 years, many new drugs, which are primarily aimed at activated oncogenic proteins in signal transduction pathways involved in cell proliferation and survival, have been introduced in the clinic. Despite its rational design, the overall efficacy of targeted therapy has been modest. Recently, the noncoding RNAs (ncRNAs) have emerged as key regulators of important cellular processes in addition to the known regulatory proteins. It now appears that dual epigenetic regulatory systems exist in higher eukaryotic cells: a ncRNA network that governs essential cell functions, like cell fate decision and maintenance of homeostasis, and a protein-based system that presides over core physiological processes, like cell division and genomic maintenance. Modifications of the ncRNA network due to altered ncRNAs can cause the cell to shift towards to neoplastic phenotype; this is cancer initiation. Mutations in the well-known cancer driver genes provide the incipient cancer cell with a selective growth advantage and fuel its consequent clonal expansion. Because of the crucial role of the altered ncRNAs in tumorigenesis, targeting them may be a reasonable therapeutic strategy.
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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 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".