Long non-coding RNAs (lncRNAs) in cancer proliferation: Molecular interactions and possible therapeutic targets
Bibliographic record
Abstract
Long non-coding RNAs (lncRNAs) are a class of non-protein coding RNAs that have more than 200 nucleotides. lncRNAs have been found to be aberrantly expressed in human diseases including cancer. In cancer, lncRNAs exert critical roles and affect all the cancer hallmarks including apoptosis, proliferative capacity, malignancy, invasiveness, immune evasion, angiogenesis, chemo-resistance, and radio-resistance via regulating various factors and signaling pathways. lncRNAs target other non-coding RNAs, commonly miRNAs. lncRNAs are predominantly either tumor suppressors or oncogenic. However, some lncRNAs have both roles based on the cancer type. For instance, MIR31HG exerts tumor suppressor functions in hepatocellular carcinoma while possessing oncogenic roles in non-small cell lung cancer. Cancer cells inhibit apoptosis, increase metabolic pathways such as glycolysis, and promote cell cycle progression to augment their proliferation. Evidence has revealed the critical role of lncRNAs in the regulation of these mechanisms, either by their suppression or by promotion. Thus, the aim of this chapter is to discuss the importance of lncRNAs in the regulation of apoptosis, autophagy, cell cycle progression, and glycolysis in various types of cancer.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".